Model Calibration and Validation Standards - FSUTMSOnline

FSUTMS-Cube Framework Phase II 

Model Calibration and Validation Standards 

final 

report 

prepared for 

Florida Department of Transportation Systems Planning Office 

October 2, 2008

final report 



prepared for 


prepared by 

Cambridge Systematics, Inc. 

2457 Care Drive, Suite 101 

Tallahassee, Florida 32308 


605 Suwannee Street, MS-19 

Tallahassee, Florida 32399-0450 

October 2, 2008



Table of Contents 

Executive Summary .............................................................................................................. ES-1 

ES.1 Literature Review.................................................................................................. ES-1 

ES.2 Model Validation Guidelines and Standards.................................................... ES-2 

ES.3 Best Practices for Model Validation.................................................................... ES-2 

ES.4 Guidelines for Model Application...................................................................... ES-3 

1.0 Literature Review ......................................................................................................... 1-1 

1.1 Introduction ........................................................................................................... 1-1 

1.2 Model Validation Standards and Benchmarks ................................................. 1-1 

1.3 Preliminary Assessment....................................................................................... 1-2 

2.0 Calibration and Validation Standards ..................................................................... 2-1 

2.1 Introduction ........................................................................................................... 2-1 

2.2 Overview of Validation Process.......................................................................... 2-2 

2.2.1 Objectives of Model Validation ................................................................ 2-2 

2.2.2 Model Validation Process.......................................................................... 2-3 

2.2.3 Use of Validation Standards and Benchmarks ....................................... 2-4 

2.3 LRTP Models with Transit................................................................................... 2-5 

2.3.1 Checking Input Data .................................................................................. 2-6 

2.3.2 Trip Generation........................................................................................... 2-9 

2.3.3 Trip Distribution ......................................................................................... 2-11 

2.3.4 Mode Choice................................................................................................ 2-14 

2.3.5 Trip Assignment ......................................................................................... 2-18 

2.4 Other Model Applications ................................................................................... 2-22 

2.4.1 LRTP Highway Only Models.................................................................... 2-23 

2.4.2 FTA New Starts Models............................................................................. 2-24 

2.4.3 Subarea Models........................................................................................... 2-28 

2.4.4 Corridor Models ......................................................................................... 2-29 

2.4.5 Models for DRIs and Other Traffic Impact Studies ............................... 2-30 

2.5 Summary and Other Findings............................................................................. 2-31 

2.5.1 State-of-the-Practice Findings ................................................................... 2-31 

2.5.2 Additional Guidance.................................................................................. 2-32 

3.0 Calibration and Validation Best Practices ............................................................... 3-1 

3.1 Introduction ........................................................................................................... 3-1 

3.2 Steps in Model Validation.................................................................................... 3-2 

3.2.1 Assess Modeling Needs............................................................................. 3-5 

3.2.2 Inventory Data Needs ................................................................................ 3-5 

3.2.3 Institutional Framework............................................................................ 3-6 

3.2.4 Secondary Source Data Collection ........................................................... 3-7 

3.2.5 Primary Source Data Collection ............................................................... 3-7 

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7593.560 

i




(continued) 

3.2.6 Data Checking ............................................................................................. 3-8 

3.2.7 Model Estimation........................................................................................ 3-11 

3.2.8 Model Implementation .............................................................................. 3-12 

3.2.9 Model Calibration....................................................................................... 3-13 

3.2.10 Model Validation........................................................................................ 3-14 

3.2.11 Model Application ..................................................................................... 3-15 

3.2.12 Iterate ........................................................................................................... 3-17 

3.3 Guidance on Validation and Adjustment.......................................................... 3-17 

3.3.1 Model Input Data........................................................................................ 3-18 

3.3.2 Validating Trip Generation Models ......................................................... 3-18 

3.3.3 Validating Trip Distribution Models ....................................................... 3-22 

3.3.4 Validating Mode Choice Models.............................................................. 3-26 

3.3.5 Validating Assignment Models ................................................................ 3-34 

3.4 Special Considerations ......................................................................................... 3-42 

3.4.1 Validation for FTA New Starts Projects .................................................. 3-43 

3.4.2 Subarea and Corridor Validation ............................................................. 3-43 

3.4.3 Site Impact Validation................................................................................ 3-45 

3.4.4 Other Validation Practices......................................................................... 3-46 

3.5 Summary and Future Directions......................................................................... 3-48 

3.5.1 Transferable Model Parameters................................................................ 3-48 

3.5.2 The Impact of New Model Paradigms on Validation............................ 3-48 

4.0 Guidelines for Model Application............................................................................ 4-1 

4.1 Stability of Model Parameters ............................................................................. 4-1 

4.2 Typical Model Applications and Relevant Guidelines.................................... 4-2 

4.2.1 MPO LRTP Updates ................................................................................... 4-3 

4.2.2 Comprehensive Plans................................................................................. 4-4 

4.2.3 Strategic Intermodal System (SIS) Plans.................................................. 4-5 

4.2.4 Campus Master Plans ................................................................................ 4-6 

4.2.5 Concurrency Applications and DRIs ....................................................... 4-6 

4.2.6 Congestion Management System (CMS) Plans ...................................... 4-6 

4.2.7 Air Quality and Climate Change.............................................................. 4-7 

4.2.8 Corridor Studies.......................................................................................... 4-8 

4.3 Model Application Checks .................................................................................. 4-9 

ii 


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(continued) 

Appendix A Literature Review Bibliography................................................................. A-1 

Appendix B Literature Review Summary of Available Standards ............................. B-1 

Appendix C NHTS Summary Validation Statistics....................................................... C-1 

Appendix D Model Validation Summary of Available Standards.............................. D-1 

Appendix E Supplemental Model Metrics ..................................................................... E-1 

Appendix F Draft Calibration-Validation Outline/Checklist ..................................... F-1 

Appendix G 

Calculation of Percent Highway Assignment Errors by 

Volume Group.............................................................................................. G-1 

Appendix H Best Practices Bibliography......................................................................... H-1 

Appendix I Best Practices Model Validation Worksheet ............................................ I-1 

Appendix J 

Federal Transit Administration Guidance on Travel Demand 

Forecasting for New Starts Projects........................................................... J-1 


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List of Tables 

2.1 Overview of Data Checks ............................................................................................ 2-6 

2.2 Aggregate Trip Rate Benchmarks............................................................................... 2-9 

2.3 Percent Trips by Purpose............................................................................................. 2-10 

2.4 Average Trip Length and Frequencies by Purpose.................................................. 2-12 

2.5 Percent Intrazonal Trips............................................................................................... 2-14 

2.6 Example Trip Validation Targets for HBW Trips..................................................... 2-16 

2.7 Mode Choice Validation Standards and Benchmarks ............................................. 2-16 

2.8 Supplemental Mode Choice Parameter Settings ...................................................... 2-17 

2.9 Volume-Over-Count Ratios and Percent Error......................................................... 2-19 

2.10 Revised Percent Error by Volume Group.................................................................. 2-20 

2.11 Root Mean Square Error (RMSE)................................................................................ 2-21 

2.12 Transit Assignment Validation ................................................................................... 2-22 

2.13 Suggested Auto Occupancy Rates.............................................................................. 2-23 

2.14 Mode Choice Parameter Settings for FTA New Starts ............................................ 2-25 

2.15 Volume-Over-Count Ratios for Corridor Validation............................................... 2-30 

3.1 Example Validation Worksheet on Root Mean Square Error................................. 3-15 

3.2 Example Target Validation Matrix ............................................................................. 3-28 

3.3 Mode Choice Model Parameters from U.S. Urban Areas ....................................... 3-30 

D.1 Checklist of Available Validation Standards from Literature: 

Trip Generation ............................................................................................................. D-1 


Trip Distribution ........................................................................................................... D-3 


Mode Choice.................................................................................................................. D-6 


v


Model Validation and Calibration Standards 

List of Tables 

(continued) 


Trip Assignment............................................................................................................ D-8 

D.5 Ranges of Model Statistics from Model Validation Reports 

Trip Generation, Trip Distribution, and Mode Choice ................................................... D-16 

D.6 Ranges of Model Statistics from Model Validation Reports 

Auto Occupancy and Trip Assignment .......................................................................... D-19 

E.1 Supplemental Model Metrics ...................................................................................... E-1 

F.1 Type of Model Application: Regional and MPO LRTPs, and Congestion 

Management Plans Models with Transit Networks ..................................................... F-1 

F.2 Type of Model Application: Regional and MPO LRTPs, and Congestion 

Management Plans Highway Only Models.................................................................. F-10 

F.3 Type of Model Application: FTA New Starts Projects............................................ F-17 

F.4 Type of Model Application: Subarea Studies, Comprehensive Plans, Campus 

Master Plans, Sector Plans, and Special Area Plans................................................. F-24 

F.5 Type of Model Application: Corridor/Toll Feasibility Studies, Interstate 

Master Plans, IJRs/IMRs, PD&E Studies, Final Design Studies ............................ F-28 

F.6 Type of Model Application: DRIs, Concurrency Applications, and Other Site 

Impact Studies Mostly Future Year Model Checks....................................................... F-33 

G.1 Calculation of Percent Highway Assignment Errors by Volume Group 

Initial Thresholds – Mid Count....................................................................................... G-1 


Initial Thresholds – High Count...................................................................................... G-2 


Revised Thresholds – Mid Count .................................................................................... G-3 


Revised Thresholds – High Count ................................................................................... G-4 

J.1 Reasonable Estimates of Mode Choice Model Coefficients .................................... J-6 

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List of Figures 

2.1 Example of HBW Trip Length Frequency Distribution........................................... 2-12 

2.2 Examples of Coincidence Ratios for Trip Distribution............................................ 2-13 

3.1 Validation Process......................................................................................................... 3-4 

3.2 Example Maps for Visualizing Socioeconomic Densities........................................ 3-9 

3.3 Centroid and Centroid Connector Coding Examples.............................................. 3-11 

3.4 Example Model Estimation Script .............................................................................. 3-12 

3.5 Cube Base User Interface ............................................................................................. 3-13 

3.6 Trip Length Frequency Distribution .......................................................................... 3-14 

3.7 Elasticities by Model Step ............................................................................................ 3-16 

3.8 Iterative Model Process................................................................................................ 3-17 

3.9 Visual Display of Network Characteristics ............................................................... 3-19 

3.10 Example Node-Point Chart ......................................................................................... 3-22 

3.11 District-Level Desire Line Example............................................................................ 3-25 

3.12 Example of Walk Access Buffering ............................................................................ 3-27 

3.13 Scatterplot of Estimated Volumes versus Traffic Counts........................................ 3-37 

3.14 Penalties and Prohibitors ............................................................................................. 3-39 

3.15 Example of Screenlines, Cutlines, and Cordon Lines .............................................. 3-40 

3.16 Example Auto Access Shed ......................................................................................... 3-42 

3.17 Subarea Study Area Examples .................................................................................... 3-44 

3.18 Representative Zone System for Site Impact Study ................................................. 3-46 

4.1 Example of External Trip Movements ....................................................................... 4-4 

4.2 Freight Tonnages for Use in SIS Corridor Planning ................................................ 4-5 


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List of Figures 

(continued) 

4.3 Sketch Planning Scenario Testing............................................................................... 4-7 

4.4 Comparative Analysis of Alternatives....................................................................... 4-10 

J.1 FTA Comments on Frequently Included Traveler Characteristics ........................ J-3 

J.2 FTA Comments on Frequently Included Trip Characteristics ............................... J-4 

J.3 FTA Comments on Frequently Included Other Characteristics............................. J-4 

viii 




Executive Summary 

The Florida Department of Transportation (DOT) initiated a study of model validation 

and calibration standards in early 2007. Florida DOT and the Model Task Force (MTF) are 

responsible for overseeing conversion of the Florida Standard Urban Transportation 

Model Structure (FSUTMS) from TRANPLAN to the Cube-Voyager travel demand modeling 

software platform. This change has provided a good opportunity to update previous 

approaches to modeling in Florida, including procedures for validating and 

calibrating models. Prior to initiating this project, the Florida DOT completed an earlier 

phase of the FSUTMS-Cube Framework that focused on sources for default model 

parameters. 

This document represents a compendium of three reports previously prepared during the 

FSUTMS-Cube Framework Phase II: Model Calibration and Validation Standards Study: 

• Technical Memorandum – Literature Review; 

• Technical Report No. 1 – Model Validation Guidelines and Standards; and 

• Technical Report No. 2 – Best Practices for Model Validation. 

• ES.1 Literature Review 

The first deliverable for the Model Calibration and Validation Standards, as described in 

Section 1.0 of this Final Report, was an extensive literature review to identify relevant 

model calibration and validation guidelines and standards as well as best practices in 

model validation. Reports describing Florida standard model procedures were used 

along with other well-known national publications. Additional reports referenced in 

these national publications were obtained as well. An Internet search for other potential 

documents was conducted using relevant key words. Readily available model validation 

technical reports were also compiled for subsequent use. Recent FDOT-funded model 

research reports were also reviewed for applicable information. 

This section of the report provides an introduction to the concepts of validation standards 

and benchmarks, which are discussed in greater detail during Section 2.0. Preliminary 

findings from the literature review are provided on accuracy standards, common statistics 

cited, trip purpose stratifications, and data collection efforts. Observations are provided 

on typical validation processes and checks documented in other studies and peer review 

analyses. This section of the report then concludes with a brief discussion on lessons 

learned from the literature review on improving the validation process. 


ES-1



• ES.2 Model Validation Guidelines and Standards 

The guidelines and standards provided in Section 2.0 of this Final Report represent an 

optimum, or ideal, level of model calibration and validation. The term “standards” is used 

in this report to describe desirable accuracy levels for comparing estimated and observed 

metrics. In many instances, these updated standards are more stringent than prior recommended 

accuracy ranges used in Florida. Guidelines and “benchmarks” conversely 

represent documented statistical ranges found in recent literature, travel demand models, 

and other sources such as the National Household Travel Survey (NHTS). Some preexisting 

models might not be consistent with updated standards and guidelines as these 

revisions are being established for new FSUTMS/Cube-Voyager models either under 

development or planned for development in the future. 

An overview of the model validation process in this section includes discussions on 

objectives, procedures, and the use of standards and benchmarks. Subsequent discussions 

in this section of the report focus on recommended model validation and calibration 

checks, guidelines and standards for a variety of different study applications related to 

each module of the four-step modeling process. Studies covered include long-range 

transportation plan (LRTP) models with transit, LRTP highway-only models, FTA New 

Starts models, subarea models, corridor models, and traffic impact studies. This section 

concludes with additional discussions on state-of-the-practice findings and additional 

guidance. 

• ES.3 Best Practices for Model Validation 

Section 3.0 of this Final Report presents a draft set of best practices and procedures in 

conducting model calibration and validation efforts. While it is best practice to validate 

every step of the model chain, far too many model validation efforts are predominantly 

focused on highway assignment statistics with minimal effort expended on validating trip 

generation, trip distribution, and mode choice models. Since a variety of validation techniques 

have been employed in Florida over the years, it is important to distinguish defensible 

adjustments from questionable modifications. Likewise, guidance is needed to 

identify proper increments of adjustment that will result in reasonable travel demand 

forecasts. 

This section of the report first describes a recommended step-by-step modeling process, 

including model estimation, development, calibration, validation, and application. This is 

followed by a discussion of each step in the model chain and actions to be taken in 

response to a variety of model validation and calibration errors. Guidance on model validation 

checks are provided for a variety of study types, building off checklists established 

earlier in Section 2.0. The report also touches on emerging validation issues, transferable 

parameters, and shifting paradigms in modeling. 

ES-2 




• ES.4 Guidelines for Model Application 

The Final Report concludes with a discussion of guidelines for model application. While 

some limited discussion on this topic was provided in the previous study Technical 

Reports, Section 4.0 is focused solely on model application guidelines. While statistical 

accuracy standards are not as prevalent with model applications when compared to 

model calibration and validation, this does not preclude the importance of logic checks in 

reviewing model forecasts. Guidance on a variety of model application types is provided 

in this section, along with some examples of model application checks, and a list of commonly 

used metrics for future year scenario testing. 


ES-3



1.0 Literature Review 

• 1.1 Introduction 

In coordination with the Florida DOT, Cambridge Systematics, Inc. (CS) conducted an 

extensive literature review to identify relevant model calibration and validation guidelines 

and standards as well as best practices in model validation. Reports describing 

Florida standard model procedures were used along with other well-known national 

publications. Additional reports referenced in these national publications were obtained 

as well. An Internet search for other potential documents was conducted using relevant 

key words. Readily available model validation technical reports were also compiled for 

subsequent use. Finally, recently FDOT-funded model research reports were also 

reviewed for applicable information. The bibliography provides a complete list of sources 

reviewed. 

This section of the Final Report summarizes findings from Subtask 1, Literature Review. 

Appendix A of this Final Report is a complete bibliography of reports, presentations, and 

published technical papers evaluated as part of the literature review and subsequently 

used in preparing guidelines and standards. Appendix B presents a summary of model 

validation standards, benchmarks, and additional parameters suggested in available literature. 

Appendix C is a summary of model parameters and benchmarks derived from 

the National Household Travel Survey (NHTS) as previously documented during Phase I. 

• 1.2 Model Validation Standards and Benchmarks 

As most practitioners in Florida are aware, validation accuracy standards were originally 

established in the 1980s as part of the FDOT Model Update series of technical reports. Some 

updated recommendations were subsequently included in the FSUTMS Users Library CD, 

prepared in the 1990s. Model benchmarks were recently obtained from the National 

Household Travel Survey (NHTS) and several Florida models as documented in the Cube- 

Voyager Model Parameters Technical Report published in 2006 (these statistics are not discussed 

in this memo). Appendix B compiles a variety of standards and benchmarks by 

each model step. 

For the purposes of this study, validation benchmarks represent typical values or ranges 

documented in national publications, FDOT technical reports, or model validation studies. 

Benchmarks are used extensively in trip generation to identify whether aggregate trip 

rates and ratios, and trip purpose stratifications from a base year model appear reasonable. 

Florida DOT, Systems Planning Office 1-1



Some benchmarks have been defined for trip distribution and trip assignment but 

validation of these model steps, as well as mode choice, is largely focused on accuracy 

standards. 

Accuracy standards are generally based on comparisons between model estimates and 

observed values. In trip distribution, model accuracy can be determined by comparing 

model estimated trip lengths, trip length frequency distribution curves, and percent intrazonal 

trips against a household travel survey database or the Census Transportation 

Planning Package (CTPP) for work trips. Mode split targets, established from surveys and 

other data sources, can be compared against mode splits output from the logit model. 

Traffic assignment model validation accuracy is generally based on comparisons between 

ground counts and model generated traffic estimates, while transit assignment accuracy 

generally looks at the relationship between corridor, subarea, or regional transit ridership 

estimates and model loadings. 

Also, while searching for benchmarks and standards, some additional model parameter 

settings were identified, including free-flow speeds, terminal times, and auto occupancy 

rates. Again, the Cube-Voyager Model Parameters Technical Report includes a number of 

default model parameter settings. A selection of these statistics is provided in 

Appendix C. Recommendations on new accuracy standards, benchmarks, and parameters 

are identified later in Section 2.0, including comments from the Florida DOT and the 

Model Task Force. 

• 1.3 Preliminary Assessment 

A quick review of Appendix B shows a variety of accuracy standards and benchmarks 

from different publications. Some standards and benchmarks have changed through the 

passage of time, both in response to changing conditions and policies being addressed by 

models. At the same time, a number of publications refer to the same accuracy standards 

and benchmarks, some of which date back to the early 1990s (which also referenced even 

older publications). 

The last two pages of Appendix B include a range of model validation statistics documented 

in a number of technical reports from around Florida and the U.S. Some 

preliminary findings from this assessment include the following: 

• Outside Florida, most models rely on the reports Calibrating and Adjustment of System 

Planning Models, Model Validation and Reasonableness Checking Manual, and NCHRP 365 

for model validation accuracy standards and benchmarks. In addition to Florida, a 

few other states have established their own accuracy standards such as California, 

Michigan, Oregon, and Tennessee. 

• The most commonly reported statistics include percent trips by purpose, terminal 

times by purpose, average trip length by purpose, auto occupancy rates by purpose, 

1-2 Florida DOT, Systems Planning Office



volume-over-count ratios by facility type/functional class and screenline, and RMSE 

by volume group or facility type/functional class. 

• Some statistics not commonly reported in Florida are used in other states, including 

the ratio of employment to population, person trips per TAZ, and VMT per person. 

RMSE is often summarized by facility type or functional classification rather than volume 

group, as typically reported in Florida validation studies. Other statistics commonly 

used elsewhere, but not found in Appendix B, include trip flows between 

counties, subareas, and across screenlines; RMSE, percent error, and percent VMT 

error on major corridors; absolute difference between volumes and counts; transit 

counts and ridership estimates by screenline; scatter plots with R2 of volumes versus 

counts; and a variety of statistics by time-of-day. 

• Some of the larger models in Florida might over-specify trip purposes as even most of 

the large models reviewed during this assessment (Atlanta, Baltimore, San Francisco, 

Seattle) typically use the same trip purposes as standard FSUTMS, with home-based 

social/recreation often supplanted by the home-based school purpose. Some of these 

models differentiate between grade school and university trips. The Atlanta region 

uses a separate airport model to generate these trips. The majority of moderate sized 

models outside Florida only use HBW, HBO/HBNW, and NHB purposes. 

• Interestingly, some of the largest and most sophisticated models outside Florida exhibited 

larger errors than the smaller models, particularly with regards to RMSE. 

• A number of large MPO models outside Florida have conducted speed studies and 

include comparisons of observed versus estimated speeds. 

• For better or worse, Florida has many more area types and facility types than other 

models in the U.S. Most MPOs seem to use area type categories of high density, 

urban, suburban, exurban, and rural. Few models separate one-way streets from other 

facility types while division of arterials into functional classifications of principal versus 

minor is more common than categories of divided versus undivided arterials. 

Based on contacts with a number of MPOs through model validation studies, peer review 

panels, and conducting model validation seminars, a number of additional findings can be 

summarized by model step: 

• Systematic checks of network and socioeconomic data are not the norm but visual 

checks are very common. Network checks are generally reactive, addressing validation 

problem spots. 

• Very little validation checking is done at the trip generation step. Checks against 

observed values require home-interview travel surveys. 

• Established trip distribution accuracy standards are not usually met. Checks of trip 

length frequency distributions and intrazonal trips are seldom done. K-factors are 

common in about half the models outside Florida that we are familiar with. 




• Unlinked trips are frequently compared to transit boarding counts at the regional 

level. Sensitivity analysis is not typically completed nor are forecast year checks. 

• Highway assignment is generally where most validation checking is focused, typified 

by screenline checks, and regional RMSE and VMT. A large number of models do not 

check time-of-day traffic estimates in spite of having time-of-day components while 

speeds and forecast year volumes are not usually checked for reasonability. 

• Transit assignment checks are usually completed only at the regional level or using 

groups of routes by corridor or subarea. 

Some lessons learned from the literature review on improving the validation process 

would include, but not be limited to, the following considerations: 

• Some model validation adjustments improve base year validity to the detriment of 

forecast year statistics. 

• Traffic count data need to be thoroughly checked before validation begins, acknowledging 

errors inherent in the traffic counting and normalizing process. 

• Model validation should not be considered complete until forecast year sensitivity 

tests are completed. 

• Input data should be checked more thoroughly and every model component should be 

checked. 

• A somewhat different set of model checks should be established for short term versus 

long term forecasting, and systemwide versus subarea projects. 

• Models should be checked before proceeding with any application, including verification 

that the model produces appropriate results for the particular application. 

• The Federal Transit Administration (FTA) New Starts modeling guidelines have 

improved validation practices in a number of ways including refinement of transit 

network coding procedures, standardization of mode choice parameters, and use of 

SUMMIT as a diagnostic tool. 

• A proper balance should be established between validating to traffic counts and 

observed speeds as frequently there is a direct conflict between model adjustments 

used to match volumes versus speeds. 

This literature review and its preliminary findings subsequently served as a source for the 

development of calibration and validation standards, benchmarks, and best practices. 




2.0 Calibration and Validation 

Standards 


The model calibration and validation guidelines and standards provided in this section 

represent an optimum or ideal level of model calibration and validation. The term 

“standards” is used in this section to describe desirable accuracy levels for comparing 

estimated and observed metrics. In many instances, these updated standards are more 

stringent than prior recommended accuracy ranges used in Florida. Guidelines and 

“benchmarks” conversely represent documented statistical ranges found in recent 

literature, travel demand models, and other sources such as the NHTS. Some pre-existing 

models might not be consistent with updated standards and guidelines as these revisions 

are being established for new FSUTMS/Cube-Voyager models either under development 

or planned for development in the future. 

The literature review and related findings played an important role in developing the 

draft set of model validation guidelines and standards described in this section. For the 

purposes of this section of the report, performance statistics were also derived directly 

from model outputs in order to increase the number of observations for establishing standards 

and benchmarks. New assignment error percentages were also calculated for this 

study based on 2007 Updates to Florida DOT’s Level of Service (LOS) Handbook. In addition 

to modifying the percent error, the volume groups also were adjusted to better 

synchronize with daily volumes contained in the LOS Handbook. 

Subsequent paragraphs of Section 2.0 focus on recommended model calibration and 

validation checks, as well as guidelines and standards for a variety of different study 

applications related to each module of the four-step modeling process. As described in 

Section 1.0, Appendix A provided background information from the literature review 

which is vital to an understanding of the recommended guidelines and standards. 

Appendix D presents a summary of model validation standards, benchmarks, and 

additional parameters suggested in available literature while Appendix E provides other 

measures derived from reviewing validation reports and model outputs. Appendix F 

contains a validation checklist used as the basis for discussions in Sections 2.3 and 2.4 of 

this report. Appendix G depicts a spreadsheet used during preparation of this report to 

iteratively adjust and calculate error percentages for volume groups such that most errors 

would minimize incorrect lane call estimates. 




• 2.2 Overview of Validation Process 

The process of model calibration and validation is vital to producing defensible travel 

demand forecasts. Florida standards for model calibration and validation were initially 

defined as part of the Model Update series of studies in the early 1980s. At that time, 

models were run on mainframe computers and the primary function of models was to 

support the long-range transportation plan (LRTP) update process. The use of mainframe 

computers for modeling in Florida ceased by the early 1990s and the increasing speed and 

capacity of microcomputers has expanded the use of travel demand models to a multitude 

of applications. 

It is recognized that different model applications require a variety of model validation 

checks and, in some cases, accuracy standards, and guidelines. The Florida DOT, with 

support from Cambridge Systematics, Inc., has led development of a validation checklist 

organized first by model application type and secondly by the four steps generally used in 

travel demand modeling. The checklist, depicted in Appendix F, identifies model calibration 

and validation checks, standards, and benchmarks to be used in developing models 

for LRTPs, subarea studies, FTA New Starts, corridor studies, and developments of 

regional impact (DRIs). The highest priority model checks are listed in Appendix F with 

bold type. 

2.2.1 Objectives of Model Validation 

One of the objectives for this document is to review model calibration and validation 

standards that have been used in the past in light of new applications and new requirements 

for the model. The study has examined the need to establish new standards and to 

develop guidelines on how to achieve these standards. Models are developed for engineering 

and planning applications, and different applications have different requirements 

in terms of levels of sophistication and accuracy. Model standards are therefore driven by 

the needs and requirements of the model applications. 

Model validation serves several purposes: 

• Providing a level of comfort to modelers, planners, policy and decision-makers, and, 

to some extent, the general public that the model is able to produce accurate results. 

The main way in which this is demonstrated is through comparisons of model results 

to observed data. 

• Providing evidence that model results are accurate enough to be used for the desired 

planning analyses. For example, the “plus or minus one lane” standard for highway 

assignment error originally came into use at a time when the primary use of models 

was to design highways with adequate capacity. 

• Accounting for the errors in observed data used for comparisons. For example, validation 

guidelines produced by the Federal Highway Administration (FHWA) in 1990 for 




percentage volume differences reflect the expected level of error in traffic counts, 

which can be quite high when using a 24 or 48 hour count to represent AADT. 

The last item is the only one that is not context specific (e.g., the error associated with traffic 

count data is the same regardless of what the model is being used for). A problem is 

that the errors associated with some data sources are unknown while for others, including 

traffic counts, the error ranges exceed desirable differences between model results and 

observations. 

Regarding sufficient accuracy for the desired planning analyses, it must be recognized 

that many of the validation checks are intermediate outputs that are not directly used in 

the planning analyses. For example, for a highway study, the key outputs might be roadway 

volumes and speeds; for a transit study, they might be line or station level ridership. 

But there are no planning analyses where the desired outputs are average trip lengths, or 

trips per household by purpose, or percent intrazonal trips. Thus there are no “acceptable” 

error ranges for these intermediate outputs. In this context, the importance of 

“getting them right” is important only in terms of their contribution to the errors in the 

ultimate desired outputs. 

Another issue with error ranges for the “ultimate outputs” is that there are often no clear 

“acceptable ranges.” For example, for a transit project, an objective of obtaining accurate 

ridership forecasts may be to determine whether the project is worthwhile to build. It is 

impossible to say, for example, that a project will not be worthwhile if we underestimate 

ridership by 20 percent, but will be OK if the error is only 10 percent. 

There is a significant issue in that the accuracy of replicating base year numbers does not 

guarantee that errors in forecasts will be in the same range. If the base year modeled volume 

on a link is within 10 percent of the traffic count, one cannot say that the forecast year 

volumes will be within 10 percent, or within any specific percentage, of the actual volume. 

The error ranges based on past experience are those that provide a level of comfort to 

modelers, and perhaps to planners who are used to looking at model results. If model 

results are at least as good as those from other models, that provides some assurance 

about the model’s accuracy. 

2.2.2 Model Validation Process 

The terms “calibration” and “validation” are sometimes used interchangeably. In Florida, 

the two terms have typically been distinguished as follows: 

• Model Calibration – A process where models are adjusted to simulate or match 

observed household travel behavior in the study area; and 

• Model Validation – The procedure used to adjust models to simulate base year traffic 

counts and transit ridership figures. 




Model calibration implies the availability of household travel survey data to adjust the 

model to match observed trip generation rates, trip length frequency distributions, aggregate 

trip movements, and mode shares. Model validation could include some components 

of calibration if household survey data are available; however, survey data are not 

required in adjusting the model to match traffic counts. The calibration and validation 

guidelines and standards outlined in this section represent optimum levels of accuracy. 

Achieving the accuracy standards and benchmarks found in this section does not ensure 

that the model was developed correctly, as all assumptions and adjustments to model 

parameters during calibration and validation must be defensible and documented. 

Validation also consists of reasonableness and sensitivity checks beyond matching base 

year travel conditions. The standards included in this section therefore include such 

checks as the reasonableness of model outputs and the elasticities of demand with respect 

to input variables. General guidance on improving the validation process should include 

the considerations documented earlier in the literature review. 

It should always be remembered that the purpose of the travel model is to estimate or 

forecast travel conditions for some alternative scenario(s) other than the existing situation. 

Inclusion of factors, constants, or parameters that do not vary between the base and 

alternative scenarios implies that what is represented by these parameters does not change 

between the scenarios. The more a model relies on such parameters, the less explanatory 

capability it has. 

2.2.3 Use of Validation Standards and Benchmarks 

The standards and benchmarks in this section form what is essentially a checklist for 

model validation. The list should not be considered as one where validation is achieved 

when every single box is checked while one unchecked box implies a model that is not 

validated. 

The benchmarks contained in the following subsections of Section 2.0 represent “reasonable 

ranges” for specific model parameters or outputs. Generally, benchmarks represent 

the range of experience from models in other areas, and many of them are therefore wide 

ranges. In these cases, if a model’s values fall outside the benchmark range, one should 

have a reasonable explanation for the difference based on knowledge of the model area. 

Some examples are: 

Benchmark Low High Possible Explanations 

Regionwide Persons/DU 2.0 2.7 Unusually high concentration of elderly might 

result in low average household size 

HBW Person Trips/Employee 1.20 1.55 Areas with many part-time or seasonal jobs 

might have fewer average daily work trips 

Percent Intrazonal – Total Trips 3% 5% Fine zone system might result in lower 

intrazonal percentages 




In addition, there may be observed data (for example, from household travel surveys) that 

provide strong evidence the area has different travel or traveler characteristics than is 

indicated by the benchmarks. 

The standards represent acceptable (or preferable) ranges of percentage error in models 

compared to observed data. Here it is important to consider the error associated with the 

observed data source. For example, the error associated with parameters derived from a 

small sample household survey is larger than that from a larger survey. Of course, some 

standards cannot be quantified if local data are not available. 

A model that fails to meet a small number of the standards described in this section 

should not be considered “not validated” simply based on that fact. If the model meets 

most standards and is close on the remaining ones, it would be better not to make unjustified 

adjustments to model parameters or data simply to achieve a better fit. For example, 

inserting K-factors without a reasonable explanation to achieve a better match with 

observed origin-destination patterns could reduce the explanatory power of a trip distribution 

model for forecasting. Adjusting network speeds to less reasonable levels to 

achieve a better match between modeled and observed traffic volumes would be unwise 

as well. 

It is important to consider the planning context in which the model will be used in determining 

which standards are most important to achieve. For example, models used for 

transit studies should have good mode choice results. However, as discussed previously, 

achieving a better match for a base year model output does not ensure that the model will 

predict that output more accurately for forecast year applications. 

• 2.3 LRTP Models with Transit 

Model calibration and validation is typically initiated at the start of an LRTP Update process. 

LRTPs must be revised and updated every 4 to 5 years and most of these updates 

include use of a new travel demand forecasting model. Most LRTP models include transit 

networks and pathbuilding; however some of the smaller MPO models do not include the 

capability of modeling transit. This section of the report discusses model calibration and 

validation of LRTP models with transit components. For the purposes of this section, 

LRTP transit models represent a set of default validation checks, guidelines, and standards. 

Different model checks and accuracy standards for other applications, including 

LRTP models with highway only capabilities, are described in subsequent sections of this 

report. Appendix F, Table F.1, presents a validation checklist for LRTP transit models 

with the highest priority checks identified in bold font. 




2.3.1 Checking Input Data 

The checking of input data is extremely important both preceding validation and iteratively 

during the calibration/validation process. Table 2.1 is a listing of input data checks 

that should be included as part of a model calibration or validation. Both systematic 

checks and visual checks are included here but most validation studies reference only visual 

checks. 

Table 2.1 

Overview of Data Checks 

Benchmarks/Settings 

Statistic Low High 

Socioeconomic Data 

Cube and GIS visual and statistical comparisons/checks a 

Regionwide Persons/DU (or HH) 2.0 2.7 

Regionwide Employment/Population Ratio 0.35 0.75 

Regionwide Autos/DU (or HH) 1.75 2.10 

Approximate Population/TAZ N/A 3,000 

Highway Network Data 

Transit Network Data 

Highway and Transit Speed Data 

Terminal Times (in minutes) 

Cube and GIS visual and statistical comparisons/checks a 

Check access links; compare routes against GIS data a 

Ensure logical hierarchy by AT/FT/NL/mode a 

CBD: 3-5 minutes; Fringe/OBD: 2-4 minutes; 

residential/rural: 1-2 minutes 

a See report text for additional guidance. 

Checking of socioeconomic data is a routine part of model validation. Visual checks of 

socioeconomic data generally involve preparing thematic maps using color shadings to 

indicate ranges of absolute numbers or densities in each TAZ or District. An updated land 

use checking routine has been included in the Florida DOT’s Olympus training model that 

identifies data syntax errors such as missing values, values out of typical range, and ratios 

between different socioeconomic attributes in the zonedata.dbf file. Cube can summarize 

data using different chart types overlaid on the zone system. Node-point charts can be 

prepared in Cube to display relationships between dwelling unit types, auto availability 

groupings, or employment categories. 

Regional estimates of persons per dwelling unit (DU), employment/population, and 

autos/DU can easily be output by Cube-Voyager, as found in the Olympus trip generation 

routine. The acceptable ranges listed above in Table 2.1 were derived from the previously 

described literature review in conjunction with statistics pulled directly from model outputs 

and in some cases, computed based on other reported metrics. The complete NHTS 

sample identifies persons per DU at 2.59 while the current Florida sample shows 2.46 persons 

per DU. Recommendations on population per TAZ are consistent with a recently 

published Florida DOT White Paper on TAZs and reflect ongoing discussions by the U.S. 

Census officials on geography requirements for Census 2010. Population per zone can be 




summarized regionally to estimate a general relationship for the model or this statistic can 

be calculated by individual TAZ to identify specific areas for zone splitting. 

As with socioeconomic data, systematic checks of network data are not the norm in practice 

but visual checks are very common, although generally reactive and focused on 

addressing validation problem spots. Visual highway and transit network checks are 

critical in identifying coding errors. Color-coded maps of highway network area types, 

facility types, laneages, traffic counts, and screenlines should be prepared prior to model 

validation. While such maps can be viewed on-screen, plotting allows for a permanent 

record of quality assurance reviews. The reliability of traffic counts used in highway 

assignment validation is a critical issue that will be more fully explored in subsequent 

research. Other highway network-related data to check would include penalties, prohibitors, 

and toll data. Transit network maps likewise can be prepared and color-coded 

by mode, headway, operator, etc. GIS route data from some of the state’s larger transit 

agencies can be used in transit network coding and debugging. Walk and auto access 

should also be evaluated for reasonability, along with pathbuilding checks for different 

transit modes. Once the model is executed, additional highway and transit network 

coding errors may also become apparent, particularly on links that consistently over- or 

under-assign trips. 

Highway speed data are typically found in a lookup table dimensioned by area type, 

facility type, and number of lanes; however, the Southeast Florida Regional Planning 

Model (SERPM) uses posted speeds in place of the synthetic speeds used in other models. 

Speed-delay studies have recently been conducted in Jacksonville and Tampa. It is not 

likely that speed data collected can be used in the context of a speed lookup table. At a 

minimum, speeds should be continuously reviewed for logical hierarchy (i.e., higher 

speeds on freeways, lower speeds on collectors, etc.). Locally collected speed data might 

benefit the coding of transit networks, especially in defining relationships between highway 

and transit speeds; however, models need to achieve a balance between what works 

best for highway modeling versus transit modeling. While a number of national sources 

report ranges on appropriate speeds and/or capacities, this topic is described further in 

the Cube-Voyager Model Parameters Technical Report. 

Terminal times are walk times required to travel from trip origin to auto and from auto to 

final destination. As discussed in the Cube-Voyager Model Parameters Technical Report, very 

little real data exists on typical terminal times for different area types. Terminal times are 

specified by area type in Florida and many other models throughout the U.S. Terminal 

times should exhibit a logical hierarchy. For example, it is logical that terminal times 

would be greatest in central business districts (CBD), where vehicles are often parked in a 

different block or TAZ from a work or shopping destination. Conversely, terminal times 

would be minimal in residential or rural areas, where a vehicle is typically parked on the 

owner’s personal property. Typical ranges in minutes by primary area type were 

depicted in Table 2.1. 

Of course, terminal times for primary area types should be further categorized into additional 

area type groupings in FSUTMS. For example, a High-Density OBD or Beach OBD 

area might experience higher terminal times than other OBD areas. Likewise, a Primary 




City CBD would likely experience a higher terminal time than a Non-Urbanized Small 

City CBD. It also should be noted that non-Florida models use different area type categories 

such as urban, suburban, and rural. Assumptions were made to translate each area 

type from studies outside Florida to FSUTMS primary area types. 




2.3.2 Trip Generation 

Regarding trip generation, relatively minimal validation checking is typically performed 

other than a review of input socioeconomic data, as described earlier. Checks against 

observed values require home interview travel surveys which have been cost prohibitive 

for many DOTs and MPOs. The following discussion focuses on recommended validation 

and calibration standards for trip generation. 

Aggregate Demographic and Trip Rate Benchmarks 

Aggregate trip rate benchmarks can be derived from a variety of sources, including 

Census data, household travel surveys, NHTS tabulations, other comparable models, as 

well as Federal and state guidelines on the modeling practice. Table 2.2 depicts typical 

ranges for aggregate trip generation rates that can be obtained from model outputs. As 

with other benchmarks provided in this section, acceptability ranges were mostly derived 

from national and state guidance documents, model outputs, and model validation study 

reports and then compared against available NHTS and Census statistics for Florida. It 

would be expected that most validated models would fall somewhere between the low 

and high values in this table. If the model is reporting statistics outside these ranges, 

additional verification and adjustment of socioeconomic data, trip production rates, trip 

attraction rates, dwelling unit weights, and/or special generators is likely warranted. 

Table 2.2 

Aggregate Trip Rate Benchmarks 

Benchmarks a 


Person Trips/TAZ N/A 15,000 

Person Trips/Person 3.3 4.0 

Person Trips/DU (or HH) 8.0 10.0 

HBW Person Trips/Employee 1.20 1.55 

a Generally excludes nonmotorized trips; including motorized trips could increase person trips per DU up to 11.5. 

As indicated above, zones with more than 15,000 person trip productions or attractions 

should be reviewed to identify zones for further splitting. Ideally, any TAZ with greater 

than 15,000 person trip ends should be split into multiple zones; however, some uses (e.g., 

a shopping mall) might be difficult or impossible to split out. No minimum number of 

trips is recommended in order to encourage zone sizes to be as small as necessary. The 

previously mentioned TAZ White Paper provides additional guidance in this regard. 

Aggregate rates of person trips per person, DU, and employee can easily be generated by 

the model and compared to the typical ranges in Table 2.2. As documented in the Cube- 

Voyager Model Parameters Technical Report, the NHTS shows 4.0 trips per person from the 

national sample and 4.02 trips per person from the current Florida sample, both at the 

upper boundary of typical rates. NHTS values for trips per DU also fall in line with the 




ranges in Table 2.2 at 8.43 (Florida sample) to 9.2 (national sample). Person trips per 

employee have historically been calculated using the sum of all purposes in Florida; however, 

it was decided that home-based work (HBW) trips should be the numerator. 

Percent Trips by Purpose 

The percent trips by purpose is an important way to gauge whether or not some trip production 

or attraction purposes are disproportionate when compared to other similar models. 

In order to provide a wide range of acceptable percent distribution of person trips by 

purpose, a variety of different trip generation structures were reviewed (e.g., standard 

Florida GEN, Florida Lifestyle Models, etc.). Also, since many models both in Florida and 

in other states use different trip purpose stratifications, the home-based other trip purposes 

were aggregated to provide a consistent point of comparison. 

Table 2.3 provides typical ranges of percent trips by each trip purpose. Some of these 

ranges are quite large so it is recommended that the modeler also review statistics on trip 

purpose from travel surveys, previous models of the same area as well as comparable 

regions in terms of population size and dominant employment types. Rules of thumb to 

consider are that HBW trips are usually 15 to 20 percent of regional trips and nonhomebased 

(NHB) trips generally comprise 25 to 33 percent of trips. 

Table 2.3 

Percent Trips by Purpose 

Benchmarks 


Percent Trips by Purpose – HBW 12% 24% 

Percent Trips by Purpose – HBSH 10% 20% 

Percent Trips by Purpose – HBSR 9% 12% 

Percent Trips by Purpose – HBSC 5% 8% 

Percent Trips by Purpose – HBO a 14% 28% 

Percent Trips by Purpose – HBNW b 45% 60% 

Percent Trips by Purpose – NHB c 20% 33% 

a 

b 

c 

HBO includes a variety of special trip purposes depending on the model (e.g., airport, college, and shop). 

HBNW accounts for all home-based trip purposes except HBW. 

NHB includes combined purposes for NHB Work and NHB Nonwork, where appropriate. 

Unbalanced Attractions versus Productions 

Most trip generation models balance the total number of home-based trip attractions to 

the total number of home-based productions by each purpose. The reason for balancing 

to productions is a greater sense of accuracy in socioeconomic estimates of population and 

dwelling units versus employment. Most trip generation models will end up with a larger 

number of home-based attractions than home-based productions, prior to balancing. Even 




though balancing might help resolve these regional differences, it is still good practice to 

review the ratio between unbalanced attractions and productions as a large difference 

might indicate problems with employment estimates, trip rates, etc. Most literature on 

best practices recommends that the difference between unbalanced regional attractions 

and productions be kept to +/-10 percent for each purpose, although a review of model 

validation reports shows many models that exceed this standard. Upwards of +/-50 percent 

difference at the regional level might be considered acceptable under certain conditions 

and trip purposes. 

Percent External-External Trips 

The building of an external-external (EE) trip table is now part of the FSUTMS-Cube/ 

Voyager trip generation process. The amount of regional EE trips varies considerably 

dependent on the size of a region, the proximity of neighboring urbanized areas, and the 

types of facilities that enter the region at external boundaries. It has been documented by 

three different sources in the literature review that the percent EE trips range between 

4 percent (large regions) and 21 percent (smaller urbanized areas). While this range represents 

a decent rule of thumb, it should be recognized that the percent EE trips is quite 

variable, by region and by facility type of each external zone. 

2.3.3 Trip Distribution 

Research has shown that established trip distribution accuracy standards are not usually 

met, although most of these checks require household travel survey data to conduct properly. 

Checks of trip length frequency distributions and intrazonal trips also are seldom 

done. K-factors are a common adjustment tool in about half the models outside Florida. 

Guidelines and standards are provided in this subsection for average trip lengths, frequency 

distributions by purpose, coincidence ratios, and percent intrazonal trips. 

Average Trip Length by Purpose 

Average trip length by purpose is among the most commonly reported benchmarks in all 

of model validation. Trip length estimates can be obtained from a multitude of sources, 

including household travel surveys, model validation studies, model guidance documents, 

the NHTS, and Census Journey-to-Work (JTW) for HBW trips. Trip lengths can be 

reported by mean, either in minutes, miles, or both, standard deviations, and trip length 

frequency distributions (TLFDs). Minutes is more common than miles in reporting the 

average while TLFDs are either graphed or listed by number and percent of trips on a 

minute-by-minute basis. Table 2.4 provides a summary of calibration and validation 

standards and benchmarks related to trip length, as derived from the literature review. 

Figure 2.1 depicts a TLFD for HBW trips, comparing estimated trips versus observed trips. 




Table 2.4 

Average Trip Length and Frequencies by Purpose 

Benchmarks 


Average Trip Length – HBW (minutes) 12 35 

Average Trip Length – HBSH (minutes) 9 19 

Average Trip Length – HBSR (minutes) 11 19 

Average Trip Length – HBSC (minutes) 7 16 

Average Trip Length – HBO a (minutes) 8 20 

Average Trip Length – NHB b (minutes) 6 19 

Average Trip Length – IE (minutes) 26 58 

Statistic 


Mean Trip Length, Observed Total Trips +/-3% 

Trip Length Frequency Distribution versus observed +/-5% 

Coincidence Ratios by Purpose c 70% 

a HBO includes a variety of special trip purposes, depending on the model (e.g., airport, college, and school). 

b NHB includes combined purposes for NHB Work and NHB Nonwork, where appropriate. 

c Some lower coincidence ratios have been deemed acceptable for trip purposes that had relatively few trips 

and therefore higher error rates. 

Figure 2.1 Example of HBW Trip Length Frequency Distribution 

Source: Model Validation and Reasonableness Checking Manual. 

Average trip lengths can vary significantly from one region to another due to region size, 

geography, and dispersion of activities and travel generators. A range of documented 

average trip length benchmarks by trip purpose is provided, along with a series of accuracy 

standards in comparing model estimates to observed survey data and standards for 




coincidence ratios. It is strongly recommended that average trip length comparisons be 

made against household travel surveys, wherever possible, in addition to similar models, 

NHTS, and the CTPP. Measures of dispersion should also be reviewed such as standard 

deviation, variance, and coincidence ratio. 

As documented in the Model Validation and Reasonableness Checking Manual, coincidence 

ratios provide a useful measure of dispersion in comparing two distributions by measuring 

the percent of area that coincides for two TLFD curves, such as observed versus 

estimated. The coincidence ratio lies between zero and one, where zero indicates two 

disjoint distributions and one indicates identical distributions. Figure 2.2 displays visual 

examples of poor and good coincidence. The formula for calculating the coincidence ratio 

is provided below: 

Coincidence = sum {min (count +T /count + , count -T /count - )} 

Total = sum {max (count +T /count + , count -T /count - )} 

Calculate for T = 1, maxT 

Coincidence Ratio = coincidence/total 

where, 

count +T = value of estimated distribution at Time T 

count + = total count of estimated distribution 

count -T = value of observed distribution at time T 

count - = total count of observed distribution 

Figure 2.2 Examples of Coincidence Ratios for Trip Distribution 

Source: Model Validation and Reasonableness Checking Manual. 

Percent Intrazonal Trips 

Measuring the percentage of intrazonal trips by purpose can be a means to identifying 

issues with friction factors, intrazonal time calculations, and even zone size. The percent 

of intrazonal trips is very dependent on zone size and composition. It is typical that HBW 

trips would have the smallest percentage of intrazonal trips while home-based nonwork 

(HBNW) trips, including shop, social recreational, and school, generally have the highest 

intrazonal activity. This is because a majority of workers choose to reside where shopping 




is nearby, recreational opportunities abound, and quality schools are available for their 

children. Large concentrations of employment are not necessarily close to these other 

amenities considered when deciding on a home location. 

Table 2.5 depicts a benchmark range of percent intrazonal trips by purpose and standards 

for comparing observed and estimated intrazonal activity. The best comparisons are 

against observed geocoded survey data. Since intrazonal percentages vary considerably, 

it is recommended that comparisons be made against percentages from other similar 

models where household surveys have not recently been completed. 

Table 2.5 

Percent Intrazonal Trips 

Benchmarks 


Percent Intrazonal – HBW 1% 4% 

Percent Intrazonal – HBSH 3% 9% 

Percent Intrazonal – HBSR 4% 10% 

Percent Intrazonal – HBSC 10% 12% 

Percent Intrazonal – HBO a 3% 7% 

Percent Intrazonal – NHB b 5% 9% 

Percent Intrazonal – Total Trips 3% 5% 


Statistic Acceptable Preferable 

Percent Intrazonal, Observed Total Trips +/-5% +/-3% 



2.3.4 Mode Choice 

Aggregate mode choice validation is performed by applying the estimated (or transferred) 

model and comparing the results to observed demand by mode and trip purpose. The 

application should be performed using the appropriate mode choice program (e.g., 

Generalized Nested Logit, Cube/Voyager scripting, etc.). Validation of mode choice 

models is largely based on attempting to achieve targets of trips by different travel modes. 

These targets can be estimated from household and on-board travel surveys, NHTS, and 

Census JTW data for HBW trips. The most important data source is transit boarding 

counts although an on-board survey is needed to split total transit trips into purposes and 

access modes. Validation standards on estimated versus observed transit trips also have 

been identified, along with a range of acceptable settings for key mode choice model 

parameters. 




Percent Mode Split Targets 

The ability to calibrate, or even properly validate, a mode choice model depends on the 

presence of survey data, preferably in the form of a large sample household survey but, at 

a minimum, a recent transit on-board survey. To perform aggregate mode choice model 

validation, it is necessary to develop a calibration target matrix. This matrix will include 

values representing the estimated demand by mode and trip purpose for appropriate 

market segments. For FSUTMS, it is recommended that the target matrix consider the 

following dimensions: 

• All trip purposes; 

• Mode (highway, transit, and nonmotorized modes that exist in the base year model); 

• Auto ownership level (0, 1, 2, and 3+); and 

• Geographic subarea, if appropriate. 

It is critical to get the best estimates possible for the calibration targets. The transit mode 

targets are estimated so that the total observed transit ridership for the base year is 

matched. Since ridership information from the transit operators will not be segmented by 

trip purpose, vehicle ownership, or access mode, the total ridership is split into the necessary 

market segmentation using data from a transit on-board survey. In addition, it is 

essential to adjust the targets to reflect that the ridership counts are unlinked trips (i.e., 

trips with transfers are counted twice), while mode choice model outputs are linked trips. 

Information on transfer rates required for these adjustments by market segment also can 

be obtained from the on-board survey data. 

Generally, the shares for highway (and nonmotorized, where applicable) modes by trip 

purpose are obtained from a local household travel survey. These are adjusted so that 

when the shares are applied to the total trip tables that are outputs from the trip distribution 

model, the transit ridership targets are maintained. 

It is important to recognize that it is very likely that the development of a completely 

enumerated, statistically significant set of targets for all modes and market segments is 

impossible due to lack of data. This means that market segments may need to be aggregated 

for calibration. For example, it may be possible to validate all transit trips by auto 

ownership level, or transit trips by submode by auto ownership level, but not transit trips 

by submode by auto ownership level. 

Table 2.6 shows part of a sample target matrix for HBW trips (the numbers shown are 

purely examples and do not reflect real numbers from any region). Additional geographic 

market segmentation may be considered if sufficient data to estimate observed demand 

exists. 




Table 2.6 

Example Trip Validation Targets for HBW Trips 

Mode 

Zero-Vehicle 

Households 

One-Vehicle 

Households 

Two-Vehicle 

Households 

Three-Vehicle 

Households 

Walk 5,000 6,000 4,000 3,000 

Bike 2,000 1,000 500 200 

Drive Alone – 130,000 350,000 200,000 

Shared Ride 2 Persons 6,000 15,000 20,000 10,000 

Shared Ride 3 Persons 1,000 2,000 4,000 2,000 

Local Bus, Walk 6,000 7,000 4,000 1,000 

Local Bus, PNR – 500 

Local Bus, KNR – 200 

2,000 500 

Express Bus, Walk 1,000 1,000 1,000 500 

Express Bus, PNR – 2,000 4,000 2,000 

Express Bus, KNR – 200 500 

LRT, Walk 500 1,000 400 

LRT, PNR 

LRT, KNR 

– 300 500 

Table 2.7 provides a set of mode choice calibration standards. Consideration was given to 

including a range of mode choice targets, including percent of trips categorized by drive 

alone auto, shared ride auto, and percent transit. It would be preferable instead for small 

areas to compare against a comparable area with valid targets. Large areas should 

develop targets based on surveys. Acceptable ranges for elasticity of demand with respect 

to LOS variables are based on a few data points from U.S. urban areas, the Simpson-Curtin 

rule, and professional judgment. Relatively little information is available for this check. 

Table 2.7 

Mode Choice Validation Standards and Benchmarks 


Statistic Preferable Acceptable 

Total Area Transit Trips versus Observed +/-1% +/- 2% 

Transit Trips between Districts 

compare model trip table against CTPP or HH survey 

Mean Trip Length Transit Trips versus Observed +/-5% +/-15% 

Mode Splits by Observed Calibration Targets +/- 2% +/- 2% 

In addition to survey-based data, transit ridership should be normalized to average weekday 

conditions. This is a challenge in itself as transit agencies typically report only 

weekly, monthly, and annual ridership figures. For the purposes of mode choice validation, 

observed transit ridership figures also need to be adjusted from unlinked to linked 

trips. Linked trips reflect an entire transit trip from origin to destination whereas 




unlinked transit trips represent boardings on each transit route (i.e., a transfer from one 

route to another would equal two boardings). 

Another aggregate validation check, for HBW trips, is the comparison of model results to 

data from the U.S. Census Transportation Planning Package (CTPP). This Census data set 

is based on a much larger sample than household survey data sets and is completely 

independent of the model estimation process. However, it is important to recognize that 

the definition of work trips in the CTPP is not identical to that in a travel model, and so a 

very close match may not be possible or advisable. Estimated HBW trips by mode 

between districts (i.e., groupings of TAZs) should be compared against trip tables from 

the CTPP or a regional household survey, if available. HBNW and NHB trip movement 

checks require a survey with a significant sample of trips to compare against the model. 

Supplemental Mode Choice Parameter Settings 

While the Cube-Voyager Model Parameters Technical Report includes recommendations on 

mode choice coefficients and constants, a few other parameters are defined here with 

acceptable ranges of settings derived from the literature review and recent mode choice 

calibration efforts. These parameters are important to the calibration and validation process 

as the FTA reviews assumptions in certifying transit forecasts for New Starts funding. 

Table 2.8 depicts acceptable ranges of values for elasticity of demand with respect to LOS 

variables; in-vehicle time parameters; ratios between in-vehicle and out-of-vehicle times 

by trip purpose; and the implied value of time by dollar, purpose, and percent of income. 

Acceptable ranges are based on review of mode choice models from thirteen cities with 

largely unconstrained mode choice model coefficients. 

Table 2.8 

Supplemental Mode Choice Parameter Settings 

Acceptable Range of Values 


Elasticity of Demand with Respect to LOS Variables -0.10 -0.70 

Acceptable 

Preferable 

IVT Parameter – HBW -0.01 -0.05 

IVT Parameter – HBNW -0.007 -0.033 

IVT Parameter – NHB -0.01 -0.05 

Ratio: OVT/IVT Parameters 2 3 

Ratio: OVT/IVT Parameters – HBW 1.5 3 

Ratio: OVT/IVT Parameters – HBNW 2 6 

Ratio: OVT/IVT Parameters – NHB 2 7 

Implied Value of Time – Percent of Income 25% 33% 

Implied Value of Time – HBW $2.00 $5.00 

Implied Value of Time – HBNW $0.50 $5.00 

Implied Value of Time – NHB $0.20 $5.00 




Additional guidance on model choice calibration is provided in Section 2.4 of this report, 

under the discussion on FTA New Starts projects. 

2.3.5 Trip Assignment 

Highway assignment is generally where most validation checking is focused, typified by 

screenline checks, and regional RMSE and VMT. A large number of models do not check 

time-of-day traffic estimates in spite of having time-of-day components while speeds and 

forecast year volumes are not usually checked. Transit assignment checks have been typically 

completed only at the regional level or using groupings of routes by corridor or 

subarea. Unlinked trips have sometimes erroneously been compared to transit boarding 

counts at the regional rather than route level. Sensitivity analysis has not historically been 

completed nor have been forecast year checks on transit ridership. 

It is within this context that draft guidelines and standards for highway and transit 

assignment are discussed and recommended. Certainly, more model validation standards 

and benchmarks are available for trip assignment than any of the other components of the 

four-step modeling process. Discussion topics in this section of the report include percent 

error and volume-over-count ratios for screenlines, vehicle-miles-traveled (VMT), and 

vehicle-hours traveled (VHT); other aggregate VMT statistics; root mean square error 

(RMSE); and transit assignment statistics. 

Volume-Over-Count Ratios and Percent Error 

Some of the most commonly reported model validation statistics are volume-over-count 

ratios and percent error of volumes versus counts. Volume-over-count ratios can be 

summarized by area type, facility type, and lanes; daily or peak-periods; screenlines, cut 

lines, and cordon lines; and using estimates based on VMT and VHT calculations. The 

percent error is typically summarized by volume group, using percents rather than ratios. 

While volume-over-count and percent error are commonly reported validation statistics, 

there is potential for double-counting the same trips when summing model results at the 

regional level. The summing of link volumes does have a greater potential for double 

counting than summing of VMT, thus emphasizing the importance of using caution in 

coding traffic counts and splitting network links. Calculation and visual display of volume-over-count 

ratios on a link-by-link basis is generally preferable to regional summation. 

Even VMT is not without a potential for error due to the influence of link length. 

Inconsistencies between volume-over-count and VMT ratios might indicate a problem 

with link length. Caution should also be exercised when summing volume-over-count 

ratios by VHT unless observed speed data were available and used to estimate model 

speeds. 

Table 2.9 depicts recommended validation standards for volume-over-count ratios 

summed by category (e.g., facility type). Being standards rather than benchmarks, these 

statistics are more based on established guidance documents rather than readily available 

model validation reports. While nearly all highway assignment accuracy standards reflect 




daily traffic estimates, volume-over-count ratios have also been documented for peakperiod 

time-of-day assignments, establishing a percent of links that should be at several 

varying accuracy levels based on generalized facility types. A higher level of statistical 

validity is generally recommended for higher order facilities. 

Table 2.9 

Volume-Over-Count Ratios and Percent Error 



Freeway Volume-over-Count (FT1x, FT8x, FT9x) +/- 7% +/- 6% 

Divided Arterial Volume-over-Count (FT2x) +/- 15% +/- 10% 

Undivided Arterial Volume-over-Count (FT3x) +/- 15% +/- 10% 

Collector Volume-over-Count (FT4x) +/- 25% +/- 20% 

One way/Frontage Road Volume-over-Count (FT6x) +/- 25% +/- 20% 

Freeway Peak Volume-over-Count 75% of links @ +/-20%; 50% of links @ +/-10% 

Major Arterial Peak Volume-over-Count 75% of links @ +/-30%; 50% of links @ +/-15% 

Assigned VMT-over-Count Areawide +/-5% +/-2% 

Assigned VHT-over-Count Areawide +/-5% +/-2% 

Assigned VMT-over-Count by FT/AT/NL +/- 25% +/- 15% 

Assigned VHT-over-Count by FT/AT/NL +/- 25% +/- 15% 

Florida accuracy standards along screenlines and cutlines have historically varied from 

+/-5 percent to +/-20 percent. Volume ranges and standards were adjusted as follows: 

• External model cordon lines should achieve +/- 1 percent; 

• Screenlines with greater than 70,000 AADT should achieve +/-10 percent; 

• Screenlines with 35,000 to 70,000 AADT should achieve +/-15 percent; and 

• Screenlines with less than 35,000 AADT should achieve +/-20 percent. 

The set of links with counts that are not located on screenlines (often coded as screenline 

“99” in FSUTMS models) should achieve +/- 5 percent; however, the potential for double 

counting among unrelated links is much greater than for screenlines or cordons. 

The literature review did not indicate that many areas outside Florida use screenline volume 

to determine accuracy ranges. At the same time, available literature reported an 

accuracy range of +/- 5 percent to +/-20 percent for highway screenlines with an average 

of +/-15 percent for cutlines. Even though FHWA standards are even looser for low volume 

screenlines, these same accuracy levels are recommended for use in the future, along 

with somewhat more flexibility in terms of volume range thresholds. Proper designation 

of screenlines, cutlines, and cordon lines will avoid the pitfalls of double-counting trips 

when summing volume-over-count ratios. 




Percent error has historically reflected a “plus or minus one lane” criteria in Florida. This 

concept means that highway assignment accuracy should minimize incorrect future 

laneage calls resulting from forecasted traffic. With most available error percentages 

being based on 1965 and 1985 Highway Capacity Manual (HCM) assumptions, new error 

percentages were calculated for this study based on 2007 Updates to Florida DOT’s Level 

of Service Handbook. Appendix G depicts a spreadsheet used during preparation of this 

report to iteratively adjust and calculate error percentages for volume groups such that 

most errors would minimize incorrect lane call estimates. In addition to modifying the 

percent error, the volumes contained in each group also were adjusted to better synchronize 

with daily volumes contained in the Level of Service Handbook. Table 2.10 depicts a 

range of accepted and preferable accuracy ranges for five volume groups. A desired percentage 

of links with counts in each volume group will be identified in follow up FDOTsponsored 

studies. 

Table 2.10 Revised Percent Error by Volume Group 



Percent Error: LT 10,000 volume (2L road) 50% 25% 

Percent Error: 10,000-30,000 (4L road) 30% 20% 

Percent Error: 30,000-50,000 (6L road) 25% 15% 

Percent Error: 50,000-65,000 (4-6L freeway) 20% 10% 

Percent Error: 65,000-75,000 (6L freeway) 15% 5% 

Percent Error: GT 75,000 (8+L freeway) 10% 5% 

Aggregate VMT Statistics 

In addition to accuracy standards for VMT volume-over-count ratios, the literature review 

uncovered other validation benchmarks that use VMT. The Quick Response Freight Manual 

identified an approximate percent VMT of 11 percent for trucks in a typical urbanized 

area, although this rate will vary considerably by region. Accounting for Commercial 

Vehicles in Urban Transportation Models documented a commercial vehicle range of 3 to 

25 percent of total VMT (11.79 percent average). Commercial vehicles were further 

divided into three categorical groupings for additional VMT summaries: 

• Movement of People – School buses, fixed shuttle services, taxis, paratransit, and 

rental cars (1-5 percent of total VMT; 2.4 percent average); 

• Movement of Goods – Package and mail delivery, warehouse delivery, construction 

transport (1-7 percent of total VMT; 3.5 percent average); and 

• Services – Safety vehicles, utility vehicles, public service vehicles, and business/ 

personal services (1-13 percent of total VMT; 5.9 percent average). 




Several documents have estimated typical ranges of VMT per HH and person. VMT per 

HH typically ranges from 60 to 75 while VMT per person is generally 24 to 32, with 

smaller areas at the low end and larger areas at the high end of each range. Finally, ratios 

between model estimated VMT and VMT calculated using the Highway Performance 

Monitoring System (HPMS) have been used in air quality analyses to correct for regional 

model-based VMT estimates. 

Root Mean Square Error (RMSE) 

RMSE is among the most commonly reported statistics in model validation. RMSE, a 

measure of dispersion, tends to normalize model error better than volume-over-count 

ratios that allow for high ratios to offset low ratios. The formula for calculating RMSE is 

as follows: 

( 

% RMSE = 

∑ 

j 

( Model 

j 

− Count ) 

( 

∑ 

j 

j 

Count 

j 

/( NumberofCounts −1)) 

/ NumberofCounts) 

FSUTMS Cube-Voyager scripts are readily available to calculate RMSE by volume group. 

Historically, the FSUTMS RMSE program has grouped model results into 12 directional 

volume groups, which are considerably more categories than found in most model guidance 

documents. Table 2.11 lists accepted and preferable RMSE accuracy standards for a 

reduced number of eight categories, reflecting a variety of documents included in the literature 

review (Appendix A). For overall RMSE, there is a wide variation in acceptability 

throughout the U.S. with most documents recommending values of 30 to 40, and several 

accepting as high as 50 percent areawide RMSE, as reflected in Table 2.11. A minimum 

number of sampled links should be established to consider RMSE as valid for specific volume 

groups. Consideration should also be given to summarizing RMSE by screenline. In 

addition to RMSE, dispersion can also be assessed visually by preparing scatter plots of 

model estimated volumes versus counts. 

2 

0.5 

*100 

Table 2.11 Root Mean Square Error (RMSE) 



RMSE: LT 5,000 VPD 100% 45% 

RMSE: 5,000-9,999 VPD 45% 35% 

RMSE: 10,000-14,999 VPD 35% 27% 

RMSE: 15,000-19,999 VPD 30% 25% 

RMSE: 20,000-29,999 VPD 27% 15% 

RMSE: 30,000-49,999 VPD 25% 15% 

RMSE: 50,000-59,999 VPD 20% 10% 

RMSE: 60,000+ VPD 19% 10% 

RMSE Areawide 45% 35% 




Transit Assignment Validation 

Not nearly as much effort is typically expended on transit assignment validation as is 

devoted to highway assignment validation, in part due to transit assignment not impacting 

roadway congestion or capacity, as well as a general lack of postprocessing programs and 

scripts to summarize transit assignment accuracy. The dominance of automobile traffic in 

many regions and lack of data on observed boardings have also contributed to transit 

assignment being generally ignored during model validation. 

Table 2.12 depicts several benchmarks and accuracy standards, including estimated-overobserved 

transit trips, acceptable error on transit screenlines, and acceptable percent error 

in transit system ridership by passengers per day categories. Obviously, this first statistic 

requires that boarding data be available. Implementation of transit screenlines and percent 

error by ridership group will require a methodology to estimate daily and peak 

period transit “counts” (i.e., ridership by segment) as transit ridership statistics are typically 

compiled by month and year rather than for a typical weekday. Consideration could 

be given to establishing RMSE targets for measuring transit assignment dispersion; however, 

targets would vary greatly by region. 

Table 2.12 Transit Assignment Validation 

Benchmarks 


Estimated-over-Observed Transit Trips +/- 9% +/- 3% 



Acceptable Error – Transit Screenlines +/-20% +/-10% 

Transit Ridership: 20,000 Passengers/Day +/- 20% +/- 15% 

• 2.4 Other Model Applications 

The previous section described validation procedures, checks, benchmarks, and standards 

that should be employed when developing an MPO or regional travel demand model for 

LRTP Updates if transit is present in the model. The majority of these same checks should 

be conducted during validation of models for use in other studies. This section of the 




report describes additional and different model validation procedures, checks, benchmarks, 

and standards that should be applied when conducting other study types. The 

study types described in this section include the following: 

• LRTP “highway only” models; 

• Transit studies desiring FTA New Starts funding; 

• Subarea transportation studies; 

• Highway corridor studies (e.g., feasibility studies, project development and environmental, 

interstate master plans, interchange justification reports, etc.); and 

• Developments of Regional Impact (DRIs) and other traffic impact studies. 

Appendix F, Tables F.2 through F.6, depicts validation checklists for each of the above 

model application types. Bold font is used to identify the highest priority model checks. 

2.4.1 LRTP Highway Only Models 

LRTP “highway only” models should include the majority of model checks, accuracy 

standards, and benchmark comparisons listed previously under Section 2.3 of this report; 

however, since these models do not include transit networks, paths, and logit mode choice 

any transit-related checks listed earlier will not apply. In the absence of logit mode choice 

models, auto occupancy rates become the primary adjustment tool available for converting 

person trips to auto trips. The Cube-Voyager Model Parameters Technical Report suggested 

auto occupancy rates for use in validation of highway only models based on results 

from the NHTS. Table 2.13 provides a summary of suggested auto occupancy rates from 

the NHTS as well as NCHRP 365. HBW auto occupancy rates used in the model should be 

compared against regional CTPP Journey-to-Work (JTW) statistics for reasonability. 

Table 2.13 Suggested Auto Occupancy Rates 

Benchmarks/Settings 


Auto Occupancy Rates – HBW 1.05 1.10 

Auto Occupancy Rates – HBSH 1.50 1.80 

Auto Occupancy Rates – HBSR 1.70 1.90 

Auto Occupancy Rates – HBO a 1.65 1.95 

Auto Occupancy Rates – NHB b 1.60 1.90 






2.4.2 FTA New Starts Models 

While nearly all medium-to-large MPO models in Florida include transit network, skim, 

logit mode choice, and transit assignment components, the needs for model validation 

during an LRTP are not necessarily as project-specific as efforts conducted in anticipation 

of New Starts financing. To accurately and fairly rate projects for New Starts funding, the 

FTA has established a set of modeling procedures and guidelines. 

While there is no document produced by FTA that includes all of the guidelines and 

requirements for New Starts ridership forecasts, FTA has held two workshops on “Travel 

Forecasting for New Starts Proposals.” As referenced in Appendix A (Bibliography), 

materials from these workshops, held in June 2006 and September 2007, are posted on 

FTA’s web site and include information on these topics. 

FTA’s guidance suggests that there must be a reasonable and valid interpretation of the 

“story told by the model” about traveler behavior. This relates to transit network coding 

and path building as well as the mode choice models. This helps to ensure that the various 

parameters, constants, network coding conventions, and other decision rules in the 

models “tell a coherent story” about travel behavior. 

Since the New Starts program focuses on project evaluation, it is also necessary to demonstrate 

that future changes in the transportation (especially transit) system produce reasonable 

model results. Tests of the sensitivity to changes must be done through model 

application in full production mode. Simple elasticity tests are insufficient because they 

do not exercise the full range of model components, particularly network coding conventions 

and transit path building parameters that are central to the transit-related properties 

of a model set. 

The main FTA requirement regarding data collection for model development is to have 

“transit rider data for model testing.” This implies a rider survey, usually conducted onboard 

transit vehicles that provides information on transit travel patterns, markets, and 

trip purposes. Indeed, it is difficult to perform validation for mode choice in an area with 

significant transit service without such a survey. This type of survey allows the development 

of validation targets as shown previously in Table 2.6 as well as information on transit 

transfers and other observed data needed to test for the standards shown previously in 

Table 2.7. 

Table 2.14 describes more stringent FTA accuracy standards for mode choice model 

parameters as compared to earlier statistics provided in Table 2.13. While these standards 

are not labeled as requirements by FTA, it is best to stay within these guidelines for models 

used in New Starts forecasting. 




Table 2.14 Mode Choice Parameter Settings for FTA New Starts 



Elasticity of demand with respect to LOS variables -0.10 -0.70 

IVT parameter – HBW a -0.02 -0.03 

IVT parameter – HBNW a 0.1 to 0.5*CIVT HBW trips 0.1 to 0.5*CIVT HBW trips 

IVT parameter – NHB a ~CIVT HBW trips ~CIVT HBW trips 

Ratio: OVT/IVT parameters – HBW a 2.0 3.0 

Ratio: OVT/IVT parameters – HBNW a 2.0 3.0 

Ratio: OVT/IVT parameters – NHB a 2.0 3.0 

Implied value of time – Percent of income 25% 33% 

Implied value of time – HBW $2.00 $7.00 

Implied value of time – HBNW $0.50 $5.00 

Implied value of time – NHB $0.20 $5.00 

a FTA published guideline. 

FTA guidelines are not presently available for the implied value of time (VOT) but an 

example from a 2006 FTA-sponsored workshop shows the same VOT for all trip purposes. 

The ranges are dependent on the analysis year, are based on information from the 1980s 

and 1990s, and should be updated to account for inflation. The following discussion on 

model calibration procedures could apply to calibration efforts for models used in forecasting 

transit use but is especially relevant for FTA New Starts projects. 

Transit Path Building 

FTA has noted that certain common practices in transit path building can have undesired 

impacts on ridership forecasts. Minimum and maximum values of time and distance used 

to determine valid transit paths and modal availability can have unexpected effects. It is 

recommended to use continuous functions instead of such “either/or” tests. It also is 

important that transit access coding conventions are consistent among transit modes. Path 

building parameters and settings should remain the same for all steps of the model 

(skimming, assignment). 

FTA recommends evaluating the transit skims by comparing the skim settings to the range 

of experience in on-board surveys. Settings to check include maximum access distances, 

travel times, and transfers. Another FTA recommendation is the assignment of 

“observed” transit trip tables, derived from the expanded transit rider survey, to the 

coded transit networks. This will provide an opportunity to examine transit network and 

path building without the influence of errors in the trip distribution and mode choice 

models. 




Trip Distribution Model Checks 

FTA has found that many of the issues with ridership forecasts result not from errors in 

mode choice models but from incorrect transit travel patterns, which are products of the 

mode choice models. Consequently, validation of trip distribution is a critical component 

of validation for models used in New Starts ridership forecasting. Validation must 

include a careful comparison of highway and transit travel times used as inputs to trip 

distribution and mode choice. This is a reasonableness check, with no specific standards. 

While FTA clearly wants forecasters to look at trip distribution, no specific accuracy standards 

have been recommended. 

FTA recommends a detailed inspection of the person-trip tables that are the outputs of trip 

distribution. Checking trip length frequency distributions is insufficient. Since information 

on observed travel patterns is seldom available at a zone level, this must be a district 

level summary. The motivation behind this recommendation is that if demand in a corridor 

is significantly overestimated or underestimated, it will be difficult to produce accurate 

ridership forecasts for a proposed transit project in the corridor. The implication is 

that recent household survey data are needed to perform this comparison. In the absence 

of household survey data, comparisons should at least be made between CTPP/JTW data 

and model-estimated home-based work trip tables at the planning district or sector level. 

FTA has not specified any standards for this check either. 

Mode Choice Model Calibration Procedures 

After the mode choice model is applied, the results by market segment are compared to a 

calibration target matrix, as described earlier in Section 2.3. Aggregate model calibration 

and validation ensure agreement between the estimated and observed data at the aggregate 

level through the adjustment of mode-specific constants. The primary role of the 

constants is to capture the effects of those variables affecting mode choice that cannot be 

modeled, such as safety, security, and reliability. Constants are included to “explain” 

which existing specifications of the model (i.e., model structure, variables, and coefficients) 

cannot be addressed adequately. The concerns with the use of constant terms, in 

lieu of explanatory variables, lies in the application of the model in the forecasting mode, 

since changes in variables affecting modal use, but not included in the model, are held 

constant over time. The ideal situation is a robust model with a strong explanatory power 

and constants that are of relatively small magnitude. It is not acceptable simply to adjust 

constants without consideration to the reasons for the differences between model results 

and observed data. When large adjustments are needed, this usually indicates problems 

with the model that need to be corrected before validation can continue. 

It is important to recognize the relationship between the magnitudes of alternative-specific 

constants and the other model parameters. For example, if the difference between the 

constants for two modes is 3.0, and the in-vehicle time coefficient is -0.02, this implies that 

(all other things being equal), a traveler would be indifferent between spending 30 minutes 

on the mode with the lower constant and spending three hours on the higherconstant 

mode. This may be reasonable if the higher-constant mode is an auto mode and 

the lower constant mode is a transit or nonmotorized mode, when issues such as vehicle 




availability, parking availability, and transit/nonmotorized mode captivity are not explicitly 

considered in the model. (Then again, this may indicate that the ways in which these 

issues are treated in the model need to be reconsidered.) However, if the two modes are 

either transit modes or both auto modes, it is likely there are other issues in the model that 

need to be corrected. In the case of two transit modes, it is likely that FTA would deem 

this difference to be a case of “bizarre” alternative-specific constants. 

One of the most significant problems that may occur in traditional model development is 

a calibration effort that results in adjustments necessary to match current data that are no 

more than correction factors for errors made elsewhere in the model set. The “calibration” 

of alternative-specific constants is meaningful only when the person-trip tables, highway 

and transit networks, and observed patterns are sufficiently accurate. 

To summarize, the initial response to the identification of discrepancies between the 

model results and the calibration targets is to examine the potential reasons for the discrepancies 

in the model itself and to correct any model problems that are identified. After 

all such issues have been addressed; it would be acceptable to make relatively small 

adjustments to modal constants to provide a better fit between modeled and observed 

mode shares. 

FTA has noted that simply matching regional targets by mode is insufficient. Besides segmentation 

by trip purpose, socioeconomic class (such as auto ownership level), and transit 

access mode/submode, checks for individual geographic markets must also be performed. 

FTA asks, “Do our models grasp adequately the characteristics of our key transit ridership 

markets?” FTA contends that a model is not sufficiently validated unless it accurately 

represents transit demand in key markets (this requires good validation of both trip distribution 

and mode choice). 

Recommended FTA Transit Assignment Checks 

As described in Section 2.3, the transit assignment process is often overlooked during the 

process of regional model and LRTP development. FTA has recommended specific checks 

on the transit assignment process for projects requiring New Starts funding. The first of 

these, as described above, is to assign a trip table from an expanded on-board survey and 

compare the results against a model estimated transit assignment. Checks should be conducted 

on individual transit lines (or groups of lines in the case of local buses), guideway 

facilities, stations, and park-and-ride lots, and between station pairs if the data are 

available. 

FTA specifically recommends performing modeling for future baseline (“TSM” in the case 

of New Starts projects) and build alternatives. Future baseline results should be compared 

to base year results, and future build results should be compared to future baseline 

results. This is a reasonableness check. 

FTA developed a software tool, known as SUMMIT, for analyzing travel demand forecasts. 

SUMMIT also computes and reports transportation system user benefits which can 

be used in mobility and cost effectiveness measures for New Starts reporting. SUMMIT 




requires software changes to regional travel forecasting models to export files required by 

SUMMIT for the calculation of user benefits. FTA recommends using the SUMMIT program 

as a diagnostic check for unusual or anomalous transit assignment results. SUMMIT 

analyses are performed based on comparisons between future baseline and future build 

results. 

2.4.3 Subarea Models 

Subarea transportation studies are becoming increasingly popular in addressing growth 

management issues at the local level including Transportation Concurrency Exception 

Areas (TCEAs), Local Government Comprehensive Plans (LGCPs), Special Area Plans 

(SAPs), campus master plans, sector plans, downtown master plans, proportionate share, 

and impact fees. Subarea transportation models often include splitting of the regional 

model TAZs, reevaluating base year and future year socioeconomic estimates, and adding 

streets to the model network that are important for local traffic circulation but not necessarily 

needed at the regional level. 

Validation of the regional transportation model should be completed and approved for 

use by the Florida DOT and the local MPO prior to developing a subarea model. Not all 

model validation checks required for LRTPs and FTA New Starts projects are needed at 

the subarea level as some of these would potentially be redundant. The subarea should be 

defined within the model by designating districts and sectors to summarize TAZ and 

network information for the subarea. Some statistics should be compared between the 

subarea and regional level to ensure the subarea model validation does not disrupt 

regional model accuracy should the subarea model be used later for other purposes. A 

sample of validation measures to compare between subarea and regional levels may 

include the following: 

• Input Data – This is a primary focus of validating a subarea model and should include 

review of socioeconomic data and highway and transit networks; 

• Trip Generation – Review and comparison of subarea against the regional model 

based on aggregate trip rates (e.g., trips/person, trips/DU, HBW trips/employee); 

• Trip Distribution – Comparisons on average trip length and percent intrazonal trips 

by purpose; 

• Mode Choice – If the subarea includes transit access, check the mode shares within the 

subarea against local data or use professional judgment; and 

• Trip Assignment – Highway validation checks on volume-over-count, VMT-overcount, 

VHT-over-count, screenline volume-over-count, RMSE, and percent error. 

It might be desirable to add cutlines or modify screenlines to better assess trip patterns 

into, out of, and through the subarea. If the subarea has major freight generators, some 

amount of checking on percent trucks or truck VMT should also be conducted. 




2.4.4 Corridor Models 

There are many different types of corridor studies that require travel demand forecasts. In 

Florida, these include Environmental Impact Statements (EIS) and Environmental 

Assessments (EA), Alternatives Analysis (AA), Project Development and Environmental 

(PD&E) studies, final design studies, Interstate Master Plans, corridor action plans, 

Interchange Justification/Modification Reports (IJR/IMR), corridor feasibility studies, and 

traffic and revenue studies. 

Dependent on the length of a corridor, the focus of corridor validation will likely be on a 

smaller geographic scale than subarea studies. Many of the same validation checks for 

subareas apply to corridor studies, with some checks removed and a few added. Corridor 

validation is typically focused on network details surrounding the corridor being studied. 

This includes additional review of zone centroids and connectors and network characteristics 

in areas surrounding the corridor. Checking of regional or subregional statistics on 

persons per dwelling unit (DU), employment per population, and autos per DU should 

not be necessary for corridor validation. 

Beyond data verification, validation checks by model step should include the following (in 

addition to those already described for subarea studies): 

• Trip Generation – Review and comparison of corridor study area trip productions 

and attractions by zone; 

• Trip Distribution – Trip table desire line analyses; 

• Mode Choice – Unless the corridor represents an FTA New Starts project, a simple 

review of mode shares within the study area should suffice; and 

• Trip Assignment – Highway validation checks same as subarea studies with more 

stringent accuracy standards for corridor volume-over-count ratios and screenlines. 

Thematic GIS mapping of trip productions and attractions within corridors can be used to 

identify any potential localized socioeconomic data corrections or modifications. For long 

corridors, desire lines can be used to check the reasonableness of zone-to-zone or districtto-district 

trip movements. Future year sensitivity testing of hypothetical or planned 

alternative corridor strategies should also be accomplished to determine if additional 

validation adjustments are necessary. Table 2.15 depicts summed link volume-over-count 

accuracy standards for validation by facility type within a corridor study area. 




Table 2.15 Volume-Over-Count Ratios for Corridor Validation 



Freeway Volume-over-Count (FT1x, FT8x, FT9x) +/- 6% +/- 5% 

Divided Arterial Volume-over-Count (FT2x) +/- 10% +/- 7% 

Undivided Arterial Volume-over-Count (FT3x) +/- 10% +/- 7% 

Collector Volume-over-Count (FT4x) +/- 15% +/- 10% 

One way/Frontage Rd Volume-over-Count (FT6x) +/- 20% +/- 15% 

Accuracy standards along screenlines and cutlines within the corridor study area should 

be more stringent for corridor validation, as follows: 

• External model cordon lines crossing corridor should achieve +/- 0 percent; 

• Screenlines with greater than 70,000 AADT should achieve +/-5 percent; 

• Screenlines with 35,000 to 70,000 AADT should achieve +/-10 percent; and 

• Screenlines with less than 35,000 AADT should achieve +/-15 percent. 

2.4.5 Models for DRIs and other Traffic Impact Studies 

Among the most common uses of models in Florida is for DRIs and other traffic impact 

studies, including concurrency applications and impact fee assessments. Unless substantial 

changes are made to the regional model, such as model expansion, there should not be 

a need to review regional statistics. Model checks should be limited to the estimated 

impact area for the development but should include review of future year data assumptions. 

Data checks should include the following: 

• Socioeconomic Data – Assumptions used for internal (site) and surrounding zones; 

check population per TAZ to identify the need for zone splits; 

• Highway Network Data – Verify that the network is coded correctly and that the only 

committed projects included are those anticipated for construction with three years; 

conduct path traces within the impact area to assess potential routing of site-generated 

trips; and 

• Transit Network Data (If Site Is Accessible by Transit) – Check access coding, headways, 

stop locations, etc., for routes providing access to the site. 




Model checks should include the following: 

• Trip Generation – Document any assumptions used in converting ITE vehicle trips to 

person trips by purpose for the site; check person trips/TAZ for zone splitting; 

• Trip Distribution – Conduct district-to-district summaries of trips that begin or end at 

the site and review resulting distribution patterns for logic; 

• Mode Choice – Confirm consistency between ITE trip generation and resulting vehicle 

trips; and 

• Trip Assignment – Network links surrounding the site should meet the same accuracy 

standards recommended for subarea studies; reasonableness checks should be conducted 

using select zone and select link techniques for site-generated trips; turn volumes 

near the site should be compared against available data. 

The result is a combination of base year validation checks and sensitivity checks on how 

the model performs once site-generated trips are added to the model. 

• 2.5 Summary and Other Findings 

Section 2.0 of this Final Report has provided draft recommended calibration and validation 

standards, benchmarks, settings, and procedures derived from a literature review of 

model guidelines and standards from around the U.S. along with professional experience 

and training workshops presented by the authors of this document. An overview of the 

validation process was provided along with general guidance on improving model validation. 

Guidelines and standards were described for data checking, trip generation, trip 

distribution, mode choice, and trip assignment. These recommendations were provided 

for different modeling efforts including LRTPs, FTA New Starts, subarea plans, corridor 

studies, and traffic impact studies. This report concludes with some general state-of-thepractice 

findings and additional guidance. 

2.5.1 State-of-the-Practice Findings 

The guidelines and standards recommended in this report are based in part on an assessment 

of validation practices used throughout the country. Some general findings from the 

assessment of calibration and validation processes and results from across the U.S. were 

described earlier in Section 1.3. 




2.5.2 Additional Guidance 

Discussions with members of the Florida Model Task Force (MTF) on preliminary findings 

of this project led to the recommendation of a subsequent study on establishing traffic 

count needs and guidelines for model validation. The study on traffic count needs should 

include criteria for traffic count needs, criteria for breaking network links to minimize 

count duplication, normalizing traffic counts to improve accuracy, and identifying 

potential sources of count error. 




3.0 Calibration and Validation 

Best Practices 


The process of model calibration and validation is vital to producing defensible travel 

demand forecasts. It is best practice to validate every step of the model chain. Unfortunately, 

far too many model validation efforts are predominantly focused on highway 

assignment statistics with minimal effort expended on validating trip generation, trip distribution, 

and mode choice models. Since a variety of validation techniques have been 

employed in Florida over the years, it is important to distinguish defensible adjustments 

from questionable modifications. Likewise, guidance is needed to identify proper increments 

of adjustment that will result in reasonable travel demand forecasts. 

As this report is written, there is currently a great deal of interest in improving the model 

validation process throughout the United States. It has been the subject of discussion at 

the Transportation Research Board, on the Travel Model Improvement Program e-mail 

exchange 1 , and at Federal agencies, where the Federal Highway Administration has 

recently sponsored a Peer Exchange on Model Validation. 

Some of the discussion has focused on the concept that practitioners should not fall prey 

to an over-reliance on questionable model adjustments to achieve documented model 

accuracy standards. Some would contend that if a model includes special adjustment 

factors, validation checks without these factors in place are needed to determine how significant 

these factors are to the final model outputs and possible reasons why the model 

output is inconsistent with observations. The risk over overfitting by proliferating 

adjustment factors to “blow away” a target goodness of fit statistic must be taken into 

account. Even some experienced modelers simply assume that the best technique is the 

one that comes closest to replicating the ground counts. It can be argued that a perfect 

model should have some mismatch to ground counts since a base year calibration should 

never validate closer than the error in the ground counts, which can be appreciable. 

Regarding accuracy standards, arbitrary targets may be neither necessary nor sufficient 

since statistics may vary with context, model dependent variables, model explanatory 

values, and the type of data. “There is no numerical standard (outside of controlled 

experimental data) that should be used in lieu of expert judgment and careful review.” 

This section first describes a recommended step-by-step process, including model estimation, 

development, calibration, validation, and application. This is followed by a discussion 

of each step in the model chain and actions to be taken in response to a variety of 




model validation and calibration errors. Guidance on model validation checks are provided 

for a variety of study types, building off checklists described earlier in Section 2.0. 

Section 3.0 also touches on emerging validation issues, transferable parameters, and 

shifting paradigms in modeling. 

Appendix H is a bibliography of references used in preparing Section 3.0. Appendix I 

provides a recommended worksheet for use in validation efforts. This worksheet, also 

available in spreadsheet form, should be filled in for each model run to assess performance 

relative to prior validation runs, prior validated versions of the same model, other comparable 

models, and acceptable benchmarks and standards documented in Section 1.0. 

• 3.2 Steps in Model Validation 

The model calibration and validation process is as much art as science and there is truly 

no substitute for experience with a variety of model types and regions, a good mentor, 

and a strong desire to master the techniques. Model validation is a continual learning 

experience as exposure to new data, model structures, study types, and findings leads to 

new realizations about the impact of validation adjustments on project forecasting. 

This section of the report provides a step-by-step process on model estimation and development, 

validation, calibration, and application. The overall process described in this 

section of the report requires iterating among different model steps, primary and secondary 

source data, and a variety of input files and parameter settings. The following steps, 

terms, and considerations are later described separately in somewhat greater detail: 

1. Initiate – Assess key policies, applications, and unique characteristics to be modeled; 

2. Inventory – Identify data needs, sources, gaps, and availability; 

3. Institutional Framework – Prepare action plan and identify responsibilities/timeline; 

4. Secondary Source Data Collection – Socioeconomic and network data; 

5. Primary Source Data Collection – Household travel surveys, on-board transit ridership 

surveys, traffic counts, speed studies; 

6. Data Checking – Compile, process, format, clean, and review data; 

7. Model Estimation – Analyze travel behavior surveys and estimate parameters; 

8. Model Implementation – Draft, test, refine initial model programming and scripting; 

9. Model Calibration – Adjust model parameters to match observed travel behavior; 

10. Model Validation – Adjust model assumptions to meet standards of accuracy; 




11. Model Application – Conduct future year sensitivity testing; and 

12. Iterate – Based on sensitivity testing, return to steps 6-10, if necessary. 

Following the recommended process described in this section of the report should help 

ensure that all key steps in the validation process are being addressed and to encourage 

model development that addresses key issues of importance to the agencies who support 

and will later use the model. Due to funding limitations, some model validation studies 

may exclude steps 5, 7, and 9 (primary survey data collection, model estimation, and 

model calibration) and borrow parameter settings from other comparable models. 

Figure 3.1 depicts the general flow of this process. 




Figure 3.1 

Validation Process 

Initiate 

Inventory 

Institutional 

Framework 

Secondary 

Data 

Collection 

Primary 

Data 

Collection 

Data 

Checking 

Model 

Estimation 


Implementation 

Iterate 


Calibration 


Validation 

NO 


Application 

Satisfactory 

Results? 

YES 

Continual 


Maintenance, 

Application 




3.2.1 Assess Modeling Needs 

Prior to beginning the process of model development/estimation, calibration, and validation, 

Metropolitan Planning Organization (MPO) staff should work closely with the 

Florida DOT District Office to identify key policies, applications, and unique characteristics 

to be assessed with the model. Unique local and regional characteristics to be 

addressed in the model structure could include large percentages of retirees, seasonal 

residents, students, or tourists. 

Example questions that could impact model structure and resulting validation requirements 

could include: 

• Are there plans to study fixed guideway transit corridors for the plan update? If the 

answer is “yes,” the model will need transit networks and pathbuilding, mode choice, 

and transit assignment steps. If Federal Transit Administration (FTA) New Starts 

funding is being sought, FTA guidelines for ridership forecasting must be considered. 

• Are data expected to be available from a household and/or roadside travel survey? If 

a household travel survey is conducted, a process of model estimation and calibration 

will be required. A roadside survey will result in new assumptions for the validation 

of external trips. 

• What role does freight play in the local economy? If freight plays a significant role in 

the local economy, it would be recommended that truck modeling procedures be carefully 

considered and a reasonable sample of truck counts be added to the model 

network. 

Four-step models clearly have limitations in addressing policies such as congestion 

pricing, new urbanism, and peak spreading. While sketch modeling and planning techniques 

can be employed to address some of these issues, consideration might be given to 

activity-based modeling to provide a more objective assessment of these policies. 

3.2.2 Inventory Data Needs 

Data needs, sources, gaps, and availability are a direct outgrowth from discussions on 

policies and applications to be addressed with the travel demand model. Examples of 

policy issues affecting data availability could include the desire of an MPO to assess the 

impact of tourists or students on mode split and roadway congestion. In the case of student 

trips, it would be very important to have socioeconomic data on concentrations of 

students and information on the travel behavior of students. Census school enrollment 

data would be the best source for student data at the resident end, as there is a category 

for “enrolled in college, graduate school, or professional school.” Student and employee 

trip attraction rates are available in a report entitled State University System Transportation 

Study 2 prepared for the Florida Board of Regents in 1993 using extensive travel surveys. 




At this point, it should be determined whether or not data already exists or could be collected 

within available budgets to address any new policies and applications identified 

earlier. If a significant data collection effort cannot be funded, it may be possible to borrow 

data or models from secondary sources such as an area with similar sociodemographic 

characteristics, preferably in a region that has recently conducted travel behavior 

surveys. An assessment of similarities among different regions and urban areas for borrowing 

model parameters could include the following considerations: 

• Trip Generation – Similarities in lifestyle (concentrations of retirees, students, tourists, 

etc.) and employment focus (warehousing, government, attractions, etc.) between areas; 

• Trip Distribution – Similarities in physical geography and development patterns 

between areas (elongated coastal development, large internal water bodies, etc.); 

• Mode Choice – Similarities in available transit technologies/services for different 

areas (all day local bus service only versus fixed guideway transit systems); and 

• Traffic Assignment – Similarities in congestion patterns between areas (peak 

spreading, commercial vehicles, long-distance tourist trips, etc.). 

The National Cooperative Highway Research Program (NCHRP) 8-61 Study, currently 

underway, will provide guidance in data/model transferability. A new report is expected 

by late 2009, as a revision to NCHRP 365 – Travel Estimation Techniques for Urban Planning. 3 

3.2.3 Institutional Framework 

Responsibilities for funding and maintaining models and related data will have a significant 

impact on data collection and model structure. For example, some models are maintained 

by the Florida DOT District Planning Office while other models are the 

responsibility of MPO staff. Timelines for subsequent model application projects might 

limit the ability to incorporate all the model enhancements that are desired by partner 

agencies. 

Several areas in Florida now share a single regional model for multiple MPOs and/or 

Florida DOT Districts. Recent Federal legislation in support of regional coordination is 

aimed at getting adjacent MPOs to communicate with each other, including model needs. 

Model accuracy goals can be impacted when multiple MPOs are using the same model as 

some partners might desire a higher level of accuracy within their jurisdiction while this 

can only be achieved at the expense of compromising regional model validity. An example 

of this could be the case of a large MPO and small MPO sharing a regional model, with 

the larger MPO accounting for a greater share of highway volumes than the smaller MPO. 

Improving the validation accuracy within the smaller MPO area might be at the expense 

of trips actually destined for the CBD of the larger MPO being redistributed within the 

smaller MPO county. 




There are no textbook solutions to resolve regional conflicts such as these; however, negotiations 

between MPOs and the FDOT District Office will be critical to identify a proper 

course of action. These issues also highlight the pivotal role that model users groups can 

play in Florida as an arena for dialogue on such topics. 

3.2.4 Secondary Source Data Collection 

Collection of secondary source data is a requirement of all travel demand forecasting 

models. Such information includes socioeconomic data, highway and transit network 

data, and model parameters for regions that do not have recent household travel behavior 

surveys. Secondary source data on socioeconomic characteristics, for example, would 

include the U.S. Census, private vendors of business data (e.g., InfoUSA), School Boards, 

Chamber of Commerce listings, and the Florida Department of Business and Professional 

Regulation, to name a few. In the absence of household surveys, parameters can be borrowed 

from other regions, national databases (e.g., National Household Travel Survey) 

and guidance documents (e.g., NCHRP 365), or derived directly from Census Journey-to- 

Work data. The report entitled FSUTMS-Cube Framework Phase I: Default Model Parameters 4 

provides additional guidance on borrowing model parameters. Adjustments to secondary 

source data represent a key component of model validation. 

3.2.5 Primary Source Data Collection 

Primary source data collection generally refers to data collection efforts conducted specifically 

for a planning study or model being developed. Examples include travel surveys of 

households, external trips, hotel guests and other visitors, employers, the trucking industry, 

transit riders, and special traffic counts for validation purposes. Travel behavior surveys 

provide data needed for estimation and calibration of trip rates, friction factors, and 

other model parameters. Best practice is to use computer-assisted telephone interviews 

(CATI) for household travel surveys. CATI surveys, while costing more than mailback 

surveys, result in more usable data as trained interviewers and software setups can probe 

for incomplete or inconsistent answers from survey respondents during the interview. 

With the strong interest in seeking FTA New Starts and Small Starts funding for fixed 

guideway transit systems, and a resurgence of concerns over the environmental implications 

of transportation systems, the accuracy of model estimated travel speeds has become 

another area of focus in model validation. While most models in Florida are using synthetically 

derived highway and transit speeds, some areas are factoring posted speeds or 

using speed-delay studies to estimate model speeds. Additional analyses are needed, 

however, to arrive at travel speeds that improve the accuracy of highway assignment 

validation. FTA’s guidelines also indicate the need to know about existing travel patterns 

for transit riders. This implies that recent transit on-board survey data should be available 

for models that will be used in developing New Starts ridership forecasts. 




3.2.6 Data Checking 

While data checking clearly needs to be a key activity prior to starting the model validation 

process, Section 2.0 of this report also emphasized the importance of continuous and 

iterative checking of input data throughout the model validation process. Data checking 

goes beyond the input data (generally secondary source) and includes travel surveys, 

special traffic counts, and speed studies (primary source). Input data should be checked 

against other sources of similar data. Checking and verification should include reasonableness 

checking, statistical analyses, mapping and other visual displays, and limited 

field checks, where feasible. 

The following data should be checked: 

• Socioeconomic input data at the zone level; and 

• Highway and transit network data. 

Socioeconomic Data 

Typically, socioeconomic input data at the zone level includes population; households 

classified by variables such as number of persons, number of workers, income level, 

and/or auto ownership; and employment data classified by types (such as retail, manufacturing, 

etc.). These can be checked at the regional and zone levels against data from 

national sources (e.g., NHTS), and zone data can be sorted to check for reasonableness and 

outliers that may indicate data errors. A number of relationships among different variables, 

such as persons per household, jobs per person, jobs per worker, autos per household, 

and employment and population densities can also be checked through sorting or 

mapping, as can comparisons of base and forecast year data for reasonable growth rates. 

Figure 3.2 depicts visual displays of population and employment densities used in 

assessing data reasonableness. 




Figure 3.2 Example Maps for Visualizing Socioeconomic Densities 




Network Data 

Likewise, highway and transit network data can be checked using summaries, sorting, or 

visual displays. For examples of summaries, highway link speeds could be sorted and/or 

summarized by facility type and/or area type, or transit routes could be sorted by 

headway. 

Highway network maps could include color-coding or posting of attributes such as area 

types, facility types, lanes, screenlines, traffic counts, and speeds. Transit network maps 

can be used to display transit routes by mode (e.g., local bus, express bus, fixed guideway, 

etc.), headway, operating periods, stations, and access connectors. Other checks that 

should be performed include connectivity checks and checking that one-way links are 

coded correctly and in the correct direction. 

Minimum travel-time paths (e.g., time, distance, cost, etc.) can be built “on the fly” using 

Cube to check for connectivity, directionality, logic, and consistency. Detailed recommendations 

on highway network coding procedures are provided in two reports prepared 

in 1998 for the Florida DOT as part of the HNET Enhancements Study 5,6 . 

As stated in the report entitled A Recommended Approach to Delineating Traffic Analysis 

Zones in Florida 7 , “representing realistic access to and from the zones with the use of centroid 

connectors is also important. The following is a summary of guidelines… that one 

should consider when coding centroid connectors: 

• Centroid connectors should represent realistic roadway and transit access; 

• Centroid connectors should not cross man-made or natural barriers, such as lakes, rivers, 

railroad tracks, limited access highways, etc.; 

• Include a sufficient number of centroid connectors to avoid the loading of too many 

trips onto one roadway network link; 

• Do not connect centroid connectors at intersections or directly to interstate ramps; and 

• When two centroid connectors are connected to the same roadway segment, the access 

points should be separated by a certain distance.” 

Figure 3.3 provides three examples of the proper way to code centroid connectors in relation 

to physical geography, street patterns, and subdivision access. TAZs are depicted 

with different fill colors, centroids are represented by a dot, and centroid connectors are 

depicted as dashed lines with arrowheads. 




Figure 3.3 Centroid and Centroid Connector Coding Examples 

Zone - 425 

Zone - 425 

Zone - 426 

Zone - 426 

427 

425 

428 

426 427 

Since travel surveys are conducted infrequently, and the resulting model estimation will 

likely be used for many model validation efforts in the future, it is critical to conduct an 

extensive data cleaning effort using statistical analysis software. Software routines can be 

written to flag any remaining illogical responses to key questions based on a respondent’s 

household characteristics. Survey sampling, expansion, weighting, and addressing 

missing values also are important steps prior to model estimation. 

3.2.7 Model Estimation 

Model estimation is the process of analyzing travel survey data to develop a model 

framework and parameter settings for that framework. Sometimes the model stakeholders 

already have a specific model structure in mind and the focus is on estimating trip 

rates, friction factors, etc., for a predefined set of trip purposes and household characteristics. 

In other situations, the model estimation process is designed to identify which 

household characteristics have the most explanatory power in determining trip generation 

or mode choice. Some small sample cells in a trip rate cross classification matrix might 

require interpolation and adjustment for consistency with results for more common 

household structures. 

While it is best practice that future calibration or validation efforts include updated travel 

surveys and model estimation efforts every 10 years, continued funding limitations have 

made this frequency the exception rather than the rule. Therefore, the model estimation 

conducted from a single travel survey might be the source for several model validation 

cycles into the future. If survey data are ever to be evaluated again in the future, it is critical 

that data and analysis scripts be stored for easy retrieval. Data cleaning and analysis 

scripts (e.g., SAS, SPSS, Excel Macros, etc.) also should be well documented and maintained 

electronically. Figure 3.4 depicts an excerpt from a statistical analysis script used 

for model estimation. Obviously, if the model is not being developed from travel survey 

data, there will not be a model estimation process involved; the process of identifying 

model structures and parameters to be transferred replaces model estimation. 




Figure 3.4 Example Model Estimation Script 

/************************************************************/ 

/* Project 7***.*** */ 

/* SCAG Travel Model Improvement Study */ 

/* HHSurvey.SAS */ 

/************************************************************/ 

/* This program processes the Post Census Regional */ 

/* Household Travel Survey for use in the model development*/ 

/* effort. */ 

/************************************************************/ 

libname hhsurv “P:\7290 – SCAG Regional Airport Demand Model 

Phase I\001\TMIP”; 

*libname hhsurv “D:\KFT\TMIP”; 

libname MNI ‘P:\7290 – SCAG Regional Airport Demand Model 

Phase I\001\TMIP\mni’; 

libname SCAG ‘P:\7365 – SCAG_MODEL IMPROVEMENT’; 

libname SKIM ‘P:\7365 – SCAG_MODEL IMPROVEMENT\HS031504’; 

libname KV ‘P:\7365 – SCAG_MODEL IMPROVEMENT\KV’; 

libname SCAGMNI ‘P:\7365 – SCAG_MODEL IMPROVEMENT\MNI’; 

filename corresp ‘P:\7290 – SCAG Regional Airport Demand Model 

Phase I\001\TMIP\locno2taz.dbf’. 

3.2.8 Model Implementation 

During model implementation, the actual construction of the model takes place. In 

Florida, this would generally focus on Cube-Voyager model scripting. While in the past, 

this process has included programming work (FORTRAN, C++, etc.) for implementing 

model subroutines, the Cube-Voyager scripting language should make the use of external 

“black box” programs obsolete. Within Cube-Voyager, the application manager, scenario 

manager, data and directory structure, and catalog keys should all be set. The application 

manager is used to direct model sequencing through a series of flow charts defining 

model inputs and outputs. The scenario manager will include a user interface for model 

execution and selection of modules and options for each model run. 

File formats and contents need to be identified at this stage as well; however, it is recommended 

to remain consistent with the FSUTMS/Cube-Voyager Data Dictionary 8 where feasible. 

Catalog keys should be used much like the FSUTMS/TRANPLAN PROFILE.MAS 

file to set model parameters and assumptions for all subsequent model runs, with access 

protection from most model appliers. The Olympus training model, developed by the 

Florida DOT Systems Planning Office in Tallahassee, is a good example to use in setting 

up a model consistent with the latest FSUTMS standards. Figure 3.5 depicts the scenario 

and application manager for this model. 




Figure 3.5 

Cube Base User Interface 

Cube Graphics 

Scenario 

Manager 

Application 

Manager 

Data 

Catalog 

Keys 

3.2.9 Model Calibration 

Model calibration is a process where models are adjusted to simulate or match observed 

household travel behavior in the study area. As with model estimation, calibration is not 

possible in the absence of survey data on household travel behavior. Where such data are 

not available, the analyst will skip this step and proceed directly to model validation. In 

situations where household surveys were recently completed, model calibration should 

strive for model replication of trip generation and distribution patterns from the survey. 

Depending on the sample size of transit riders in the survey, the model could potentially 

be adjusted to match reported mode splits. A transit on-board survey can help prepare 

mode choice targets for transit submodes. If a time-of-day assignment model is present or 

under consideration, the travel survey should provide sufficient information to calculate 

time-of-day factors for the model. 

Much of calibration is typically focused on trip distribution. Figure 3.6 depicts an example 

trip length frequency distribution comparing observed against model estimated trip 

length patterns. The relationship between the two lines depicted should be measured by 

calculating a coincidence ratio. As described in Section 2.0, the coincidence ratio should 

be 70 percent or higher to achieve calibration in most cases. Adjustment and calibration of 

friction factors by trip purpose is a primary calibration tool. 




Figure 3.6 

Trip Length Frequency Distribution 

8% 

6% 

Coincidence Ratio = 0.82 

Estimated (ATL = 18.2 Min) 

Observed (ATL = 18.9 Min) 

Percent of Total Trips 

4% 

2% 

0% 

0 10 20 30 40 50 60 

Travel Time (in Minutes) 

A key consideration in calibrating friction factors is how to measure trip travel times. One 

approach is to use survey reported travel times; however, most people round off their trip 

estimates in even five-minute increments, thus distorting the trip length frequency distribution. 

A more common approach is to use travel-time skims to estimate travel times 

between zones in place of reported times. Yet another approach is to calculate some form 

of moving weighted average based on respondent times. If time permits, it might be 

worthwhile to test more than one of these calibration approaches and compare the results. 

3.2.10 Model Validation 

Model validation is a process where models are adjusted to simulate base year traffic 

counts and transit ridership figures. Model validation is conducted whether or not 

household travel survey data are available. Model validation involves comparing model 

estimates against data not used directly in model estimation or calibration, such as traffic 

counts, and is focused on the ability of models to conform to a prescribed set of accuracy 

standards. Recommended accuracy standards and a validation checklist were provided in 

Section 2.0 of this report. Documentation of each validation run using a spreadsheet 

template, such as depicted in Table 3.1, is strongly recommended to identify changes 

made to the model and results from making these adjustments. 




Table 3.1 

Example Validation Worksheet Root Mean Square Error 

NERPM 2000 1998 1998 

Count Range Accuracy Range Cube TRANPLAN NERPM (old) JUATS 

0-5,000 45-55 79.00% 75.15% 99.39% 75.35% 

5,000-10,000 35-45 34.80% 33.90% 43.77% 35.80% 

10,000-20,000 27-35 31.80% 30.92% 28.81% 30.56% 

20,000-30,000 24-27 22.90% 20.04% 21.72% 19.66% 

30,000-40,000 22-24 18.40% 19.90% 22.44% 16.87% 

40,000-50,000 20-22 14.80% 14.78% 17.67% 15.80% 

50,000-60,000 18-20 11.10% 11.82% 10.77% 12.76% 

60,000-70,000 17-18 5.30% 13.97% 32.06% 37.49% 

70,000-80,000 16-17 14.70% 11.77% 21.24% 27.93% 

80,000-90,000 15-16 0.00% 0.00% 0.00% 0.00% 

90,000-100,000 14-15 0.00% 0.00% 0.00% 0.00% 

100,000-400,000 LT 14 0.00% 0.00% 0.00% 0.00% 

Average Total 32-39 33.70% 32.34% 37.72% 32.86% 

RMSE NERPM 2000 by County 

Nassau Duval St. John Clay 

34.90% 29.85% 41.44% 37.87% 

Validation efforts are sometimes misguided as some model validation adjustments 

improve the match between base year results and observed data while being detrimental 

to the accuracy of forecast year results. Adjustments to key model parameters can sometimes 

result in unintended consequences when applying the model to future year conditions. 

Rather than making arbitrary adjustments to model parameters such as trip 

production rates, it would be better to borrow validated parameters from another model 

previously calibrated using household travel survey data. Similarities in household and 

employment characteristics as well as physical geography and development patterns 

should be considered when borrowing parameters from other models. 

3.2.11 Model Application 

As described in Section 2.0, a model cannot be considered fully validated until it is applied 

to simulate future year travel conditions. Unintended consequences might result from 

validation adjustments. The reasonability of future year model forecasts is usually 

assessed by conducting a series of sensitivity tests, measuring the change in demand 

based on a change in supply. For example, as lanes are added to a congested roadway 

network, it is expected that traffic would increase on the facilities receiving the additional 




capacity. Likewise, improving bus headways should lead to increased transit ridership in 

the affected corridor. 

The elasticity of induced demand also can be quantified as an assessment of model sensitivity. 

Elasticities can be calculated in several different manners but generally use the following 

formula equating elasticity to a change in VMT over a change in lane-miles: 

ΔVMT 

Elasticity = 

ΔLaneMiles 

As documented in a literature review for the Wasatch Front Regional Council Model 

Sensitivity Testing and Training Study 9 prepared for the Utah DOT, short-term effects have 

an elasticity range from near 0 to about 0.40, while long-term elasticities range from about 

0.50 to 1.00. This means that a 10 percent increase in lane-miles under short-term conditions 

can cause up to a 4 percent increase in VMT while a 10 percent increase in lane-miles 

under long-term conditions can cause up to a 10 percent increase in VMT. For the Utah 

DOT Study, as depicted in Figure 3.7, scenarios were run with select model steps held 

constant in order to identify whether trip generation, distribution, or assignment were 

contributing more to the elasticity of travel demand. 

Figure 3.7 Elasticities by Model Step 

Total Elasticity 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

Alternative 3 Total Elasticities 

Scenario Description 

Alternative 4 Arterial Elasticities 

Only trip generation 

held constant 

Trip generation and 

distribution held constant 

Trip generation, distribution, 

and mode choice held constant 




3.2.12 Iterate 

The process from data checking through model application, described in this report section 

and highlighted in Figure 3.8, requires a series of successive iterations. A review of 

model output results should help explain which step in the process might be causing 

model results to be unacceptable. Guidance is provided in the next section of this report 

that links specific validation problems with potential solutions. 

Figure 3.8 The Iterative Modeling Process 

Validation and Reasonableness Checks 

Estimation 

Calibration 

Validation 

Application 

Iterate 

Iterate 

Iterate 

• 3.3 Guidance on Validation and Adjustment 

Charting a course for model validation is easier with some combination of local area 

knowledge, familiarity with sources for transferable model parameters, an understanding 

of what constitutes acceptable model results, experience with the cause and effect of different 

model adjustments, and the quality and availability of data. Section 3.3 provides 

guidance on how to improve model validity when expected results are not achieved, 

organized in accordance with the classic four steps of travel demand forecasting. This 

report is not a standalone document but rather part of a greater body of FSUTMS 

guidance and standard procedures. 

Section 2.0 of this report described accuracy standards and benchmarks of reasonableness. 

The Phase I Model Parameters report, referenced earlier, provides suggestions on similar 

urban areas for borrowing and testing different parameters. Hands-on experience with 

model validation concepts is provided in a series of training workshops sponsored by the 

Florida DOT Systems Planning Office. 




3.3.1 Model Input Data 

Model input data, including socioeconomic data and networks, should be checked at the 

beginning of the validation process, and before validating trip generation or other steps in 

the model chain. The input data could cause errors in many model steps and must be 

continuously reviewed during the validation process as some data errors might not 

become apparent until a model is executed. It is best practice to conduct a series of different 

data checks that could be categorized as follows: 

• Syntax – Data improperly coded or mathematically inconsistent such as percent auto 

availability categories in socioeconomic data not adding up to 100 percent; 

• Ratio Analyses – Calculate ratios among different variables at the regional, subarea, 

and zonal level such as population/dwelling units or employment/population; 

• Comparisons among Multiple Sources – Use of different sources for similar information 

to identify inconsistencies among these sources (e.g., InfoUSA versus Dun & 

Bradstreet employment estimates); 

• Backcasting – Use of land use forecasting models to project a new base year (e.g., 

2005) from a previous base year (e.g., 2000); and 

• Visual Checks – Use of Cube Base and GIS software to review data on screen or 

through plotting and printing of maps, graphs, and charts as depicted in Figure 3.9. 

After reviewing statistics and visual checks, the next step should be to address issues 

identified during the data review. Most problems will likely be limited to specific zones 

or network links; however, some errors might be intrinsic to the assumptions used in preparing 

the data. A list of potential errors and suggestions on how to correct these would 

be quite exhaustive. Correcting data should be a team effort of consulting staff, MPO 

staff, Florida DOT District staff, and local government staff from relevant agencies. 

Checking of socioeconomic data is a routine, iterative part of model validation. Visual 

highway and transit network checks are critical in identifying coding errors. Additional 

guidance is provided on checking and reviewing input data in Section 1.1. 

3.3.2 Validating Trip Generation Models 

Proper validation of a trip generation model requires checking and correcting socioeconomic 

data, in addition to verifying and adjusting model parameters. Since trip generation 

affects the accuracy of all subsequent model steps, it is critical that all inputs be 

confirmed and reassessed throughout validation. Some symptoms of an underperforming 

trip generation model are found in statistics from this model step while others might be 

more obvious from subsequent steps in the model chain. 




Figure 3.9 Visual Depiction of Network Characteristics 

Issue A: Aggregate Trip Rates Higher/Lower Than Typical Benchmarks 

Examples of aggregate trip rates, as described in Section 2.0, include trips per person, trips 

per household, and home-based work trips per employee. 

Possible reasons for this result: 

1. Socioeconomic Data Errors – Population, dwelling unit, and/or employment data 

over- or under-reported. 

2. Trip Production Rates – Accuracy of observed trip rates might be suspect or borrowed 

trip rates are perhaps not so applicable to the region being validated. 

3. Dwelling Unit Weights – Assumptions for household stratification curves might be 

generalized or outdated. 

Ways to address: 

1. If aggregate rates are out of reasonableness range, this probably indicates more of a 

global problem as errors in a handful of zones are not likely to affect regionwide rates. 

Population, dwelling unit, and employment data should be compared to other sources 




(Census, proprietary employment data providers) at the City, County, and 

Metropolitan Area level for applicability and assumptions. 

2. As the selection of trip rates could be the cause of these problems, methodologies used 

to calculate observed trip production rates should be reviewed since analysis assumptions 

could be adjusted (e.g., inclusion/exclusion of zero trip households, procedures 

for weighting and missing values, trip purpose definition, etc.). If observed trip production 

rate data are not available, consideration could be given to borrowing rates 

from a different, yet similar, area. 

3. Many areas just recycle the same dwelling unit weights from one validation effort to 

another and the rates used are often default FSUTMS stratifications from the 1980 

report entitled Urban Transportation Model Update Task B: Review and Refinement of 

Standard Trip Generation 10 . As these dwelling unit weights were derived from the 1970 

Census, a preferred approach is to calculate dwelling unit weights for the region using 

Census 2000 stratifications by household size. 

Issue B: Trip Purpose Percentages Higher/Lower Than Typical Benchmarks 


1. Trip Production Rates – This issue already was described above. 

2. Trip Attraction Rates – Primarily for nonhome-based and commercial vehicle purposes 

that do not generally have separate trip production rates. 


1. Possible solutions to this problem differ based on the specific trip purpose that is out 

of range. The number of home-based trips is dependent on the production rates and 

household socioeconomic data, as discussed in the aggregate trip rate discussion 

above. 

2. Nonhome-based (NHB) purposes and commercial vehicle trip purposes are conversely 

controlled by trip attraction rates and employment socioeconomic data. Survey-based 

trip attraction rates are generally in short supply as are the workplace surveys typically 

used to calculate these rates. Consideration could be given to borrowing attraction 

rates from either the Southeast Florida Regional Planning Model (RPM) or the 

Tampa Bay RPM rather than recycling default FSUTMS trip attraction rates from the 

1970s. These are the only two regions in Florida that have conducted recent workplace 

surveys; however, the trip purpose stratification for these two regions differs from 

most FSUTMS models. NHB activity also can be adjusted through the introduction of 

special generators as 25 to 33 percent of trips destined for universities, airports, etc., 

are likely not home-based. It is best practice to keep special generators to a minimum 

as the nature of such adjustments can lead to complexities in forecasting trips for 

future years. 




Issue C: Large Difference between Balanced and Unbalanced Trips by Purpose 


1. Socioeconomic data errors with an emphasis on employment data, as discussed 

earlier. 

2. Trip Production Rates – This issue already was described above. 

3. Trip Attraction Rates – This issue already was described above. 


1. Check the source of control totals for population and employment estimates, as well as 

employment levels by category. 

2. See Issue A above. 

3. See Issue B above. Balancing trips by subarea, rather than across the entire region, 

could also be a consideration. 

Issue D: Problems Related to Other Model Steps (e.g., Trip Distribution or 

Assignment) 


1. Under- or over-assignment of trips along corridors adjacent to major activity centers 

might indicate a problem with trip generation or external trip assumptions for locations 

near the study boundary. 

2. Incorrect trip purposes dominating trip generation. 


1. Corridor-specific assignment errors may require the addition or modification of special 

generators while assignment problems on major intercity corridors could warrant 

a reassessment of internal-external/external-external splits, unless splits are based on 

a recent roadside survey. 

2. Cube node-point charts, as depicted in Figure 3.10, can be used to assess both the magnitude 

of productions or attractions (size of the circle) within a subarea by zone as well 

as the relative share by trip purpose (pie slices within the circle). Node-point charts 

can also be used in distribution and mode choice. 




Figure 3.10 Example Node-Point Chart 

3.3.3 Validating Trip Distribution Models 

Validation of trip distribution models, by nature, must include the highway network and 

pathbuilding processes since travel times estimated between each pair of zones are critical 

to linking trip productions to attractions. In addition to networks, pathbuilding, and the 

Gravity Model itself, validating trip distribution requires an assessment of subsequent 

mode choice and assignment steps. During mode choice, it is important to evaluate the 

distribution patterns of transit trips, particularly from zero auto households. Trip assignment 

accuracy along screenlines is a key to validating trip distribution as well. 

Issue A: Poor Match between Observed and Modeled Trip Length Frequency 

Distribution 


1. Inappropriate Friction Factors – Friction factors may not have been derived correctly. 

If formulas or factors were obtained from other models, or older versions of the same 

models, they may not be appropriate for the current model. 

2. Use of Highway Travel Time as Impedance for Non-Auto Travel – This could be 

checked by examining observed versus modeled travel times for areas with relatively 

high shares of non-auto travel. Such areas could be identified through results from 

previous versions of the model, survey data (where available), and Census journey-towork 

data. 




3. Inaccurate Internal-External Trip Generation – This could be checked by examining 

observed versus modeled travel times for internal-external travel. Observed data 

could be obtained from household or external station surveys, where available. 

4. Missing or Incomplete Socioeconomic and land Use Data – Sometimes running of subsequent 

model steps reveals input data errors that initial checks did not catch. 

Observed versus modeled trip lengths should be examined for specific areas 

throughout the model region. If some areas indicated poorer matches between 

observed and modeled trip lengths, additional checks of socioeconomic data may be 

warranted. 


1. If friction factors are derived from a formula (e.g., gamma function), the parameters of 

the function can be adjusted to result in lower or higher average trip lengths. If the 

friction factors are derived through an automated calibration routine or other sources 

(such as a previous model), the automated routine can be run again, or manual 

adjustments can be made. Higher friction factors for shorter trips can be used to lower 

average modeled trip lengths, and vice versa. 

2. Two ways of addressing this issue would be: 

- Modeling zero-auto household separately in trip distribution, using a more appropriate 

impedance measure (such as distance, transit travel time, or mode choice 

logsum); and 

- Using the mode choice logsum as an impedance measure, although if nonmotorized 

travel is significant and is not modeled, the effect of such a measure could be 

limited. 

3. If friction factors for internal-external travel are derived from a formula, the parameters 

of the function can be adjusted to result in lower or higher average trip lengths. If 

the friction factors are derived through an automated calibration routine or other 

sources, the automated routine can be run again, or manual adjustments can be made. 

Higher friction factors for shorter trips can be used to lower average modeled trip 

lengths, and vice versa. 

4. Any data errors should be corrected. 

Issue B: Over- or Under-Estimation of Intrazonal Trips 


1. Inappropriate friction factors – Friction factors may not have been derived correctly, 

resulting in incorrect instances of shorter trips (including intrazonals). If formulas or 

factors were obtained from other models, or older versions of the same models, they 

may not be appropriate for the current model. 




2. Inappropriate intrazonal travel times – Unlike skimmed interzonal travel times, intrazonal 

travel times are not derived from centroid-to-centroid paths. Common ways of 

estimating intrazonal times include functions of terminal times, zone areas and shapes, 

and travel times to a set number of adjacent or nearby zones (e.g., the three nearest 

zones). This could be an especially important issue for models that include nonmotorized 

travel, which would then constitute a high percentage of trips. A relatively 

easy check is to ensure that intrazonal travel times are not about the same as or greater 

than times to other zones, unless geography dictates that such results are reasonable. 


1. See (1) under Issue A above. 

2. The method for estimating intrazonal travel times would need to be adjusted. Another 

method might be chosen, or the function used to estimate intrazonal time could be 

revised. For example, the number of nearest zones from which times are estimated 

could be changed. 

Issue C: Inability to Achieve Closure with Gravity Model (Modeled Attractions 

Do Not Match the Output of Trip Generation Well) 


1. Not Enough Iterations of Gravity Model – The user can set either a fixed number of 

iterations or a specific convergence criterion. These settings can easily be revised. 

2. Incomplete Path Sets – Any zones for which the gravity model has trouble matching 

the trip attractions may not be properly connected to the network as a whole. 


1. The number of iterations should be increased, or the convergence criterion tightened, 

until reasonable convergence is achieved. It is a relatively quick process to revise the 

parameters and run a new gravity model test, and so several tests can be run to determine 

the optimal settings. 

2. Paths should be built between each zone with this problem and all other zones. Any 

zones for which a path cannot be built should be checked link by link to determine the 

cause for the disconnect. 

Issue D: Inconsistencies between Modeled and Observed District-to-District Trips 

This check generally requires an expanded regional household travel survey from which 

an observed district-to-district trip table can be derived. An observed home-based work 

district-to-district trip table could be derived from U.S. Census journey-to-work data 

(although caution should be used as the Census reports an entire home-work chain rather 

than individual home-based work trips). Results from screenline summaries (see 




Section 3.3.5) could also help identify such problems in the case of trips between districts 

on one side of the screenline and the other. Desire lines, as depicted in Figure 3.11, are a 

useful tool for reviewing distribution patterns of district-level trip tables. 

Figure 3.11 District-Level Desire Line Example 


1. Too Many or Too Few Trips between the Most Distant Districts – This is indicative of a 

problem with the friction factors (see issue A above) or perhaps issues with travel-time 

skims. 

2. Inaccurate Socioeconomic Data – This could be a cause in some areas, as well as the 

presence of special generators with atypical distribution patterns (universities, airports, 

etc.). 

3. Errors in Networks – Other possible indications could be network problems (connectivity 

or coding of attributes such as speed or capacity). 

4. Other Poor District-Level Matches – Each district pair with a poor match should be 

examined. Exchanges which require crossing bodies of water or other barriers, or 

crossing from one jurisdiction to another, have been shown in many analyses to have 

fewer trips made than might be indicated solely considering travel time. 





1. See (1) under issue A above. 

2. Socioeconomic Data in Problematic Districts Should Be Rechecked – Districts with specific 

trip distribution issues should be examined to determine whether socioeconomic 

data are accurate or special generators may need to be examined separately by introducing 

separate sets of friction factors or K-factors (see below). 

3. Network Link Coding Should Be Checked for Errors – Paths should be built, and 

travel times checked, between zone pairs with one zone in each problematic district 

pair. Any problematic link coding or disconnects should be addressed. 

4. Consideration of K-Factors or Subarea Balancing Could Be Warranted – Such measures 

should never be used as a first line adjustment, and they should reflect explainable 

travel behavior factors (such as jurisdictional or physical barriers). K-factors 

should be relatively low so as not to marginalize the explanatory power of travel 

impedance in trip distribution. Factors that are higher than 3 or lower than 0.3 should 

require specific justification. Subarea balancing could be used when survey data 

shows patterns of trips tending to stay within certain districts. 

3.3.4 Validating Mode Choice Models 

Validation of mode choice models is heavily influenced by logit coefficients and constants 

as well as exogenous inputs to the process, including highway and transit networks and 

skims, transit fare matrices, and parking cost data. Checks on transit networks and skims 

should include the following: 

• Network connectivity and shortest paths; 

• Checks of access link times; 

• Sort routes by headways; 

• Walk percentages and walk access buffering around transit routes; and 

• Compare bus speeds to highway speeds. 

Figure 3.12 depicts walk access buffers around transit routes and stops. The area displayed 

in red represents the proportion of each TAZ that is accessible to transit via 

walking. 




Figure 3.12 Example of Walk Access Buffering 

Mode split targets, once estimated, can be used as a guide for aggregate mode choice validation. 

Mode choice validation targets should be developed at the most disaggregate 

level supported by available data and rechecked to make sure linked transit trips were 

used to calculate the numbers rather than unlinked trips. Disaggregate validation goes 

beyond mode shares to look at household characteristics, traveler characteristics, zonal 

characteristics, and trip characteristics. 

Table 3.2 presents an example of a mode choice validation target matrix. It is important to 

ensure that the validation matrix does not contain many cells where the observed mode 

shares are based on very little observed information. This may mean that some segments 

which the mode choice model can estimate separately should be combined for validation 

purposes. For example, if the number of observations from the on-board survey for light 

rail with walk access for high income for District 1 is five, and that is expanded to the total 

population with a value of 50, the error range with only five observations is so great that 

the “real” observed trips for this segment might be anywhere from a dozen or two up to a 

few hundred. It would be better to combine such a market segment with others so that 

the sampling error is reduced. 




Table 3.2 

Example Validation Target Matrix 

(For Home-Based Work Trips) 

Low Income Medium Income High Income 

Mode Number Mode Number 

Share of Trips Share of Trips 

Number 

of Trips 

Mode 

Share 

District 1 Drive Alone 3,090 69.95% 34,360 80.39% 17,933 81.39% 

District 1 Shared Ride 2 678 15.36% 4,289 10.04% 1,972 8.95% 

District 1 Shared Ride 3+ 233 5.28% 1,442 3.37% 665 3.02% 

District 1 Transit-Walk Access 374 8.47% 2,196 5.14% 604 2.74% 

District 1 Transit-Auto Access 42 0.94% 452 1.06% 860 3.90% 


District 2 Shared Ride 2 1,228 17.95% 8,962 10.23% 5,587 13.27% 

District 2 Shared Ride 3+ 294 4.30% 2,148 2.45% 1,339 3.18% 

District 2 Transit-Walk Access 1,559 22.80% 26,708 30.49% 2,562 6.08% 

District 2 Transit-Auto Access 115 1.69% 3,826 4.37% 2,046 4.86% 


District 3 Shared Ride 2 9,935 15.15% 85,828 9.65% 33,696 8.67% 

District 3 Shared Ride 3+ 3,217 4.91% 27,770 3.12% 10,904 2.81% 

District 3 Transit-Walk Access 2,605 3.97% 18,926 2.13% 1,619 0.42% 

District 3 Transit-Auto Access 293 0.45% 4,840 0.54% 2,181 0.56% 

Total Drive Alone 56,247 73.22% 832,231 81.62% 388,543 85.85% 

Total Shared Ride 2 11,841 15.41% 99,079 9.72% 41,255 9.12% 

Total Shared Ride 3+ 3,745 4.87% 31,360 3.08% 12,908 2.85% 

Total Transit-Walk Access 4,538 5.91% 47,830 4.69% 4,785 1.06% 

Total Transit-Auto Access 450 0.59% 9,118 0.89% 5,087 1.12% 

Total All Trips 76,821 1,019,619 452,578 




Issue A: Inappropriate Mode Choice Parameters 

All parameters should be checked for reasonableness in sign and magnitude, and relationships 

among parameters should be checked as well. All coefficients in a mode’s utility 

function related to level of service (time, cost, number of transfers) for that mode should 

be negative. Out of vehicle travel is generally considered to be more onerous than invehicle 

travel, and so out-of-vehicle time coefficients should be greater in absolute value 

than in-vehicle time coefficients. The ratio of the in-vehicle time to cost coefficient for each 

mode represents the value of (in-vehicle) time and can be converted to familiar units (e.g., 

dollars/hour) for reasonableness checking. Table 3.3 presents some values for level of 

service coefficients from urban areas around the United States. 


1. Inappropriate coefficients – If coefficients were estimated from survey data, data 

insufficiency or errors might be preventing estimation of valid coefficients. If coefficients 

were transferred from another context or model, the model transferability might 

not be completely appropriate, or the original model from which the coefficients were 

transferred might have had errors. 


1. Coefficients or relationships between them (e.g., ratio of out-of-vehicle time coefficient 

to in-vehicle time coefficient) might have to be constrained to a reasonable value. For 

a model being estimated from survey data, the model should be re-estimated with the 

constraint imposed. 




Table 3.3 

Mode Choice Model Parameters from U.S. Urban Areas 

Baltimore Dallas Denver Detroit Houston 

Los 

Angeles Milwaukee Philadelphia Pittsburgh Portland Sacramento 

St. 

Louis Tucson 

Average Range 1993 1996 1985 1996 1985 1991 1991 1986 1978 1985 1991 1965 1965 

Home-Based Work 

Auto IVT (Minutes) -0.032 -0.01 to -0.05 -0.034 -0.055 -0.018 -0.052 -0.022 -0.021 -0.016 -0.042 -0.047 -0.039 -0.025 -0.023 -0.018 

Auto OVT (Minutes) -0.058 -0.04 to -0.09 -0.044 -0.093 -0.041 -0.069 -0.065 -0.038 -0.057 

Auto Operating Cost 

(Dollars) 

-0.513 -0.3 to -1.3 -0.143 -0.558 -0.350 -0.410 -0.614 -0.296 -0.450 -0.260 -2.100 -1.353 -0.279 -1.170 -0.184 

Parking Cost (Dollars) -0.672 -0.3 to -1.3 -0.143 -0.558 -0.950 -0.410 -1.540 -0.296 -0.450 -0.260 -2.100 -1.353 -0.279 -1.170 -0.184 

Transit IVT (Minutes) 

(Dollars) 

Transit Walk Time 

(Minutes) 

Transit Wait Time 

(Minutes) 

Transit Transfer Time 

(Minutes) 

-0.024 -0.01 to -0.05 -0.012 -0.025 -0.018 -0.009 -0.022 -0.021 -0.016 -0.011 -0.047 -0.039 -0.025 -0.023 -0.018 

-0.050 -0.04 to -0.07 -0.044 -0.064 -0.054 -0.019 -0.057 -0.053 -0.041 -0.032 -0.069 -0.065 -0.038 -0.057 -0.040 

-0.050 -0.04 to -0.07 -0.029 -0.064 -0.028 -0.019 -0.057 -0.053 -0.041 -0.051 -0.069 -0.040 -0.038 -0.057 -0.040 

-0.050 -0.04 to -0.07 -0.016 -0.064 -0.059 -0.019 -0.057 -0.053 -0.041 -0.051 -0.069 -0.090 -0.038 -0.057 -0.040 

Transit Cost (Dollars) -0.531 -0.3 to -1.3 -0.053 -0.550 -0.440 -0.410 -0.614 -0.296 -0.450 -0.115 -2.100 -1.353 -0.279 -1.170 -0.184 

Number of Transfers -0.268 -0.088 

Ratio: Walk/IV Time 2.08 1.5 – 3 3.55 2.56 3.00 2.00 2.58 2.50 2.62 2.97 1.47 1.64 1.52 2.50 2.25 

Ratio: Wait/IV Time 2.10 1.5 – 3 2.33 2.56 1.57 2.00 2.58 2.50 2.62 4.80 1.47 1.01 1.52 2.50 2.25 

Value of Time (Auto) $3.79 $2 – $5 $14.16 $5.91 $3.09 $7.61 $2.15 $4.25 $2.09 $9.66 $1.33 $1.75 $5.39 $1.17 $5.78 

Value of Time (Transit) $2.72 $2 – $5 $14.16 $2.73 $2.45 $1.36 $2.15 $4.25 $2.09 $5.53 $1.33 $1.75 $5.39 $1.17 $5.78 




Table 3.3 

Mode Choice Model Parameters from U.S. Urban Areas (continued) 

Home-Based Nonwork 


Los 


St. 

Louis Tucson 

Average Range 1993 1996 1985 1996 1985 1991 1991 1986 1978 1985 1991 1965 1965 

Auto IVT (Minutes) -0.019 -0.01 to -0.03 -0.082 -0.011 -0.012 -0.019 -0.017 -0.024 -0.009 -0.020 -0.017 -0.033 -0.021 -0.024 -0.024 

Auto OVT (Minutes) -0.066 -0.06 to -0.08 -0.049 -0.076 -0.041 -0.061 -0.069 -0.079 -0.086 -0.055 -0.060 

Auto Operating Cost 

(Dollars) 

-0.839 -0.2 to -1.3 -0.347 -0.162 -1.310 -0.657 -0.592 -0.216 -1.330 -0.100 -1.450 -0.399 -0.557 -2.430 -0.250 

Parking Cost (Dollars) -0.787 -0.2 to -1.3 -0.347 -0.162 -0.657 -1.479 -0.216 -1.330 -0.100 -1.450 -0.399 -0.557 -2.430 -0.250 


(Dollars) 


(Minutes) 


(Minutes) 


(Minutes) 

-0.016 -0.01 to -0.05 -0.023 -0.007 -0.012 -0.002 -0.017 -0.024 -0.009 -0.001 -0.017 -0.033 -0.021 -0.024 -0.024 

-0.049 -0.01 to -0.07 -0.049 -0.053 -0.076 -0.007 -0.026 -0.061 -0.069 -0.002 -0.079 -0.086 -0.055 -0.060 -0.054 

-0.051 -0.01 to -0.07 -0.049 -0.053 -0.076 -0.007 -0.035 -0.061 -0.069 -0.002 -0.079 -0.086 -0.055 -0.060 -0.054 

-0.048 -0.01 to -0.07 -0.036 -0.053 -0.007 -0.043 -0.061 -0.069 -0.002 -0.079 -0.086 -0.055 -0.060 -0.054 

Transit Cost (Dollars) -0.717 -0.2 to -1.3 -0.096 -0.217 -0.220 -0.592 -0.216 -1.330 -0.012 -1.450 -0.399 -0.557 -2.430 -0.250 

Number of Transfers -0.230 -0.230 

Ratio: Walk/IV Time 3.16 2 to 6 2.18 7.57 6.34 3.05 1.50 2.50 7.36 2.50 4.56 2.61 2.62 2.50 2.25 

Ratio: Wait/IV Time 3.23 2 to 6 2.18 7.57 6.34 3.05 2.00 2.50 7.36 2.50 4.56 2.61 2.62 2.50 2.25 

Value of Time (Auto) $1.35 $0.5 to $5 $14.16 $4.07 $0.55 $1.74 $1.75 $6.73 $0.42 $12.18 $0.72 $4.96 $2.26 $0.59 $5.78 

Value of Time (Transit) $1.31 $0.5 to $5 $14.16 $1.94 N/A $0.60 $1.75 $6.73 $0.42 $4.82 $0.72 $4.96 $2.26 $0.59 $5.78 




Table 3.3 

Mode Choice Model Parameters from U.S. Urban Areas (continued) 

Nonhome-Based Work 


Los 


St. 

Louis Tucson 

Average Range 1993 1996 1985 1996 1985 1991 1991 1986 1978 1985 1991 1965 1965 

Auto IVT (Minutes) -0.021 -0.01 to -0.03 -0.045 -0.011 -0.013 -0.019 -0.024 -0.050 -0.011 -0.004 -0.012 -0.035 -0.023 -0.014 

Auto OVT (Minutes) -0.102 -0.02 to -0.2 -0.099 -0.033 -0.041 -0.126 -0.074 -0.009 -0.195 -0.127 -0.082 -0.058 

Auto Cost -0.998 -0.2 to -1.3 -0.189 -0.200 -1.330 -0.657 -0.562 -0.453 -0.310 -0.046 -3.050 -1.103 -2.350 -0.151 

Parking Cost (Dollars) -0.965 -0.2 to -1.3 -0.189 -0.200 -0.657 -1.404 -0.453 -0.310 -0.114 -3.050 -1.103 -2.350 -0.151 


(Dollars) -0.017 -0.01 to -0.05 -0.013 -0.007 -0.013 -0.002 -0.024 -0.050 -0.011 -0.007 -0.012 -0.035 -0.023 -0.014 


(Minutes) -0.064 -0.02 to -0.2 -0.099 -0.036 -0.033 -0.007 -0.036 -0.126 -0.074 -0.017 -0.195 -0.127 -0.082 -0.058 -0.031 


(Minutes) -0.062 -0.02 to -0.2 -0.037 -0.036 -0.033 -0.007 -0.047 -0.126 -0.074 -0.017 -0.195 -0.127 -0.082 -0.058 -0.031 


(Minutes) -0.063 -0.02 to -0.2 -0.037 -0.036 -0.007 -0.036 -0.126 -0.074 -0.017 -0.195 -0.127 -0.082 -0.058 -0.031 

Transit Cost (Dollars) -0.945 -0.2 to -1.3 -0.056 -0.200 -0.220 -0.562 -0.453 -0.310 -0.086 -3.050 -1.103 -2.350 -0.151 

Number of Transfers 

Ratio: Walk/IV Time 3.78 2 to 7 7.44 5.14 2.50 3.05 1.50 2.50 7.00 2.50 15.99 N/A 2.34 2.50 2.25 

Ratio: Wait/IV Time 3.65 2 to 7 2.81 5.14 2.50 3.05 2.00 2.50 7.00 2.50 15.99 N/A 2.34 2.50 2.25 

Value of Time (Auto) $1.23 $0.2 to $5 $14.16 $3.30 $0.59 $1.74 $2.53 $6.66 $2.05 $4.75 $0.24 N/A $1.90 $0.59 $5.41 

Value of Time (Transit) $1.08 $0.2 to $5 $14.16 $2.10 N/A $0.60 $2.53 $6.66 $2.05 $4.75 $0.24 N/A $1.90 $0.59 $5.41 




Issue B: Transit Trips Over- or Under-Estimated 


1. It is likely that, if modeled and observed shares match poorly, there are errors in transit 

or highway networks, path building and skim settings, transit accessibility, socioeconomic 

data, or other inputs such as parking costs. Some settings (e.g., maximum 

walk and auto access times) should also be checked for consistency with the results of 

on-board surveys. 

2. Transit markets might not be represented properly in the model. One of the best ways 

to check this is by assigning the trip table obtained by expanding the transit on-board 

survey (if available) to the transit network and comparing the results to observed ridership. 

(This may also help identify network coding and path building errors.) 

3. After all other areas are checked, it is still common that modeled mode shares and/or 

transit ridership from an uncalibrated model do not match the observed data. This 

may be due to errors in the survey data such as undetectable or uncorrectable biases, 

sampling error, response errors, recording errors, or missing responses. 


1. All network data, including transit route fares, headways, and stops, and transit access 

should be rechecked for completeness and consistency. Other data, including socioeconomic 

data and parking costs, should also be checked. Path building settings, such 

as weights for walk and wait time and values of time, should be made consistent with 

the mode choice model parameters as well as the results of on-board surveys. 

2. The results of the assignment of the on-board survey trip table should be examined by 

any market segmentation that is available. This includes trip purpose, geographic, 

corridor, and demographic (e.g., income level). Route-level differences from the 

observed ridership should result in checks of the coding of the transit route and its 

stops and accessibility. Additional market segmentation might be indicated to correct 

for discrepancies from certain markets (for example, adding income-level segmentation 

to the model). It is possible that the comparison will show errors in the trip distribution, 

which should be corrected before mode choice is rerun. 

3. The most common method for achieving reasonable mode shares is to adjust the mode 

choice constants to achieve better replication of observed mode shares and/or transit 

ridership. However, it is critical not to use the calibration of mode choice constants as 

corrections for other unrelated model problems such as those described above. Therefore, 

extreme care should be taken to ensure that all other causes have been ruled out 

before constants are adjusted. The adjustment of constants is usually done in an iterative 

manner, where the mode choice model is run, the results are compared to 

observed data, and constants are re-estimated to provide a better match between modeled 

and observed data. 




Issue C: Auto Occupancies Not Matching Observed Data 

While logit mode choice models generate auto occupancy rates as an output, highway 

only mode choice models use auto occupancy rates as an input to convert person trips to 

auto trips. 


1. Inaccurate assumed auto occupancy rates – If coefficients were estimated from survey 

data, data insufficiency or errors might result in errors in occupancy rates. If coefficients 

were transferred from another context or model, the model transferability might 

not be completely appropriate, or the original model from which the coefficients were 

transferred might have had errors. 


1. With local knowledge on typical auto occupancy rates by purpose from travel surveys, 

Census Journey-to-Work (work trips only), NCHRP Report 365 (and its upcoming 

update) or the National Household Travel Survey (NHTS), mode choice outputs 

should be checked and adjustments made to the auto mode constants/auto occupancy 

rates. 

3.3.5 Validating Assignment Models 

As noted in previous documents for this study, more time and effort is typically put into 

validating trip assignment models than any other model step. This is due in part because 

assignment is typically the bottom line in the modeling process (e.g., the number of vehicle 

trips forecasted for a new highway corridor, number of riders on a new transit route, 

etc.). Additionally, there are more measures on model performance and established standards 

for highway assignment than any other model step. 

Issue A: Areawide Overassignment of Highway Trips 


1. If the highway assignment model as a whole is loading an excessive number of trips, 

this could also result from trip production and attraction rates. 

2. Mode splits could also potentially be responsible for overassignment. 

3. For highway only models, auto occupancy factors might be the problem. 





1. Possible corrections might include rechecking or borrowing lower trip production and 

attraction rates (in the absence of survey data). 

2. Checking the reasonableness of mode splits and adjusting mode choice constants 

within acceptable levels, particularly to the drive alone mode. 

3. For highway only models, testing of other relevant auto occupancy factors can have a 

dramatic impact on the number of trips loaded on a highway network. 

Issue B: Areawide Underassignment of Highway Trips 


1. Trip production and attraction rates, mode splits, and auto occupancy rates, as 

described under Issue A. 

2. Insufficient nonhome-based trips could also be a potential source for error. 


1. See Issue A. 

2. Nonhome-based trips typically represent about 25-33 percent of daily travel. 

Increasing nonhome-based trips at special generators can help somewhat assuming 

these land uses carry a lot of weight in local trip-making. 

Issue C: Over- or Under-Assignment of Highway Trips by Facility Type/Area Type/ 

Number of Lanes 


1. Improper coding of area types, facility types, and/or number of lanes on key facilities 

comprising these categories. 

2. Variable factors are not indicative of regional travel. 

3. Free flow and/or congested speeds look suspect. 





1. If the highway assignment model is loading too many or too few trips in certain categories 

of facility type, area type, or number of lanes, network coding should be 

rechecked on corridors contributing to the assignment problem. There is some level of 

subjectivity in assigning area types and facility types to roadway links. For example, 

some roadways functionally classified as collectors have been reconstructed to operate 

more like arterial highways thus leaving the facility type somewhat open to interpretation. 

Area type models (based on density rather than personal interpretations) have 

proven effective in several models at eliminating subjectivity. 

2. FSUTMS variable factors can be adjusted by facility type, including UROAD (ratio 

between actual and practical capacity), CONFAC (peak-to-daily ratio), and the BPR 

EXP and BPR LOS (exponents used in capacity restraint). Such adjustments should be 

based on more sophisticated calculations of these capacity-related attributes and not 

arbitrarily modified solely to improve model validity for select facility type categories. 

3. Another commonly used approach is to adjust speeds or capacities for select combinations 

of area type, facility type, and number of lanes. If speed or capacity adjustments 

are made, the following rules should be adhered to: 

- Adjustments should be kept to a minimum and employed mainly as a tool for finetuning 

validation of the assignment process; 

- A logical hierarchy of free-flow speeds should be maintained (e.g., higher speeds 

on freeways than arterials); 

- Mode choice and trip distribution results should be reviewed to ensure that speed 

adjustments have not caused the balance of highway and transit speeds to be significantly 

impacted; and 

- Resulting congested speeds should be reviewed for consistency and reasonableness. 

Some models have used different approaches to avoid the above pitfalls, including 

posted speeds (with or without factoring), and capacity calculators (based on detailed 

base year intersection data on signalization and turn lanes). There is no perfect 

approach to dealing with assignment issues by roadway category but the analyst must 

be aware of the pitfalls in making model adjustments. 

Issue D: Over- or Under-Assignment of Highway Trips on Specific Links and 

Corridors 


1. Centroid connectors might not properly address assess to the corridor. 

2. Coding errors might be present for area types, facility types, and/or lanes. 

3. Corridor alignments are incorrect. 





1. If the highway assignment model is loading too few or too many trips on specific links 

and corridors in the network, adjustment of centroid locations and centroid connectors 

can be considered. Centroid locations generally reflect the geographic center of trip 

making activity within a traffic analysis zone (TAZ) and thus are relatively subjective. 

Likewise, centroid connectors also are subjective in representing central loading points 

from centroids to the surrounding roadway network. Centroids and connectors can be 

reviewed and adjusted with caution not to skew these locations too far from logic and 

to ensure that there is no negative impact on the calculation of transit access and 

egress links. 

2. Area types, facility types, and laneages should be reviewed for reasonability. Other 

network checks could include reviewing links without assignment volumes or those 

with very high volume/capacity ratios. Figure 3.13 depicts a scatterplot of estimated 

assignment volumes versus counts, including a link with a count but no assignment 

volume. 

Figure 3.13 Scatterplot of Estimated Volumes versus Traffic Counts 




3. Integrating GIS data into the network coding and refinement process will improve the 

accuracy of coding alignments. Additionally, certain facility types can be benefit from 

adjustments geared to typical trip types. Examples would include the following: 

- Freeways – Use of external-external (EE) exclusion codes on ramps to and from 

local streets, in conjunction with preloading of EE trips, can limit tourist and other 

long-distance trips to major highways as such travelers are unlikely to divert from 

congested freeway links due to a lack of familiarity with alternate routes; 

- Toll Facilities – The CTOLL parameter, roughly approximating the cost of travel 

time, can be adjusted if toll corridors as a group are over- or under-assigning. For 

specific toll plaza locations, average toll rates should be rechecked to account for 

axle-based tolls and service times can be adjusted to reflect the perceived time for 

toll collections; 

- HOV Lanes – Standard FSUTMS coding methodologies for HOV lanes are 

designed to minimize the number of short-distance trips that are willing to cross 

several lanes of traffic to travel in such special use lanes. Part of this process is the 

adjustment of penalties to achieve the proper balance of shared ride trips between 

general purpose and special use lanes, as depicted in Figure 3.14. 

- Ramps – It is important that the coding of interchanges reflects the actual use and 

configuration of on- and off-ramps. In the case of partial cloverleaf interchanges, 

prohibitors might be necessary to ensure that trips are guided on to the proper 

ramps, trips don’t use ramps for freeway trips through the crossroad, and trips 

don’t cross over traffic where access management does not allow these movements, 

as also depicted in Figure 3.14; and 

- Other Access Management Concerns – Centroid connectors for small TAZs along 

divided arterials with restricted median cuts should sometimes be prohibited from 

allowing trips to cross the roadway. CBDs and other areas with dense signal 

spacing often employ prohibitors to prevent left turn movements that lead to traffic 

backups during peak period. Identifying these locations requires some degree 

of local knowledge. 




Figure 3.14 

Penalties and Prohibitors 

Example of Coding Penalties for HOV Lanes 

General Purpose Lanes 

HOV Lanes 

General Purpose Lanes 

Example of Coding Prohibitors for Proper Access 

Would not cross traffic 

to take loop ramp 

Cannot cross median to access 

driveway (centroid connector) 

Red lines represent penalized or prohibited movements. 




Issue E: Over- or Under-Assignment of Highway Trips on Cordon Lines, Cutlines, 

and Screenlines 

Figure 3.15 depicts an example of screenlines, cutlines, and cordon lines superimposed on 

a highway network. 

Figure 3.15 Example of Screenlines, Cutlines, and Cordon Lines 


1. Problems with the regional distribution of trips. 

2. Erroneous traffic counts coded on screenline, cutline, or cordon line. 

3. Gaps in continuity of the screenline, cutline, or cordon line. 


1. As noted earlier in this section, screenline validation problems could be indicative of 

errors in the distribution of trips. Therefore, some of the adjustments and checks recommended 

for trip distribution should be considered to correct screenline validation, 

in addition to network- and path-related adjustments such as network corrections, and 

the addition of bridge penalties and prohibitors. Since cordon lines and cutlines 




sometimes represent specific area types such as central business districts (CBD) and 

beach areas, adjustments to terminal times and centroid connector speeds can be quite 

helpful. 

2. Traffic counts should be checked and verified continuously throughout the validation 

process as coding errors are easy to make. Counts in the model network can be 

checked against other sources of count data such as the Florida Traffic Information CD 

and local government count programs. Coding of traffic count stations and jurisdictional 

responsibility for each count will enhance the process of verifying count 

accuracy. 

3. Screenlines, cutlines, and cordon lines should be designed to maximize continuity. 

For example, if a screenline crosses a northbound one-way street, that same screenline 

should cross the complimentary southbound one-way street. This requires that a 

screenline number and traffic count be coded on each link. If a count is not coded, 

summed assignment results will be incomplete (i.e., there will be a missing link). 

Likewise, if the screenline number is not coded on a specific link, it will not be 

summed as part of the screenline volume. 

Other Highway Assignment Considerations – The above model adjustments apply to 

root mean square error (RMSE), volume-over-count ratios, and volume-over-count ratios 

based on vehicle-hours and vehicle-miles traveled (VHT, VMT). Ratios of VMT per person 

or dwelling unit and percent VMT by facility type or vehicle class also are useful 

measures that can help identify improper loading of trips to certain types of facilities, possible 

underestimation of select vehicle types, and mileage estimation procedures. Ratios 

of assigned volume over highway performance monitoring system (HPMS) values can be 

used to correct for inaccuracies in estimating vehicle emissions. 

Issue F: Transit Assignment Problems 


1. Error in the estimation of linked trips. 

2. Error in transit network coding. 

3. Error in access coding. 


1. If the model is replicating mode shares properly yet individual routes are generally 

under- or over-assigning, assumptions used to convert unlinked trips to linked trips 

should be rechecked. 

2. If transit assignment error is limited to specific bus lines, the user should recheck the 

coding of mode, headways, and fares. 




3. Access, egress, and transfer links along the route should be reviewed for correctness. 

Park-and-ride facilities and transit stations should be checked for assumptions such as 

parking availability, feeder bus connections, etc. New FSUTMS-Cube procedures for 

transit network microcoding could also be considered. Auto access sheds, as depicted 

in Figure 3.16, can be checked for reasonability to ensure typical users of the transit 

line can access the station. 

Figure 3.16 Example Auto Access Shed 

Interstate 

Coverage 

LOT 

Arterial 

To work destinations 

• 3.4 Special Considerations 

The processes described in Section 3.3 of this report are applicable to all model validation 

studies; however, there are additional validation concerns for special types of studies 

described in Section 2.0 of this series. Such studies include FTA New Starts, subarea, 

corridor, and site impact studies. Key differences between these study types and the 

typical validation study for long-range transportation plan (LRTP) updates include: 

• General assumption that an MPO or regional model already has been approved for 

use in the given project study area; and 

• Some level of subarea, corridor, or site-level revalidation will be warranted. 




3.4.1 Validation for FTA New Starts Projects 

Most urban areas likely to apply for FTA New Starts or Small Starts funding already have 

a validated mode choice nested logit model; however, this does not necessarily equate 

with a model that will suffice for competing against other urban areas for FTA funding. 

Appendix J provides a summary of FTA guidance in travel demand forecasting. Key considerations 

in assessing a model for FTA New or Small Starts funding also may include: 

• On-Board Survey Data – The presence, completeness, or reasonability of on-board 

transit survey data is a key consideration here. How long ago was an on-board survey 

completed? Has the transit agency introduced substantially new services since the last 

survey? How many transit surveys were completed correctly relative to the number of 

riders on the system? Were responses received from all key market segments using 

transit? 

• Transit Pathbuilding and Mode Choice Assumptions – Are the minimum paths, 

travel times, and fare matrices reasonable? Have travel-time studies been conducted 

to assess relationships between auto and transit speeds? What is the basis for mode 

choice validation targets? Are the mode choice parameters set within acceptable 

ranges as spelled out in Section 2.0? Has a matrix of on-board survey trips been generated, 

loaded, and compared against model-estimated trip tables? 

• Transit Assignment Checks – Validation should include checks on regional boardings 

by mode and time of day, the number of transfers per trip, screenline volumes, and 

boardings by route, route group, and corridor. 

3.4.2 Subarea and Corridor Validation 

Subarea and corridor studies might be located entirely within an MPO or regional model, 

overlap between adjacent models, or outside the confines of currently available models. 

The approach to validation will vary widely depending on compatibility of available 

models with the area being studied, as depicted in Figure 3.17. 




Figure 3.17 

Subarea Study Area Examples 

Statewide Model 

Urban 


Area 

Study 

Area 

Urban 


Area 1 


Study 

Area 

Urban 


Area 2 

Study 

Area 


Urban 


Area 

Study 

Area 

Study Area 

Within One 

Model Area 

Study Areas 

Within Two 

Model Areas 

Study Area 

Outside Urban 

Model Areas 

Some considerations might include the following: 

• Study Area Entirely Within Existing Model Boundary – A geographic location code 

should be added to all links within the subarea or along the corridor and validation 

statistics should be summarized for these links. Where substantial validation issues 

are discovered or the present level of network detail seems inadequate, consideration 

should be given to zone splitting, adding key circulator streets to the network consistent 

with any zone splits, and taking special traffic counts to provide a greater count 

coverage within the study area. 

• Study Area Overlaps Adjacent Models – One option is to simply ensure compatibility 

of base and future year external trips for the two models at any common external 

zones and use both models for any subsequent analyses. This includes scenarios 

where the study area is entirely within a statewide model but only partially within an 

MPO or regional model. Another option would be to stitch part of the adjacent model 

to the model encompassing the majority of the study area and conducting a revalidation 

of the model. A third option would be to merge the two models together in their 

entirety, necessitating a completely new model validation study. 

• Study Area Is Entirely Outside Limits of Available Urban and Regional Models – 

One option is to check validity and detail of the statewide model within the study 

area. Depending on proximity to the state line, urban areas, types of facilities being 

studied, trip types (e.g., trucks, HOVs, etc.) and level of detail needed, the statewide 

model might require zone splits, network edits, and some level of revalidation. It is 

strongly recommended that statewide model forecasts be compared against linear 

extrapolations of available traffic count data from the Florida DOT Traffic Information 

CD. Another option would be to create and validate a new model for the study area, 

assuming sufficient time and budget are available to complete the effort and produce 

satisfactory results. 




3.4.3 Site Impact Validation 

Most of the recommendations above for subarea and corridor validation also apply to site 

impact studies. The primary differences are related to the size, complexity, and location of 

the proposed development and methodologies employed to estimate site-generated trips. 

The emphasis of site impact validation should largely be on base year and future year 

network and socioeconomic data checking with some limited validation checks focused on 

highway assignment volume-over-count ratios on nearby links. In addition to recommendations 

provided earlier in Sections 3.3 and 3.4, the suggestions listed below can be 

used as a checklist to determine what changes, if any, should be made to the base and/or 

future year model for the purposes of site impact analyses. 

• Size, Location, and Complexity – Large developments will require splitting TAZs and 

adding internal circulation streets to the model network. Developments that include 

large quantities of different land use types should be split into multiple TAZs, as well. 

The relative proportion of internal-external trips in existing nearby zones (if the land 

uses are similar) can potentially be used as a starting point for development site 

assumptions on internal-external trips. Developments bisected by a major existing 

highway should have centroid connectors that reflect true access to such highways. If 

there are special trip purposes generated by the development (e.g., airport, university, 

tourist, or truck trips), some portion of site trips should be apportioned to these trip 

purposes if these exist in the model. Where substantial numbers of transit trips are 

generated in zones near the site, transit network access coding and impacts on mode 

choice must be addressed for development TAZs. 

• Methodologies Employed – If the model will be used to calculate actual numbers of 

trips, ITE vehicle trips should be converted to person trips by purpose and entered 

into a special generator file. Conversely, if the model is only being used to estimate 

the percent distribution of trips, estimates of dwelling units and employment can be 

entered in the zone data files with post-processing of the assignment to determine 

distribution of vehicle trips. If the model is used to look at turn volumes for nearby 

intersections (most appropriate where new facilities are planned), the analyst should 

review nearby turn volumes from the model to assess reasonableness. If the model is 

used to simulate agreed upon internally captured trips, trip table factoring will be necessary 

to achieve the desired output. Should the background traffic be estimated by 

the model, a greater focus should be placed on existing model assumptions as 

opposed to when background trips are derived from a concurrency database. If the 

model will be used to simulate site-generated trips by time of day or peak hour, trip 

table factors should be derived from ITE to convert site-based trips from daily values. 

Figure 3.18 depicts a detailed zone system for a development of regional impact (DRI) currently 

under construction in the Tallahassee region. TAZs were delineated based on land 

use categories (displayed on the map) and the internal street system. Additional guidance 

on site impact studies is provided in Section 2.0, and the Site Impact Handbook 11 , prepared 

by the Florida DOT Systems Planning Office. 




Figure 3.18 Representative Zone System for Site Impact Study 

3.4.4 Other Validation Practices 

One recently recurring theme in model validation has been to focus more attention on 

travel speeds. While adjustment of highway speeds by area type, facility type, and lanes 

categories has been a key component of model validation in Florida for the past 30 years, 

there is growing recognition that overtweaking of highway speeds might result in unrealistic 

congested speeds and might have an adverse impact on mode choice and transit 

assignment. While it is still generally acceptable to adjust speeds during validation, extra 

care must be taken to maintain logical congested highway speeds and the impacts to transit 

modeling also should be continually assessed. Where available, observed peak-period 

speeds should be measured against model congested speeds. 

Special generators, as described earlier in Section 3.3.2, can be a useful tool in model validation 

if caution is exercised. Unfortunately, in many cases, very limited information is 

available on person trips, trip purposes, vehicle occupancies, and mode splits for these 

major activity centers so estimating rates might require a trial-and-error process based on 

network traffic counts. There are some land uses for which the model will likely not generate 

sufficient trips in the absence of special generators, including: 

• Regional Parks and Beaches – Attractiveness of such uses is based on the amenities 

provided rather than number of employees, which is generally quite small. 

• Group Quarters – Dormitories, military barracks, and nursing homes do not contain 

dwelling units as defined by the U.S. Census, needed to produce home-based trips. 

Other land uses are common for special generator applications; however, special generators 

should only be implemented if there is a demonstrated need, including: 




• Airports – If a survey of airport landside travel is available, consideration can be given 

to developing a special trip purpose/submodel for airports; the other option is to use 

the number of person trips per enplanement cited in ITE Trip Generation. 12 

• Universities – The State University System Transportation Study is a reliable source on 

person trip rates for student trips based on a series of travel surveys conducted at 

Florida’s public universities. Trip rates for “commuter” universities can be considered 

for community colleges also. 

• Military Bases – As with airports and universities, military bases generate a large and 

complex number of trips; unfortunately, due to security issues, there is a limited 

amount of information available on trips produced by and attracted to these sites. 

• Large Regional Shopping Malls – Assuming plausible adjustments can be made to 

convert vehicle trips to person trips, ITE has a wealth of information on trip generation 

at shopping centers; it is best practice to limit such special generators to only the largest 

shopping malls. 

The literature review conducted for this project identified some other validation procedures 

and concepts not historically employed in Florida. These would include the 

following: 

• Dynamic Validation – A process of assessing a model’s ability to respond to change 

through sensitivity testing (e.g., how responsive is a model to network changes, etc.?). 

FTA continues to place a greater emphasis on sensitivity testing than Florida has 

historically. 

• Coincidence Ratios – A calculation of how closely trip length frequency distributions 

match between observed and model estimated conditions (as described in Sections 2.0 

and 3.3.2). 

• Scatter Plots – A visual image of the correlation between traffic counts and model estimates, 

as displayed earlier in Figure 3.13. Regression measures such as r squared can 

be computed to quantify the dispersion of counts and volumes. 

• Per Capita VMT – Dividing vehicle-miles traveled by the number of persons or households 

and comparing this to other models (also described in Section 2.0). 

• Independent or Peer Review – Review of model validation by an independent consultant 

or a peer review comprised of a team of modelers can potentially identify validation 

issues not noted by those directly involved in the study. 

• Sensitivity of Costs – Sensitivity testing of measures such as tolls, congestion pricing, 

auto operating costs, parking, and transit fares to ensure a reasonable response to 

changing conditions. 




• 3.5 Summary and Future Directions 

Section 3.0 has provided guidance in best practices for model calibration and validation. 

Issues of model implementation, estimation, and application also were addressed as part 

of a discussion on the model validation process. Guidance was provided on validation 

and adjustment of models. Special study considerations in validation also were described. 

Previous sections of this report also provided recommended benchmarks and standards of 

accuracy along with a literature review of validation practices and statistics from around 

the U.S., although a major theme of Section 3.0 is greater reliance on best practices and 

somewhat less on achieving stringent standards. Section 3.5 identifies some additional 

and future considerations on improving best practices in model validation. 

3.5.1 Transferable Model Parameters 

A key decision in model validation is where to borrow model parameters from in the 

absence of recent household travel survey data for the area being modeled. The report 

FSUTMS-Cube Framework Phase I: Model Parameters provided a number of potential 

sources to consider in identifying defensible sources for borrowed parameters. The 

Phase I report also identified current weaknesses in data to generate Florida model 

parameters. Recommendations from this report also should be considered for guidelines 

in the validation process. 

The National Cooperative Highway Research Program (NCHRP) recently started a 

research project to update the report entitled NCHRP 365 – Travel Estimation Techniques for 

Urban Planning. This NCHRP project should provide additional considerations for transferable 

parameters and validation practices. The 2008 Florida Add-On to the NHTS also 

should provide a wealth of data to use in estimating model parameters and validating 

models. 

3.5.2 The Impact of New Model Paradigms on Validation 

In setting forth new modeling procedures, it is important to keep an eye to the future. A 

recent brainstorming session sponsored by the Florida DOT identified a number of potential 

paradigm shifts for future consideration, including activity-based modeling. The 

Florida modeling community has recently become interested in activity-based modeling. 

As this is a fundamental shift from the classic four-step modeling process, the process of 

data development, model estimation, model validation, and model application is somewhat 

different as well. The primary differences in activity-based model estimation are 

that there are many more models to estimate than with the four-step process and care is 

needed to avoid overspecification as this can result in data being stretched too thin. 




Household travel surveys conducted to develop four-step models are generally adequate 

for developing activity-based models. In addition to typical procedures, data preparation 

should include classifying households by structure and life cycle, classifying persons by 

age, worker status, and arranging travel into tours and trips within tours. Optional data 

items for activity-based models could include in-home activities, substitution of on-line 

activities for travel, modeling household interactions, and activity type definitions. 

Beyond activity-based modeling, several other paradigm shifts have the potential to alter 

the process of model estimation, validation, calibration, and application, including: 

• Should model parameters shift over time in response to trip characteristics changes, in 

spite of limited data on temporal transferability? Most model parameters, such as trip 

rates and friction factors, are assumed to be static over time; 

• Vehicle availability modeling (in place of static auto availability ratios) Most models 

in Florida use the same auto availability stratifications for base and future year conditions 

in spite of clear evidence that auto ownership has increased dramatically; 

• Time-of-day modeling and time-of-day networks, where the focus shifts from validating 

with daily traffic counts to time-of-day counts; 

• Other methods to evaluate transit-oriented development and how to show the impacts 

of change. With static trip rates and friction factors and limited measures of development 

density, this becomes difficult to forecast; 

• Linking four-step and mesoscopic/microscopic modeling to quantify ITS and other 

operational impacts, potentially requiring more stringent accuracy standards for turn 

movements and weaving conditions; and 

• Integrated transportation-land use forecasting and visioning processes. Much attention 

is being placed on transportation-land use visioning which ties into similar issues 

of transit-oriented development at a larger scale. 

Risk should be considered when making paradigm shifts versus risks of continuing the 

same process. Software risks also exist with options, including open source versus new 

code versus vendors. Data requirements also will likely change as a result of future paradigm 

shifts. With the year 2010 at our doorstep, it might be a good time to look back at 

land use and travel demand forecasts previously made for the year 2010 and assess what 

is different from these projections to help identify what might need to be changed in the 

next generation of models. 




4.0 Guidelines for Model 

Application 

Unlike model validation and calibration, somewhat limited guidance has been provided 

on the application of models once established base year standards are achieved. While 

there are no established standards of acceptability for future year model applications, this 

does not absolve the practitioner from reviewing the results, comparing base and future 

year models, and assessing the logic of subsequent scenario testing. This section of the 

Final Report provides “food for thought” on the process of model application. First is a 

background discussion on the stability of model parameters over time. Next is a discussion 

of typical model applications along with forecasting considerations. The section then 

finishes with a set of future year model checks to assess the reasonableness of results. 

• 4.1 Stability of Model Parameters 

The FSUTMS-Cube/Voyager framework clearly distinguishes “model developers” from 

“model appliers.” When opening a Cube catalog file as model developer, full access is 

provided to model scripts and catalog keys. If the catalog is set for model appliers, the 

user will not be able to access model scripts or catalog keys. Password protection should 

be considered to limit access to model scripts and catalog keys to model developers. The 

reason for limiting user access to these files is that once a model is validated, model 

parameters and scripts should remain consistent for subsequent applications. There is 

certainly a valid argument, however, that trends over the past 50 years show some substantial 

changes have occurred in a variety of model parameters such as nonhome-based 

trip generation rates, trip length frequencies (friction factors), auto occupancy rates, and 

driver responses to congestion (BPR curves); however, making arbitrary changes to validated 

model parameters is not good practice. 

The general lack of consistent empirical data on these changes within any given region has 

limited the ability to trend out these behavioral changes. It is also difficult to state with 

any degree of certainty that trends from the past will continue into the future. At the time 

of this writing, fuel prices have increased dramatically in a short period of time. Recent 

surveys have indicated this situation has led to changes in travel patterns. Should gas 

prices continue to increase or remain relatively high, it is conceivable that some past 

trends of increasing trip lengths and decreasing auto occupancies might reverse. While it 

is still too soon to know the long term impacts, the American Community Survey (ACS) 

holds some level of promise in tracking work trip patterns over time since surveys are 

collected each year rather than every 10 years as with the former Census “long form.” It is 

still recommended at this time to keep validated parameters constant for base and future 

years until there is more empirical evidence on historic rates of changes in travel behavior. 




It is clearly unacceptable to assume, for example, that trip production rates will continue 

to increase at a specific rate in the future without statistics to validate this hypothesis. 

• 4.2 Typical Model Applications and Relevant Guidelines 

In the early days of travel demand forecasting, transportation models were developed as 

part of the long-range planning process. Federal funds were provided to conduct household 

travel behavior surveys and roadside origin-destination surveys for model development 

with the intent of applying the model in the preparation of a long-range 

transportation plan (LRTP) with a 20-year horizon. Access to travel demand models was 

rather limited as powerful mainframe computers were required, run times were relatively 

long, and the primary (and in many cases sole) purpose of the models was to develop an 

LRTP. As computer technology and computer power advanced, alternate proprietary 

travel demand modeling software platforms came into being and models became available 

for execution on desktop computers. As transportation planning became more complex, 

models evolved likewise and new policies often required, or at least recommended, 

the use of travel demand models for subsequent analyses. 

In the state of Florida, travel demand models are used for many applications. Some of 

these are listed below: 

• MPO LRTP Updates. 

• Comprehensive Plans. 

• SIS/FIHS Planning. 

• Campus Master Plans. 

• Concurrency Applications. 

• Development of Regional Impacts (DRIs). 

• Congestion Management Systems. 

• Air Quality and Climate Change. 

• Corridor Studies: 

- Corridor Feasibility Studies; 

- FTA New Starts/Small Starts Applications; 

- Project Development and Environment (PD&E) Studies; 

- Interstate Master Plans; 

- Interchange Justification/Modification Reports (IJR/IMR); and 

- Toll Feasibility Studies. 

Guidance on each of these model applications is provided in the paragraphs that follow. 




4.2.1 MPO LRTP Updates 

As the original purpose of model development, travel demand forecasting continues to 

serve a significant role in updating LRTPs. With each new federal transportation legislation 

the requirements for LRTPs become more extensive. LRTP modeling is unique in 

several respects: 

• Regionwide or areawide focus; 

• Multiple divergent network and land use alternatives; 

• “Official” status of the model means that assumptions are needed for the long run; and 

• In many areas, the need to consider a number of different transportation modes. 

Maintaining networks for a small-to-medium sized one county MPO might not be too 

onerous; however, maintaining consistency among a myriad of alternatives in a large 

multicounty region can get rather complex. Inevitably, network errors will be discovered 

while conducting future year model runs that will require correcting base, interim, and 

horizon year networks and possibly multiple alternatives. One approach that has been 

used effectively in Northeast Florida, and in several areas outside Florida, is the master 

network concept, whereby all network years and alternatives are stored together in one 

database. The distinct advantage is the ability to modify multiple networks at one time, 

minimizing both the time needed for network coding and the potential for errors. Florida 

DOT currently has a research project underway to develop a standard approach to developing 

and maintaining master networks, which includes looking at master networks from 

around the U.S. 

Special generator, internal-external (IE), and external-external (EE) trip forecasts are usually 

prepared during LRTP Updates. There are many different approaches to forecasting 

these special trips based to some degree on the model structure and geography. Special 

generators will not exhibit growth over the base year if the dependent variable does not 

change over time. Examples of static special generators include shopping malls, parks, 

and beaches. Conversely, universities, colleges, airports, and military installations would 

generally exhibit future year changes that should be reflected in special generator travel 

estimates. Short term growth rates might be available from Campus and Airport Master 

Plans; however, the LRTP study team will likely have to make assumptions to continue 

forecasts out to the horizon year. Growth at military installations might be classified and 

therefore, unavailable. 

External trips can be forecasted using a variety of techniques: 

• Statewide Model – Use future year estimates of freight and passenger trips on statewide 

model links approximating the regional study area boundary to establish control 

totals at each external zone. 

• Traffic Count Histories – The Florida DOT produces the Florida Traffic Information 

CD each year, which includes historic counts at all stations monitored by the Florida 

DOT. A tool provided with the CD allows for extrapolation of traffic count trends into 

the future, resulting a set of future year control totals for each external zone. 




• Other Available Forecasts – Population growth rates can be calculated using projections 

from the University of Florida Bureau of Economic and Business Research 

(BEBR). In this instance, population projections should relate to growth forecasts for 

adjacent counties at an external boundary; however, on major highways, a Florida 

statewide growth rate might also have merit. Other trend data on utilities, tag registrations, 

or business licenses could be substituted as well, depending on availability 

and completeness. 

In most cases, any of the above methods will be combined with base year IE/EE splits to 

estimate the number of trips in the EETRIPS and IEPRODS files. If external survey data 

are available for multiple points in time, it might be possible to estimate a trend in these 

splits. While it is evident that the percent of IE trips increases along with urban sprawl 

into areas outside the model boundary, absent any documented trends these splits are 

generally kept constant. Similarly, with models that assign home-based and nonhomebased 

purposes to external trips, the percent trips by purpose should generally remain 

stable from base to future year barring information to the contrary. Figure 4.1 depicts different 

trip types crossing external boundaries. 

Figure 4.1 

Example of External Trip Movements 

Internal-Internal (II) 

External-External (EE) 

Internal-External (IE) 

External Boundary 

4.2.2 Comprehensive Plans 

Comprehensive Plan updates in some cases might be similar to an LRTP approach, 

depending on the size of the local government being studied. For a County Comprehensive 

Plan, the MPO LRTP model can be used to provide input with only minimal changes. For 

small cities and sector plans, the modeling focus is at a finer grain and may require 

splitting zones and adding some local streets that would not be that important on a 

regional level. It might also be useful to code unique identifiers (e.g., geographic location 

codes) such that origins and destinations within a specific jurisdiction can be readily 




identified on the network. Converting comprehensive land use maps into socioeconomic 

data files is an art unto itself and could be open to several interpretations. Such assumptions 

could be adjusted iteratively based on findings from early model runs. Final 

assumptions such as dwelling units per acre or employment per square foot should be 

sufficiently documented. 

4.2.3 Strategic Intermodal System (SIS) Plans 

With a strong emphasis on intermodal travel, the forecasting of freight movements 

becomes quite important with studies of SIS corridors. A “multiple model” approach 

would be appropriate, whereby the statewide model is used to forecast freight truck volumes 

and long distance passenger trips while regional models are used to estimate local 

truck and auto movements. This approach has been employed successfully as part of the 

SIS New Corridors Initiative, I-75 North Florida Master Plan, and the Northwest Florida 

Transportation Corridor Authority. Since the vast majority of freight trucks travel on SIS 

corridors and connectors, the statewide model should have sufficient network detail to 

simulate these movements. Even though the statewide model does estimate nonfreight 

trucks, the limited network could cause problems inside an urbanized area, thus providing 

rationale for using regional models for these trips. Figure 4.2 displays freight tonnage 

desire lines from the Florida statewide model used in SIS corridor planning. 

Figure 4.2 Freight Tonnages for Use in SIS Corridor Planning 




4.2.4 Campus Master Plans 

State universities are required by Florida Statutes to periodically update their Campus 

Master Plans. The State University System (SUS) Transportation Study, referenced in 

Section 3.0 of this report, was conducted to provide information on the travel behavior of 

students, faculty, and staff on each state university campus. SUS trips are typically 

entered as special generators and growth factors are typically supplied by University 

planning staff to forecast these trips to a horizon year. Unfortunately, campus master 

planning only requires a 10-year horizon, which generally does not provide for official 

student enrollment projections to the LRTP horizon year. Allocating future year trips to 

campus TAZs requires some creativity as enrollment is not estimated below the campus 

level. Building square footage can be summed for each TAZ to use in allocating trips 

throughout the campus. Since universities are encouraging students to use alternative 

modes of transportation, it is very important to reassess the TAZ system and focus on 

micro-coding of transit access. 

4.2.5 Concurrency Applications and DRIs 

Best practices for DRIs and concurrency applications were provided earlier in Section 3.4.3 

on site impact analyses. The majority of these recommendations were directed at future 

year model applications. Assumptions on future trip generation, distribution, mode split, 

and internal capture must be consistent with general practice. 

4.2.6 Congestion Management System (CMS) Plans 

CMS plans typically focus on short term planning strategies and often base recommendations 

on existing traffic count databases. Travel demand models can be used to test the 

implications of travel demand management (TDM) strategies, either through trip table 

manipulations or by interfacing with other sketch planning models. This approach allows 

for quick response testing of how much impact various TDM strategies might have on 

congestion, VMT, and transit ridership among other measures. Figure 4.3 displays results 

from a sketch planning exercise for the 2030 Atlanta Regional Transportation Plan that 

involved research on trip reductions from a variety of strategies. Trip table adjustments, 

based on expected trip reductions, were used to simulate impacts to the region’s transportation 

system. 




Figure 4.3 Sketch Planning Scenario Testing 

2030 Needs Plan 2030 Transit 2030 Land Use 2030 Congestion Pricing 

VMT VHT VMT VHT VMT VHT VMT VHT 

185.9 8.6 178.0 7.7 178.6 7.9 183.1 8.0 

Note: 

Trip table adjustments made to reflect a) doubling transit, b) land use intensification, and 

c) parking fees. 

4.2.7 Air Quality and Climate Change 

Travel demand forecasts have been used to assess air quality conformity for many years. 

A special FORTRAN routine known as EMIS was written by the Florida DOT to interface 

FSUTMS with the Environmental Protection Agency’s (EPA) Mobile software. Base year 

estimates of vehicle-miles traveled (VMT) were compared against base year VMT estimates 

from the Highway Performance Monitoring System (HPMS) to calculate an EMIS 

factor for use in adjusting model estimated VMT to better replicate observed HPMS-estimated 

VMT. While all counties in Florida presently conform to air quality standards set 

for nitrous oxides (NOx) and volatile organic compounds (VOCs), there are a number of 

counties in Florida that have been identified as not meeting new standards for ozone. 

Therefore, it is possible that the EMIS program might get resurrected and modified for use 

in FSUTMS-Cube/Voyager for ozone calculations. This might also require that MPOs 

once again prepare socioeconomic forecasts for a number of interim years. Since interim 

year socioeconomic data are usually interpolated from the base and horizon years, more 

time and effort should be expended on checking these data at the zonal level to ensure 

credibility of these estimates. 

Climate change is now on the minds of many government agencies. Along those lines, it 

is important that climate change discussions should not just focus on the greenhouse gas 

emissions from transportation. Travel demand models already have been through the air 

quality conformity process and practitioners know how to model emissions from vehicles. 

While the effects on climate are considerably different, using travel demand models to 

forecast the emissions of carbon dioxide is not that different from the established practice 

of forecasting the emissions of carbon monoxide. The paradigm shift is likely to be 

focused on what climate change will do to travel demand forecasting models. Some 

examples could include the following: 

• How might the socioeconomic data driving the models change (snow birds returning 

to former snow belt)? 

• How might the behavior survey from the past and embedded in the TDF model coefficients 

and equations change as climate gets hotter or wetter? 




• How might the transportation facilities themselves, the operating characteristics of 

costs and time, or even the availability of transportation facilities be affected? 

• How might the questions asked of travel demand models change and present new 

emphases such as risk assessment of facilities or forecasts, evacuation plans, etc.? 

• How are model parameters different by climate today? 

• Might transportation corridors some day be under rising sea water, and what does 

that do to travel choices? 

Approaches to model application might change in response to the need to address questions 

such as these. 

4.2.8 Corridor Studies 

There are many different types of corridor studies, each with different requirements. 

Corridor feasibility studies typically analyze proposed highway and transit corridors to 

assess the viability of proposed projects. Other participants in the planning process will 

need ridership projections for transit projects and traffic forecasts for highway projects to 

determine the viability of such projects. As discussed elsewhere in this report, demand 

forecasting procedures used for transit projects seeking federal funding will be thoroughly 

scrutinized by the FTA. Highway projects proposed for toll funding must pass a litmus 

test in order to sell bonds, requiring carefully documented assumptions on exiting congestion, 

travel time estimates, competing facilities, development growth, and forecasted 

trip patterns on the proposed toll road. Model results should be backed up with on-board 

surveys for transit projects and origin-destination surveys for toll highways. 

PD&E Studies, Interstate Master Plans, and Interchange Justification Reports (IJR) require 

converting model forecasts to design hour volumes (DHVs). Off-model assumptions 

regarding K (100 th highest hour factor), D (directional distribution during peak), and T 

(truck) factors are applied against model outputs to estimate DHVs. It is important that 

all parties involved understand the limitations of the model as some combination of travel 

demand forecasts and existing traffic counts will be needed to estimate intersection turn 

movements and highway capacity. Interstate Master Plans will typically require a focus 

on the coding and simulation of future year trips in high occupancy vehicle (HOV) lanes. 

Since Florida has a very limited network of existing HOV lanes, base year model validation 

may not be an option but rather will require sensitivity testing and assessment of the 

HOV/SOV splits on general purpose and special use lanes based on available statistics 

from Southeast Florida or elsewhere in the U.S. In a similar manner, micro-coding of 

ramps and sensitivity testing is critical to the IJR process. 




• 4.3 Model Application Checks 

In conclusion, while statistical accuracy standards are not as prevalent with model applications 

when compared to model calibration and validation, this does not preclude the 

importance of logic checks in reviewing model forecasts. Some examples of model application 

checks would include the following: 

• Review logic of demographic forecasts at the regional and subarea level; 

• Generate and review color-coded plots of highway network characteristics such as 

area type, facility type, and number of lanes; 

• Compare base and future year trip productions and attractions by purpose at the 

regional and subarea level; 

• Compare base and future year trip distribution patterns at the district level using 

tables or desire lines; 

• Review the logic of changes in mode splits resulting from scenario testing that would 

seemingly benefit one mode over another; and 

• Compare traffic estimates on specific corridors and screenlines between base and 

future years and build and no-build conditions to ensure the results make sense. 

When evaluating alternative scenarios, measures of effectiveness should be defined that 

can be estimated using the travel demand forecasting model. As with the above comparisons, 

output statistics should be reviewed for logic based on the composition of one alternative 

versus another, possibly a baseline or “no build” alternative. Some commonly used 

metrics used in future year scenario testing include the following: 

• Socioeconomic growth; 

• Growth in work trips; 

• Changes in trip length frequency; 

• Growth in transit ridership; 

• VMT; 

• VHT; and 

• Volume-over-capacity or level-of-service. 

Figure 4.4 provides an example of statistical comparisons among different years and different 

alternatives. As shown in this example, VMT typically increases over a period of 

years but remains fairly stable among different alternatives from the same year. Such an 

analysis is important as a logic check and to determine which groups of project help 

achieve community goals and objectives. 




Figure 4.4 Comparative Analysis of Alternatives 

Base Year - 2000 

2005 

2015 

2025 

2030 E + C 

2030 Highway Emphasis 

2030 Transit Emphasis 

2030 Alternative Land Use Needs 

2030 Adopted Needs 

2030 Cost Feasible 

32,084 

39,418 

44,787 

51,026 

54,891 

55,429 

55,288 

54,731 

54,630 

53,835 

0 10,000 20,000 30,000 40,000 50,000 60,000 

Vehicle Miles Traveled (1000s) 

Model Year 

FCMPO 2030 LRTP Comparisons of Vehicle Hours Delay 


2005 

2015 

2025 

2030 E + C 






476 

753 

836 

683 

679 

781 

778 

937 

961 

2043 

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 

Vehicle Hours Delay (1000s) 

Model Year 


2005 

2015 

2025 

2030 E + C 






FCMPO 2030 LRTP Comparisons of Percentage of LOS F* 

15.24 

18.25 

16.92 

19.42 

14.76 

16.66 

12.93 

13.50 

21.37 

30.85 

0 5 10 15 20 25 30 35 

(Percentage) 


Appendix A 

Literature Review Bibliography



Literature Review Bibliography 

1. Lima & Associates. Lincoln MPO Travel Demand Model. Draft Model Documentation. 

Date Unknown. 

2. Schimpeler-Corradino Associates. Urban Transportation Planning Model Update Task B: 

Review and Refinement of Trip Generation Model. Prepared for Florida Department of 

Transportation, June 1980. 

3. COMSIS Corporation. Urban Transportation Planning Model Update Task C: Develop 

Standardized Distribution and Assignment Models. Prepared for Florida Department of 

Transportation, October 1981. 

4. Schimpeler-Corradino Associates. Urban Transportation Planning Model Update Task H: 

Modal Split Refinement and Calibration Standards. Prepared for Florida Department of 

Transportation, 1984. 

5. JHK & Associates. Report 255 – Highway Traffic Data for Urbanized Area Project Planning 

and Design. National Cooperative Highway Research Program, December 1982. 

6. Federal Highway Administration. Calibrating and Adjustment of System Planning 

Models. December 1990. 

7. JHK & Associates and Dowling Associates. State of California Department of Transportation 

Travel Forecasting Guidelines. Prepared for Federal Highway Administration, November 

1992. 

8. Deakin, Harvey, Skabardonis, Inc. Manual of Regional Transportation Modeling Practice 

for Air Quality Analysis. Prepared for National Association of Regional Councils, 1993. 

9. Louis Berger & Associates. The Rhode Island Statewide Travel Demand Forecasting Model. 

Prepared for Rhode Island DOT. Circa early 1990s. 

10. Parsons, Brinckerhoff, Quade & Douglas, Inc. and Kittleson & Associates, Inc. Travel 

Demand Model Development and Application Guidelines. Prepared for Oregon 

Department of Transportation. Revised, June 1995. 

11. Sam Granato, Linn County (Iowa) Regional Planning Commission. Calibration of 

Traffic Forecasting Models in Small Urban Areas: A Cedar Rapids Metropolitan Area Case 

Study. 1995. 

12. Cambridge Systematics, Inc., COMSIS Corporation, and University of Wisconsin, 

Milwaukee. Quick Response Freight Manual. Prepared for Federal Highway 

Administration, 1996. 

13. Post, Buckley, Schuh, and Jernigan, Inc. FSUTMS Interactive Users’ Library CD. 

Prepared for Florida Department of Transportation, 1996-1998. 

Florida DOT, Systems Planning Office A-1

FSUTMS-Cube Framework Phase II: 


14. Barton-Aschman Associates, Inc. and Cambridge Systematics, Inc. Model Validation 

and Reasonableness Checking Manual. Prepared for Travel Model Improvement 

Program/Federal Highway Administration, February 1997. 

15. Barton-Aschman Associates, Inc. and Transportation Research Board. Report 365 – 

Travel Estimation Techniques for Urban Planning. National Cooperative Highway 

Research Program, 1998. 

16. University of Wisconsin, Center for Urban Transportation Studies. Guidebook on 

Statewide Travel Forecasting. Prepared for Federal Highway Administration, March 

1999. 

17. Iowa Department of Transportation and Center for Transportation Research and 

Education. SIMPCO Traffic Modeling Workshop. Presentation, February 2000. 

18. East-West Gateway Coordinating Council. Transportation Redefined II and Transportation 

Improvement Program for FY 2001 – 2003, Air Quality Conformity Finding and Documentation. 

August 2000. 

19. Houston-Galveston Area Council. Regional Travel Models – 1995 Model Validation and 

Documentation Report. February 2001. 

20. Mohamed Abdel-Aty and Hassan Abdelwahab, University of Central Florida. Calibration 

of Nested-Logit Mode-Choice Models for Florida. Prepared for Florida Department of 

Transportation, November 2001. 

21. David Pearson, Patricia Ellis, and Stephen Farnsworth, Texas Transportation Institute. 

Calibration of a Past Year Travel Demand Model for Model Evaluation. Prepared for Texas 

Department of Transportation, October 2002. 

22. DKS Associates. Sacramento Regional Travel Demand Model Version 2001. Prepared for 

Sacramento Area Council of Governments, March 2002. 

23. Gannett Fleming, Inc. and Cambridge Systematics, Inc. Tampa Bay Regional Planning 

Model Phase V Technical Report No. 1, Validation of TBRPM Version 4.0. Prepared for 

Florida Department of Transportation, April 2002. 

24. Thomas Rossi, Cambridge Systematics, Inc. Validation Standards for Massachusetts 

Statewide Model. Memo to Massachusetts Statewide Model files, September 2002. 

25. University of South Florida, Cambridge Systematics, Inc., Gannett Fleming, Inc., and 

The Corradino Group. Time of Day Modeling Procedures for Implementation in FSUTMS. 

Prepared for Florida Department of Transportation, Circa 2002. 

26. Cambridge Systematics, Inc. and Urban Analytics, Inc. Model Calibration and Validation. 

Prepared for Puget Sound Regional Council, March 2003. 

A-2 Florida DOT, Systems Planning Office



27. Fang Zhao, Lee-Fung Chow, Min-Tang Li, and Albert Gan, Florida International 

University. Development of FSUTMS Lifecycle and Seasonal Resident Trip Production 

Models for Florida Urban Areas. Prepared for Florida Department of Transportation, 

March 2003. 

28. Jared Ulmer, Arkopal Goswami, John Miller, and Lester Hoel. Residential Trip Generation: 

Ground Counts Versus Surveys. Virginia Transportation Research Council, June 2003. 

29. Chang-Jen Lan, University of Miami and Albert Gan, Florida International University. 

Incorporating Feedback Loop into FSUTMS for Model Consistency. Prepared for Florida 

Department of Transportation, September 2003. 

30. Albert Gan, Rax Jung, and Min-Tang Li, Florida International University and Chang- 

Jen Lan, University of Miami. Incorporating Variable Peak-to-Daily Ratios into FSUTMS 

to Reduce Assignment Errors. Prepared for Florida Department of Transportation, 

September 2003. 

31. National Capital Region Transportation Planning Board and Metropolitan Washington 

Council of Governments. Regional Travel Forecasting Models: A Survey of the Modeling 

Practice at 11 Medium-Sized Metropolitan Planning Organizations in the U.S. October 

2003. 

32. Cambridge Systematics, Inc. in Association with Fehr & Peers. Wasatch Front Regional 

Council Model Sensitivity Testing Final Report. Prepared for Utah Department of 


33. Cambridge Systematics, Inc. Northeast Florida Regional Planning Model Technical Report 

No. 2, 2000 Model Validation. Prepared for Florida Department of Transportation, 

December 2003. 

34. Thomas Rossi, Cambridge Systematics, Inc. SCAG Model Validation. Memo to SCAG 

Model Improvement Program team members, December 2003. 

35. Baltimore Metropolitan Council. Baltimore Region Travel Demand Model for Base Year 

2000. January 2004. 

36. Cambridge Systematics, Inc., Arun Chatterjee, Ph.D., and Harry Cohen, Ph.D. 

Accounting for Commercial Vehicles in Urban Transportation Models. Prepared for Federal 

Highway Administration, March 2004. 

37. Bernardin, Lochmueller & Associates, Inc. Knoxville Regional TransCAD Travel Demand 

Model Development. Prepared for Knoxville Regional Transportation Planning 

Organization, March 2004. 

38. Iowa Department of Transportation. U.S. DOT Travel Model Improvement Plan (TMIP) 

Report on Findings of the Peer Review Panel. March – April 2004. 




39. Metropolitan Transportation Commission. 2000 Base Year Validation of Travel Demand 

Models for the San Francisco Bay Area (BAYCAST-90). May 2004. 

40. Post, Buckley, Schuh & Jernigan, Inc. 2004 Model Update Documentation. Prepared for 

Nashville Area MPO, November 2004. 

41. Caliper Corporation. MACOG TransCAD Travel Demand Model Calibration Results. 

Prepared for Michigan Area Council of Governments, December 2004. 

42. DKS Associates. Travel Model Development and Refinement – Trip Generation. Prepared 

for Puget Sound Regional Council, December 2004. 

43. Fang Zhao and Min-Tang Li, Florida International University. Calibration of Highway/ 

Transit Speed Relationships for Improved Transit Network Modeling in FSUTMS. Prepared 

for Department of Transportation, March 2005. 

44. Cambridge Systematics, Inc. and Post, Buckley, Schuh & Jernigan, Inc. U.S. 64-NC 49 

Corridor Study Travel Demand Model Calibration. Prepared for North Carolina 

Department of Transportation, April 2005. 

45. Cambridge Systematics, Inc. Chattanooga-Hamilton County/North Georgia (CHCNGA) 

TransPlan 2030, CHCNGA Year 2000 Model Validation. Prepared for CHCNGA 

Transportation Planning Organization and Day Wilburn Associates, Inc., May 2005. 

46. Post, Buckley, Schuh & Jernigan, Inc. Panama City Urban Area Transportation Study 

(PCUATS) 2030 Long-Range Transportation Plan Update – Model Validation. Prepared for 

Florida Department of Transportation, May 2005. 

47. Cambridge Systematics, Inc. in Association with Post, Buckley, Schuh & Jernigan, Inc. 

Polk County year 2000 Model Validation Final Technical Report. Prepared for Polk County 

Transportation Planning Organization, June 2005. 

48. Atlanta Regional Commission. The Travel Forecasting Model Set for the Atlanta Region 

2002-2003 Documentation. April 2005. 

49. Day Wilburn Associates, Inc. Montgomery Study Area LRTP Update Model Development 

Report. Prepared for Montgomery, AL MPO. June 2005. 

50. Chang-Jen Lan, Jibing Li, and Xiaojun Gu, University of Miami and Min-Tang Li, Rax 

Jung, and Albert Gan, Florida International University. Accuracy Standards and Data 

Coverage Requirements for Model Validation in FSUTMS. Prepared Florida Department 

of Transportation, October 2005. 

51. Genesee County Metropolitan Planning Commission Staff. Genesee County Urban 

Travel Demand Model Calibration and Validation Report. May 2006. 

52. Compass Community Planning Association of Southwest Idaho. 2002 Travel Demand 

Forecast Model Calibration Report for Ada and Canyon Counties. June 2006. 

A-4 Florida DOT, Systems Planning Office



53. Cambridge Systematics, Inc., Mark Bradley Research & Consulting, SYSTRA 

Consulting, Inc. Bay Area/California High-Speed Rail Ridership and Revenue Forecasting 

Study. Prepared for Metropolitan Transportation Commission, July 2006. 

54. The Corradino Group. Treasure Coast Regional Planning Model 2000 Model Update – 

Technical Report 2, Model Calibration and Validation. Prepared for Florida Department of 

Transportation, District IV, July 2006. 

55. HNTB and AECOM Consult. Central Florida Regional Planning Model, Technical 

Memorandum No. 2 Model Calibration and Validation. Prepared for Florida Department 

of Transportation, District V, July 2006. 

56. Mike Bitner, Council of Fresno County Governments. The Fresno COG Travel Demand 

Forecasting Model. Presentation, November 2006. 

57. Cambridge Systematics, Inc. and Edwards and Kelcey. GBNRTC Travel Model 

Refinement. Draft. December 2006. 

58. University of Tennessee Center for Transportation Research. Minimum Travel Demand 

Model Calibration and Validation Guidelines for State of Tennessee. 2006. 

59. Thomas Rossi, Cambridge Systematics, Inc. Model Validation Seminar. Prepared for 

Federal Highway Administration. Presentation, January 2007. 

60. Cambridge Systematics, Inc. UATS Travel Demand Model Calibration. Prepared for Ann 

Arbor-Ypsilanti Urban Area Transportation Study, December 2002. 

61. Kimley-Horn and Associates, Inc., Cambridge Systematics, Inc., and HNTB. Memphis 

Travel Demand Model: Technical Memorandum 11 – Memphis Model Calibration. 

Unpublished draft report prepared for the Memphis MPO, 2007. 

62. Kimley-Horn and Associates, Inc., Cambridge Systematics, Inc., and HNTB. Memphis 

Travel Demand Model: Technical Memorandum 6 – Mode Choice. Unpublished draft 

report prepared for the Memphis MPO, 2007. 

63. Federal Transit Administration. Travel Forecasting for New Starts Proposals – June 2006. 

FTA web site: http://www.fta.dot.gov/planning/newstarts/planning_environment_ 

7275.html. 

64. Federal Transit Administration. Travel Forecasting for New Starts Proposals – September 

2006. FTA web site: http://www.fta.dot.gov/planning/newstarts/ 

planning_environment_7275.html. 


Appendix B 

Literature Review Summary of Available Standards



Checklist of Available Validation Standards from Literature: Trip Generation 

Statistic Standard Benchmark Document(s) Cited 

Population/Employment Ratio 40-60% Iowa DOT Peer Review (39) 

Person Trips/Person 3.64 – 3.87 Validation and Reasonableness (14) 

Person Trips/Person (Urban) 2.54 University of Wisconsin (16): Kentucky Statewide Model/NPTS 

Person Trips/Person (Rural) 2.57 University of Wisconsin (16): Kentucky Statewide Model/NPTS 

Person Trips/HH 8.5 – 10.5 University of Tennessee (59) 

Person Trips/HH 6.8 – 12.4 Validation and Reasonableness (14), NCHRP 365 (15) 

Person Trips/DU 14.1/14.5/11.8/7.6 Calibration and Adjustment 6) – population sizes: 50-100/100-250/250-750/750k+ 

Person Trips/DU 9.2/9.0/8.6/8.5 NCHRP 365 (15) – population sizes: 50k-200k/200k-500k/500k-1M/1M+ 

Vehicle Trips/DU 9.15 VTRC (29) 

Resident/Commercial Neighborhood Trips 78.5%/21.5% VTRC (29) 

Person Trips/Employee 1.29 – 1.40 Validation and Reasonableness (14) 

TAZs/Population 1 TAZ/1k Population Iowa DOT Peer Review (39) 

Person Trips/TAZ 25k or less Iowa DOT Peer Review (39) 

Percent Trips by Purpose – HBW* 18% – 27% University of Tennessee (59) 

Percent Trips by Purpose – HBNW 47% – 54% University of Tennessee (59) 

Percent Trips by Purpose – NHB 22% – 31% University of Tennessee (59) 

Percent Trips by Purpose – HBW* 17% – 23% Validation and Reasonableness (14), NCHRP 365 (15) 

Percent Trips by Purpose – HBNW 52% – 60% Validation and Reasonableness (14), NCHRP 365 (15) 

Percent Trips by Purpose – NHB 23% – 25% Validation and Reasonableness (14), NCHRP 365 (15) 

Unbalanced Attractions/Productions 0.90-1.10 Validation and Reasonableness (14) 

External-External Trip Percentages** 21% Calibration and Adjustment 6) – population size: 50-100k 

External-External Trip Percentages** 15% Calibration and Adjustment 6) – population sizes: 100-250k 

External-External Trip Percentages** 10% Calibration and Adjustment 6) – population sizes: 250-750k 

External-External Trip Percentages** 4% Calibration and Adjustment 6) – population sizes: 750k+ 

External-External Trip Percentages** 5-20% NCHRP 365 (15) – sample of 6 different models of different population sizes 

External-External Trips – Interstate** 77%/67%/46% NCHRP 365 (15) – population sizes: 25k/50k/100k 

External-External Trips – Pr Arterial** 40%/30%/9% NCHRP 365 (15) – population sizes: 25k/50k/100k 

External-External Trips – Mi Arterial** 24%/13%/0% NCHRP 365 (15) – population sizes: 25k/50k/100k 

Percent External Trips** 10-20% Iowa DOT Peer Review (39) 

*HBW percents listed here might be outdated 

**External-external percents are very general; values outside these ranges are not necessarily indicative of a validation problem 

Florida DOT, Systems Planning Office B-1



Checklist of Available Validation Standards from Literature: Trip Distribution 

Statistic Standard Benchmark Parameters Document(s) Cited 

Average Free-Flow Speeds – Urban 50/20-35/15/10 Validation and Reasonableness (14) – Freeway/Arterial/Collector/Centroid 

Conn 

Average Free-Flow Speeds – Suburban 55/25-40/20/15 Validation and Reasonableness (14) – Freeway/Arterial/Collector/Centroid 

Conn 

Average Free-Flow Speeds – Rural 60/35-45/25/20 Validation and Reasonableness (14) – Freeway/Arterial/Collector/Centroid 

Conn 

Average Free-Flow Speeds – CBD 60/45/35,35,30/15 NCHRP 365 (15) – Freeway/Expy/Pr Div,Ma Div,Mi Arterial/Collector 

Average Free-Flow Speeds – Suburban 60/45/45,45,35/30 NCHRP 365 (15) – Freeway/Expy/Pr Div,Ma Div,Mi Arterial/Collector 

Average Free-Flow Speeds – Rural 60/55/50,45,35/30 NCHRP 365 (15) – Freeway/Expy/Pr Div,Ma Div,Mi Arterial/Collector 

Terminal Times – Urban 2 (Prod), 4 (Attr) Validation and Reasonableness (14) – Initial Terminal Times 

Terminal Times – Suburban 1 (Prod), 2 (Attr) Validation and Reasonableness (14) – Initial Terminal Times 

Terminal Times – Rural 1 (Prod), 1 (Attr) Validation and Reasonableness (14) – Initial Terminal Times 

Terminal Times – CBD 5 NCHRP 365 (15) 

Terminal Times – CBD Fringe 4 NCHRP 365 (15) 

Terminal Times – Urban 3 NCHRP 365 (15) 

Terminal Times – Suburban 2 NCHRP 365 (15) 

Terminal Times – Rural 1 NCHRP 365 (15) 

Average Trip Length – HBW* 11.2 – 35.4 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBSH* 8.6 – 18.7 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBSC 8.9 – 15.9 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBO* 10.4 – 17.3 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBNW 10.6 – 15.3 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – NHB* 8.1 – 17.1 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBW* 15 – 20 mins. Calibration and Adjustment 6) – Large Populations (unspecified) 

Average Trip Length – HBNW 13 – 17 mins. Calibration and Adjustment 6) – Large Populations (unspecified) 

Average Trip Length – NHB* Calibration and Adjustment 6) – Large Populations (unspecified) 

Average Trip Length – Transit Trips +/-5% FDOT Model Update Task H 4) Acceptable Error 

Mean Trip Length 

+/-3% of 

FDOT Model Update Task C (3) 

observed 

Trip Length Frequency Distribution visual 


comparison 

Trip Length Frequency Distribution +/- 5% of 

Cambridge Systematics’ Model Validation Seminar (60) 

observed 

Coincidence Ratios by Purpose GT 65%-70% Cambridge Systematics’ Model Validation Seminar (60) 

Percent Intrazonal 3-5% of total 

Iowa DOT Peer Review (39) 

trips 

Percent Intrazonal 

+/-3% of 

Massachusetts Statewide Model Validation Memo – CS (23) 

observed 


+/-5% of 


observed 

Ratio of Productions/Attractions +/-10% Iowa DOT Workshop (17) 

*See also NHTS statistics found in Phase I Model Parameters report; NHTS Add-On will provide better Florida-specific benchmarks 

B-2 Florida DOT, Systems Planning Office



Checklist of Available Validation Standards from Literature: Mode Choice 


Auto Occupancy Rates – HBW 1.11/1.12/1.13/1.11 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k-999k/1M+ 

Auto Occupancy Rates – HBSH 1.44/1.48/1.45/1.48 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k-999k/1M+ 

Auto Occupancy Rates – HBSR 1.66/1.72/1.66/1.69 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k-999k/1M+ 

Auto Occupancy Rates – HBO 1.67/1.65/1.65/1.66 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k-999k/1M+ 

Auto Occupancy Rates – NHB 1.66/1.68/1.66/1.64 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k-999k/1M+ 

Total Area Transit Trips +/-1% FDOT Model Update Task H 2) Acceptable Error 

Trips Entering the Central Area +/-2.5% FDOT Model Update Task H 2) Acceptable Error 

Mode Splits 

within 2 percentage 

SCAG model validation memo (35) 

points of calibration 

targets 

Elasticity of demand with respect to -0.6 to -0.1 SCAG model validation memo (35) 

level of service variables 

IVT parameter 

-0.02 to -0.03 FTA 

-0.01 to -0.05 CS summary of existing U.S. models 

Ratio: OVT/IVT parameters 

2 to 3 FTA 

1.5 to 3 HBW CS summary of existing U.S. models 

2 to 7 NW CS summary of existing U.S. models 

Implied value of time 

25%-33% of income FTA 

$2 to $5 HBW CS summary of existing U.S. models 

$0.20 to $5 NW CS summary of existing U.S. models 




Checklist of Available Validation Standards from Literature: Trip Assignment 


Freeway Volume-over-Count +/- 7% Calibration and Adjustment (6), Validation and Reasonableness (14), 

University of Tennessee (59), NARC (8) 

Principal Arterial Volume-over-Count +/- 10% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Minor Arterial Volume-over-Count +/- 15% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Collector Volume-over-Count +/- 25% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Frontage Rd Volume-over-Count +/- 25% Calibration and Adjustment (6), University of Tennessee (59), NARC (8) 

Freeway Peak Volume-over-Count 75% of links @ +/-20% Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10) 

Freeway Peak Volume-over-Count 50% of links @ +/-10% Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10) 

Major Arterial Pk Volume-over-Count 75% of links @ +/-30% Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10), 

Memphis Validation – CS (61) 

Major Arterial Pk Volume-over-Count 50% of links @ +/-15% Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10), 


Freeway Volume-over-Count +/- 7% Cambridge Systematics’ Model Validation Seminar (60) – Massachusetts 

Standards (23) 

Arterial Volume-over-Count +/- 15% Cambridge Systematics’ Model Validation Seminar (60) – Massachusetts 


Collector Volume-over-Count +/- 20% Cambridge Systematics’ Model Validation Seminar (60) – Massachusetts 


Freeway Volume-over-Count +/- 6% Michigan DOT Urban Model Calibration Targets-1993, University of 

Tennessee (59), CS MVS (60) 

Principal Arterial Volume-over-Count +/- 7% Michigan DOT Urban Model Calibration Targets-1993, University of 


Minor Arterial Volume-over-Count +/- 10% Michigan DOT Urban Model Calibration Targets-1993, University of 


Collector Volume-over-Count +/- 20% Michigan DOT Urban Model Calibration Targets-1993, University of 





Checklist of Available Validation Standards from Literature: Trip Assignment (continued) 


VMT % Distribution – Freeways* 18-23%/33-38%/40% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 

and Adjustment (6), UT(59) 

VMT % Distribution – Pr. Arterials* 37-43%/27-33%/27% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 


VMT % Distribution – Mn. Arterials* 25-28%/18-22%/18-22% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 


VMT % Distribution – Collectors* 12-15%/8-12%/8-12% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 


Assigned VMT-over-Count Areawide +/-5% FDOT Model Update Task C (3), Iowa DOT Peer Review (39), Validation and 

Reasonableness (14) 

Assigned VHT-over-Count Areawide +/-5% FDOT Model Update Task C (3), Iowa DOT Peer Review (39) 

Assigned VMT-over-Count by FT/AT/NL +/-15%,+/-25% FDOT Model Update Task C (3): +/- 15% VMT>100k, +/-25% VMT20k, +/-25% VHT 50k 

Screenline Volume/Count +/- 20% FDOT Model Update Task C (3): Screenline volume < 50k 

Screenline Volume/Count by Volume +/- 20%-65% Figure contained in Calibration and Adjustment (6), Validation and 


Screenline Volume/Count +/- 10% Massachusetts (24), BMC (35), Iowa DOT Peer Review (39), Memphis (61) 

Cutline Volume/Count +/- 15% Memphis (61) 

Cutline Volume/Count +/- 10% Iowa DOT Peer Review (39), Cambridge Systematics’ Model Validation 

Seminar (60) – MI Standards 

Screenline Volume/Count +/- 5% Cambridge Systematics’ Model Validation Seminar (60) – Michigan Standards 

Screenline Volume/Count +/- 10% – +/- 20% Cambridge Systematics’ Model Validation Seminar (60) – FHWA Standards 

*General guidance; comparison against observed VMT is a better measure 






Deviation from Counts: LT 1,000 AADT 200% Michigan DOT Urban Model Calibration Targets-1993, University of 

Tennessee (59) 

Deviation from Counts: 1,000-2,500 AADT 100% Michigan DOT Urban Model Calibration Targets-1993, University of 










Deviation from Counts: GT 50,000 AADT 10% Michigan DOT Urban Model Calibration Targets-1993, University of 


Deviation from Counts: LT 1,000 AADT 60% Validation and Reasonableness (14), University of Tennessee (59) 

Deviation from Counts: 1,000-2,500 AADT 47% Validation and Reasonableness (14), University of Tennessee (59) 





Deviation from Counts: GT 50,000 AADT 21% Validation and Reasonableness (14), University of Tennessee (59) 

RMSE: LT 5,000 AADT 116% Oregon DOT (10), University of Tennessee (59) – percentages rounded to 

whole numbers 

RMSE: 5,000-9,999 AADT 43% Oregon DOT (10), University of Tennessee (59) – percentages rounded to 

whole numbers 


whole numbers 


whole numbers 







whole numbers 


whole numbers 

RMSE Areawide





Percent Error – 10k-35k volume +/-25% Iowa DOT Workshop (17) 

Percent Error – 35k-60k volume +/-15% Iowa DOT Workshop (17) 

Estimated-over-Observed Transit Trips 0.91-1.03 NCRTPB (32), note: assumed as referring to transit assignment, reflects 

survey of 11 MPOs 

Acceptable Error – Transit Screenlines* +/-10% FDOT Model Update Task H 2) Acceptable Error 

Transit Ridership: 20,000 Passengers/Day +/- 15% FDOT Model Update Task H 2) Acceptable Transit Assignment Error 

Corridors/Cutlines 

*Standard might be difficult to achieve on screenlines with low transit volumes 




Ranges of Model Statistics from Model Validation Reports 

Trip Generation, Trip Distribution, and Mode Choice 

Non-Florida 

FL Statistics 

Statistics 

Statistic Low High Low High Document(s) Cited 

Persons/DU (or HH) 2.23 2.66 2.50 2.69 22, 34 (NERPM updated), 36, 40, 47, 48, 49, 52 

Person Trips/DU (or HH) 7.31 11.49 8.43 9.09 22, 27, 34 (NERPM updated), 36, 40, 41, 47, 48, 49, 52, 55 

Vehicle Trips/HH (or DU) 6.72 8.70 36, 42, 50 

Person Trips/Person 3.28 3.77 3.64 3.64 22, 34 (NERPM updated), 47, 48, 49, 

Person Trips/TAZ 7,435 7,435 36 

Vehicle Trips/Person 3.00 3.00 42, 

Person Trips/Employee 6.17 13.11 4.97 4.97 22, 34 (NERPM updated), 47, 48, 49, 55, 

Employment/Population 0.33 0.33 52, 

Percent Trips by Purpose – HBW 11.56% 19.30% 12.69% 25.00% 21, 22, 34 (NERPM updated), 40, 41, 47, 48, 49, 50, 53, 55, 56 

Percent Trips by Purpose – HBSH 9.84% 20.74% 9.54% 11.30% 21, 22, 34 (NERPM updated), 47, 48, 49, 53, 55, 56, 

Percent Trips by Purpose – HBSR 9.00% 12.68% 5.56% 11.70% 22, 34 (NERPM updated), 40, 47, 48, 49, 53, 55, 56, 

Percent Trips by Purpose – HBSC 5.00% 7.20% 5.09% 10.90% 21, 22, 40, 47, 53, 55 

Percent Trips by Purpose – HBO* 14.00% 28.41% 17.29% 39.00% 21, 22, 34 (NERPM updated), 40, 41, 47, 48, 49, 50, 53, 55, 56 

Percent Trips by Purpose – NHB** 18.27% 35.25% 18.00% 32.92% 21, 22, 34 (NERPM updated), 40, 41, 47, 48, 49, 50, 53, 55, 56 

Percent Trips by Purpose – Truck-Taxi*** 1.86% 11.00% 9.00% 11.00% 22, 34 (NERPM updated), 40, 41, 47, 48, 49, 50, 55, 56 

Percent Trips by Purpose – IE**** 0.39% 7.85% 9.00% 14.01% 22, 34 (NERPM updated), 40, 41, 47, 48, 49, 50, 55, 56 

External-External Trip Percentages (Range) 0.00% 89.90% 0.00% 55.00% 34, 46, 47, 48, 49, 55, 

Terminal Times – CBD 3 5 2 6 21, 22, 34, 41, 47, 48, 49, 53, 55, 

Terminal Times – CBD Fringe 2 4 2 5 22, 34, 41, 47, 48, 49, 53, 55, 

Terminal Times – Residential 1 2 1 1 22, 34, 47, 48, 49, 55, 

Terminal Times – OBD 1 2 1 2 22, 34, 41, 47, 48, 49, 55, 

Terminal Times – Rural 1 1 1 1 22, 34, 41, 47, 48, 49, 53, 55, 

Average Trip Length – HBW 15.42 27.98 12.05 42.50 21, 22, 27, 34 (NERPM up), 36, 38, 41, 47, 48, 49, 46******, 50, 52, 55 

Average Trip Length – HBSH 12.58 18.09 10.40 16.60 21, 22, 27, 34 (NERPM updated), 36, 47, 48, 49, 46******, 55 

Average Trip Length – HBSR 12.37 19.03 11.36 11.36 22, 34 (NERPM updated), 47, 48, 49, 55, 

Average Trip Length – HBSC 13.86 14.03 6.82 15.30 21, 22, 27, 36, 38, 47, 46******, 55 

Average Trip Length – HBO* 12.54 20.25 7.98 19.20 21, 22, 27, 34 (NERPM up), 36, 38, 41, 47, 48, 49, 46******, 50, 52, 55 

Average Trip Length – NHB** 10.15 18.75 6.40 18.30 21, 22, 27, 34 (NERPM updated), 36, 38, 41, 46, 47, 48, 49, 50, 52, 55 

Average Trip Length – Truck-Taxi*** 13.99 17.54 11.50 19.62 22, 34 (NERPM updated), 36, 47, 48, 49, 50, 55, 

Average Trip Length – IE**** 26.17 58.01 27.80 41.23 22, 34 (NERPM updated), 36, 47, 48, 49, 50 




Ranges of Model Statistics from Model Validation Reports (continued) 


Non-Florida 

FL Statistics 

Statistics 


Percent Intrazonal – HBW 1.09% 3.16% 2.40% 7.05% 34 (NERPM updated), 41, 47, 48, 49, 

Percent Intrazonal – HBSH 3.63% 11.09% 9.96% 9.96% 34 (NERPM updated), 47, 48, 49, 

Percent Intrazonal – HBSR 4.14% 11.22% 21.07% 21.07% 34 (NERPM updated), 47, 48, 49, 

Percent Intrazonal – HBSC 11.17% 11.17% 47, 

Percent Intrazonal – HBO* 2.95% 5.20% 6.50% 11.20% 34 (NERPM updated), 41, 47, 48, 49, 

Percent Intrazonal – NHB** 4.69% 8.90% 7.20% 8.04% 34 (NERPM updated), 41, 47, 48, 49, 

Percent Intrazonal – Truck-Taxi*** 4.13% 9.05% 3.07% 3.07% 34 (NERPM updated), 47, 48, 49, 

Percent Drive Alone – HBW 77.26% 83.94% 71.00% 81.30% 21, 22, 34 (NERPM updated), 40, 46, 49 

Percent One Passenger – HBW 10.35% 16.78% 7.70% 10.10% 21, 22, 34 (NERPM updated), 40, 46, 49 

Percent Two+ Passengers – HBW 3.14% 6.24% 2.20% 3.55% 21, 22, 34 (NERPM updated), 40, 46, 49 

Percent Transit – HBW***** 0.39% 1.23% 6.20% 15.30% 21, 22, 34 (NERPM updated), 40, 46, 49 

Percent Drive Alone – All Trips 42.96% 55.11% 33.30% 48.40% 21, 22, 27, 34 (NERPM updated), 40, 49 

Percent One Passenger – All Trips 39.50% 30.06% 12.10% 26.50% 21, 22, 34 (NERPM updated), 40, 49 

Percent Two+ Passengers – All Trips 17.30% 21.14% 7.30% 20.70% 21, 22, 34 (NERPM updated), 40, 49 

Percent Shared Ride – All Trips 42.50% 42.50% 27, 

Percent Transit – All Trips***** 0.24% 0.54% 3.08% 16.40% 21, 22, 27, 34 (NERPM updated), 36, 40, 49 

Notes: *HBO includes a variety of special trip purposes, depending on the model, airport, college, and shop (e.g., MTC) 

**NHB includes combined purposes for NHB Work and NHB Nonwork, where appropriate; PSRC model refers to these trips as Work-Other and Other-Other 

***Truck-Taxi includes all commercial vehicle trips (might not be included in percentage totals for models that only summarize person trip percentages (e.g., MTC) 

****Internal-External trips include vehicle trips with one trip end inside and one trip end outside the model boundary; some models do not include IE as a separate trip purpose 

*****Percent transit includes nonmotorized trips, where these are separately accounted for (e.g., MTC-10.5%, PSRC-6.7%) 

******ARC model documentation only provides average trip lengths by income group for home-based trips; lower and upper ends of range included where appropriate 

“NERPM Updated” indicates statistics referenced from latest version of Cube-Voyager model rather than taken directly from TRANPLAN validation report 




Ranges of Model Statistics from Model Validation Reports 

Auto Occupancy and Trip Assignment 

FL Statistics Non-Florida Statistics 


Auto Occupancy Rates – HBW 1.10 1.12 1.06 1.16 27, 36, 38, 41, 46, 47, 48, 49, 52, 53 

Auto Occupancy Rates – HBSH 1.43 1.54 1.27 1.96 21, 27, 36, 47, 48, 49, 53, 

Auto Occupancy Rates – HBSR 1.43 1.54 1.72 2.47 47, 48, 49, 53, 

Auto Occupancy Rates – HBSC 1.00 2.45 27, 36, 38, 47, 53, 

Auto Occupancy Rates – HBO 1.43 1.54 1.30 2.07 21, 27, 36, 38, 41, 46, 47, 48, 49, 52, 53 

Auto Occupancy Rates – NHB 1.12 1.65 1.17 1.96 21, 27, 36, 38, 41, 46, 47, 48, 49, 52, 53 

Freeway/Expressway Volume-over-Count 0.98 1.07 0.86 1.15 22, 27, 34 (NERPM updated), 41, 45, 47, 49, 50, 53, 55, 

Divided/Principal Arterial Volume-over-Count* 0.97 1.06 0.89 1.02 22, 27, 34 (NERPM updated), 41, 45, 47, 48, 49, 50, 53, 55, 

Undivided/Minor Arterial Volume-over- 

0.93 1.04 0.77 1.07 22, 27, 34 (NERPM updated), 41, 45, 47, 48, 49, 50, 53, 55, 

Count** 

Collector Volume-over-Count*** 0.85 0.98 0.37 1.05 22, 27, 34 (NERPM updated), 41, 45, 47, 48, 49, 50, 53, 55, 

One-Way Volume-over-Count 0.97 1.39 0.71 0.71 22, 34 (NERPM updated), 47, 49, 55, 

Ramp Volume-over-Count 0.90 1.33 22, 34 (NERPM updated), 47, 49, 55, 

HOV Lane Volume-over-Count 1.10 1.10 55, 

Toll Road Volume-over-Count 0.96 1.00 22, 34 (NERPM updated), 47, 49, 55, 

CBD Volume-over-Count 0.96 1.20 0.88 0.93 22, 34 (NERPM updated), 47, 48, 49, 55 

CBD Fringe Volume-over-Count 0.90 1.03 0.91 0.91 22, 34 (NERPM updated), 47, 48, 49, 55 

Residential Volume-over-Count 0.99 1.02 0.97 1.04 22, 34 (NERPM updated), 47, 48, 49, 55 

OBD Volume-over-Count 0.95 1.06 0.91 0.91 22, 34 (NERPM updated), 47, 48, 49, 55 

Rural Volume-over-Count 0.97 1.07 0.91 1.12 22, 27, 34 (NERPM updated), 38, 47, 48, 49, 55 

Screenline Volume/Count 0.70 2.01 0.69 1.22 22, 27, 34 (NERPM up), 36, 38, 40 (part), 41, 42, 45, 46, 47, 48, 49, 55, 56 

Screenline Volume/Count – Externals 0.95 1.05 1.00 1.00 22, 27, 34 (NERPM updated), 45, 47, 48, 49, 

Cutline Volume/Count – All (if different) 0.88 1.13 27, 

Nonscreenline Volume/Count (if available) 0.98 1.01 22, 34 (NERPM updated), 48, 49, 55, 




Ranges of Model Statistics from Model Validation Reports (continued) 


FL Statistics Non-Florida Statistics 


Assigned VMT-over-Count Areawide 1.00 1.02 0.95 1.00 34 (NERPM updated), 36, 38, 48, 49, 52, 55, 56, 

Assigned VMT-over-HPMS Areawide 0.86 0.86 36 

Assigned VMT per Person 25.97 25.97 52 

Assigned VMT per HH 70.81 70.81 62.20 62.20 42, 55, 

Assigned VHT-over-Count Areawide 1.00 1.03 34 (NERPM updated), 48, 49, 55, 56, 

Assigned VMT per HH 1.51 1.51 55 

Assigned volume-over-Count Areawide 0.98 1.02 0.93 1.03 21, 34 (NERPM updated), 38, 41, 42, 45, 48, 49, 50, 53, 55, 56 

RMSE: LT 5,000 AADT*** 45.000% 80.528% 14.800% 106.600% 22, 34 (NERPM updated), 38, 41, 42, 45, 46, 47, 48, 49, 55, 56, 

RMSE: 5,000-10,000 AADT 28.000% 37.520% 32.740% 51.100% 22, 34 (NERPM updated), 38, 41, 42, 45, 46, 47, 48, 49, 55, 56, 




RMSE: 40,000-50,000 AADT 2.000% 15.014% 7.200% 25.800% 22, 34 (NERPM updated), 38, 41, 42, 45, 46, 47, 49, 56 

RMSE: 50,000-60,000 AADT 10.990% 14.260% 3.500% 25.800% 22, 34 (NERPM updated), 38, 41, 45, 46, 47, 49, 56 

RMSE: 60,000-70,000 AADT 6.800% 13.520% 8.010% 25.800% 22, 34 (NERPM updated), 38 (60k+), 41, 45, 46, 49, 56 

RMSE: 70,000-80,000 AADT 8.170% 18.197% 3.500% 25.800% 22, 34 (NERPM updated), 45, 46, 49, 56, 

RMSE: 80,000-90,000 AADT 4.900% 5.740% 4.200% 11.100% 22, 45, 46 (80k+), 56 

RMSE: 90,000-100,000 AADT 9.890% 10.100% 4.200% 4.200% 22, 45, 56, 

RMSE: GT 100,000 AADT 2.600% 2.600% 45, 

RMSE Areawide 24.000% 33.846% 23.400% 43.300% 22, 34 (NERPM updated), 36, 38, 41, 42, 46, 47, 49, 52, 53, 55 

Estimated-over-Observed Transit Trips 0.94 1.11 1.02 1.30 21, 22, 27, 34, 40, 56, 

*Some models categorize arterials into principle and minor arterials instead of divided and undivided 

**Some models report separate collector and local street statistics 

***Some models used different RMSE volume groups (e.g., Knoxville) or functional class (Boise) so lows and highs might reflect subcategories of those listed 


Appendix C 

NHTS Summary Validation Statistics



NHTS Nationwide Summary Statistics 

Trip Generation 

2001 NHTS 

Number of Households 

Number of Households (Thousands) 

Count of HH members 

Type of housing unit 

1 2 3 4 5 6 7 8 9 10 11 12 14 All 

Refused . 0 . 0 1 . . . . . . . . 2 

Don’t Know 1 0 1 0 . . . . . . . . . 2 

Detached single house 12,337 23,639 12,249 12,121 5,445 1,775 510 220 62 46 46 5 0 68,456 

Duplex 1,277 1,423 1,008 701 318 131 63 28 15 18 . . 1 4,983 

Rowhouse or townhouse 1,053 1,279 794 488 196 72 32 0 5 . . . . 3,919 

Apartment, condominium 11,162 6,798 2,640 2,003 647 247 53 8 26 0 0 . . 23,585 

Mobile home or trailer 1,678 1,841 1,046 873 482 117 44 16 4 4 1 . . 6,106 

Dorm room, fraternity or sorority 

house 84 32 5 1 3 . . 1 . . . . . 126 

Other 126 20 6 15 18 . 2 . 0 . . . . 187 

All 27,718 35,032 17,749 16,203 7,111 2,342 704 274 112 68 46 5 1 107,365 

25.82% 32.63% 16.53% 15.09% 6.62% 2.18% 0.66% 0.26% 0.10% 0.06% 0.04% 0.00% 0.00% 

5+ 10663 9.93% 100.0% 

Florida DOT, Systems Planning Office C-1



2001 NHTS 

Person Trips (by Purpose) 

Travel Day Person Trips (Millions) 

Household in urban/rural area 

Urban Rural All 

Trip purpose 

Not Ascertained 397 96 493 Urban Rural All 

Home-base work 34,186 8,995 43,181 10.63% 10.57% 10.62% 

Home-based shopping 72,502 17,073 89,575 22.54% 20.07% 22.02% 

Home-based social/recreational 44,671 10,901 55,572 13.89% 12.81% 13.66% 

Other home-based 69,748 18,760 88,508 21.68% 22.05% 21.76% 

Not home-based 100,576 29,357 129,933 31.27% 34.50% 31.94% 

All 322,080 85,183 407,262 

Excluding “Not Ascertained” 321,683 85,087 406,769 100.00% 100.00% 100.00% 

Weighted Weighted Weighted Aggregate 

Person Trips Persons Households Rates 

Trips per Person 406,769,000,000 277,203,000 1467.40 

Trips per Dwelling Unit 406,769,000,000 107,365,000 3788.66 

Average Household Size 2.59 Census 2000 

Trip Distribution 

2001 NHTS 

Average Person Trip Duration (Minutes) 

TD Person Trip Duration (Mean) 


Trip purpose 


Not Ascertained 

16.51 8.59 15.3 

Home-base work 24.42 24.64 24.46 

Home-based shopping 15.27 19.71 16.12 

Home-based social/recreational 23.94 25.32 24.22 

Other home-based 17.87 20.13 18.35 

Not home-based 19.84 19.94 19.86 

All 19.43 21.12 19.78 

C-2 Florida DOT, Systems Planning Office



Mode Choice/Auto Occupancy 

2001 NHTS 

Avg. Vehicle Occupancy (Persons) 

TD Vehicle Occupancy (Mean) 



Trip purpose 

Not Ascertained 1.17 1.23 1.18 

Home-base work 1.09 1.1 1.1 


Home-based social/recreational 1.95 1.89 1.94 


Not home-based 1.73 1.66 1.71 

All 1.63 1.62 1.63 




Trip Assignment 

2001 NHTS 

Person Trips (Time-of-Day) 

Not 

Ascertained 

Home-base 

work 


Trip purpose 

Homebased 

Home-based 

shopping social/recreational 

Other 

homebased 

Not 

homebased 

All HBW HBSH HBSR HBO NHB ALL 

Travel day trip start time, military 

Not Ascertained 493 . . . . . 493 

Hour of 00:00 . 409 237 526 218 284 1,673 0.95% 0.26% 0.95% 0.25% 0.22% 0.41% 

Hour of 01:00 . 170 64 294 123 162 812 0.39% 0.07% 0.53% 0.14% 0.12% 0.20% 

Hour of 02:00 . 114 44 217 76 123 574 0.26% 0.05% 0.39% 0.09% 0.09% 0.14% 

Hour of 03:00 . 103 28 61 69 62 322 0.24% 0.00% 0.11% 0.08% 0.05% 0.08% 

Hour of 04:00 . 567 125 70 187 624 1,572 1.31% 0.14% 0.13% 0.21% 0.48% 0.39% 

Hour of 05:00 . 2,039 315 291 639 648 3,932 4.72% 0.35% 0.52% 0.72% 0.50% 0.97% 

Hour of 06:00 . 4,594 805 721 2,770 1,658 10,548 10.64% 0.90% 1.30% 3.13% 1.28% 2.59% 

Hour of 07:00 . 6,076 1,730 1,262 10,844 5,003 24,916 14.07% 1.93% 2.27% 12.25% 3.85% 6.12% 

Hour of 08:00 . 3,387 2,945 1,516 8,745 5,752 22,345 7.84% 3.29% 2.73% 9.88% 4.43% 5.49% 

Hour of 09:00 . 1,304 4,796 1,856 5,263 6,538 19,756 3.02% 5.35% 3.34% 5.95% 5.03% 4.85% 

Hour of 10:00 . 728 6,690 2,389 4,898 9,332 24,037 1.69% 7.47% 4.30% 5.53% 7.18% 5.90% 

Hour of 11:00 . 1,066 7,374 2,588 4,293 11,766 27,088 2.47% 8.23% 4.66% 4.85% 9.06% 6.65% 

Hour of 12:00 . 1,747 6,780 2,686 4,697 14,404 30,314 4.05% 7.57% 4.83% 5.31% 11.09% 7.44% 

Hour of 13:00 . 1,342 6,840 2,794 3,932 11,600 26,508 3.11% 7.64% 5.03% 4.44% 8.93% 6.51% 

Hour of 14:00 . 1,860 6,529 3,103 7,154 11,288 29,934 4.31% 7.29% 5.58% 8.08% 8.69% 7.35% 

Hour of 15:00 . 3,111 6,786 3,775 8,820 11,446 33,939 7.20% 7.58% 6.79% 9.97% 8.81% 8.33% 

Hour of 16:00 . 3,920 7,254 4,705 5,601 10,145 31,624 9.08% 8.10% 8.47% 6.33% 7.81% 7.77% 

Hour of 17:00 . 4,600 7,218 5,456 6,016 9,070 32,360 10.65% 8.06% 9.82% 6.80% 6.98% 7.95% 

Hour of 18:00 . 2,128 7,524 5,660 4,738 7,020 27,070 4.93% 8.40% 10.18% 5.35% 5.40% 6.65% 

Hour of 19:00 . 971 6,440 5,193 3,105 4,967 20,676 2.25% 7.19% 9.34% 3.51% 3.82% 5.08% 

Hour of 20:00 . 695 4,555 3,980 2,658 3,684 15,573 1.61% 5.09% 7.16% 3.00% 2.84% 3.82% 

Hour of 21:00 . 742 2,933 3,203 2,015 2,433 11,326 1.72% 3.27% 5.76% 2.28% 1.87% 2.78% 

Hour of 22:00 . 822 1,101 1,915 1,091 1,240 6,170 1.90% 1.23% 3.45% 1.23% 0.95% 1.52% 

Hour of 23:00 . 685 462 1,312 556 686 3,701 1.59% 0.52% 2.36% 0.63% 0.53% 0.91% 

All 493 43,181 89,575 55,572 88,508 129,933 407,262 100.00% 99.97% 100.00% 100.00% 100.00% 99.88% 

10.6% 22.0% 13.7% 21.8% 31.9% 406,769 100.00% 




2001 NHTS 

Vehicle Trips (Time-of-Day) 

Not 

Ascertained 

Home-base 

work 

Travel Day Vehicle Trips (Millions) 

Trip purpose 

Homebased 

social/ 

Home-based 

shopping recreational 

Other 

homebased 

Not 

homebased 


Travel day trip start time, military 


Hour of 00:00 . 346 123 263 150 144 1,026 0.95% 0.23% 1.20% 0.34% 0.19% 0.44% 

Hour of 01:00 . 130 37 156 86 90 500 0.36% 0.07% 0.71% 0.20% 0.12% 0.21% 

Hour of 02:00 . 92 30 129 45 48 345 0.25% 0.06% 0.59% 0.10% 0.06% 0.15% 

Hour of 03:00 . 90 18 33 52 40 233 0.25% 0.00% 0.15% 0.12% 0.05% 0.10% 

Hour of 04:00 . 505 78 37 132 428 1,180 1.39% 0.14% 0.17% 0.30% 0.56% 0.51% 

Hour of 05:00 . 1,735 288 158 330 492 3,003 4.76% 0.53% 0.72% 0.76% 0.64% 1.29% 

Hour of 06:00 . 3,922 679 288 1,212 1,251 7,351 10.76% 1.26% 1.31% 2.78% 1.63% 3.15% 

Hour of 07:00 . 5,202 1,329 546 4,264 3,439 14,780 14.27% 2.46% 2.49% 9.79% 4.47% 6.34% 

Hour of 08:00 . 2,832 2,084 648 3,835 3,908 13,308 7.77% 3.86% 2.95% 8.81% 5.08% 5.71% 

Hour of 09:00 . 1,075 3,212 896 2,963 4,314 12,459 2.95% 5.95% 4.08% 6.80% 5.60% 5.35% 

Hour of 10:00 . 591 4,287 995 2,716 5,761 14,349 1.62% 7.95% 4.53% 6.24% 7.48% 6.16% 

Hour of 11:00 . 909 4,588 1,101 2,354 7,321 16,273 2.49% 8.50% 5.02% 5.41% 9.51% 6.98% 

Hour of 12:00 . 1,518 4,109 1,117 2,555 8,569 17,868 4.17% 7.62% 5.09% 5.87% 11.13% 7.67% 

Hour of 13:00 . 1,190 4,217 1,158 2,158 6,980 15,703 3.27% 7.82% 5.28% 4.96% 9.07% 6.74% 

Hour of 14:00 . 1,577 3,892 1,200 2,905 6,422 15,996 4.33% 7.21% 5.47% 6.67% 8.34% 6.86% 

Hour of 15:00 . 2,581 4,003 1,341 3,530 6,356 17,811 7.08% 7.42% 6.11% 8.11% 8.26% 7.64% 

Hour of 16:00 . 3,296 4,345 1,662 3,082 5,994 18,378 9.04% 8.05% 7.57% 7.08% 7.79% 7.89% 

Hour of 17:00 . 3,874 4,262 1,862 3,138 5,304 18,439 10.63% 7.90% 8.49% 7.21% 6.89% 7.91% 

Hour of 18:00 . 1,759 4,162 2,149 2,590 3,652 14,313 4.83% 7.71% 9.79% 5.95% 4.74% 6.14% 

Hour of 19:00 . 790 3,453 1,813 1,752 2,448 10,256 2.17% 6.40% 8.26% 4.02% 3.18% 4.40% 

Hour of 20:00 . 551 2,336 1,453 1,488 1,827 7,655 1.51% 4.33% 6.62% 3.42% 2.37% 3.29% 

Hour of 21:00 . 619 1,563 1,418 1,141 1,185 5,927 1.70% 2.90% 6.46% 2.62% 1.54% 2.54% 

Hour of 22:00 . 699 608 857 684 626 3,474 1.92% 1.13% 3.91% 1.57% 0.81% 1.49% 

Hour of 23:00 . 562 254 661 379 382 2,238 1.54% 0.47% 3.01% 0.87% 0.50% 0.96% 

All 164 36,445 53,957 21,941 43,542 76,981 233,030 100.00% 99.97% 100.00% 100.00% 100.00% 99.93% 




NHTS Florida Summary Statistics 

Trip Generation 

2001 NHTS 

Number of Households 

Number of Households (Thousands) 

Count of HH members 

1 2 3 4 5 6 7 8 9 10 11 12 14 All 

Type of housing unit 

Refused . 0 . 0 1 . . . . . . . . 2 

Don’t Know 1 0 1 0 . . . . . . . . . 2 

Detached single house 12,337 23,639 12,249 12,121 5,445 1,775 510 220 62 46 46 5 0 68,456 

Duplex 1,277 1,423 1,008 701 318 131 63 28 15 18 . . 1 4,983 

Rowhouse or townhouse 1,053 1,279 794 488 196 72 32 0 5 . . . . 3,919 

Apartment, condominium 11,162 6,798 2,640 2,003 647 247 53 8 26 0 0 . . 23,585 

Mobile home or trailer 1,678 1,841 1,046 873 482 117 44 16 4 4 1 . . 6,106 

Dorm room, fraternity or 

sorority house 84 32 5 1 3 . . 1 . . . . . 126 

Other 126 20 6 15 18 . 2 . 0 . . . . 187 

All 27,718 35,032 17,749 16,203 7,111 2,342 704 274 112 68 46 5 1 107,365 

25.82% 32.63% 16.53% 15.09% 6.62% 2.18% 0.66% 0.26% 0.10% 0.06% 0.04% 0.00% 0.00% 

5+ 10663 9.93% 100.0% 




2001 NHTS 

Person Trips (by Purpose) 


Household in urban/rural 

area 


Trip purpose 

Not Ascertained 397 96 493 Urban Rural All 

Home-base work 34,186 8,995 43,181 10.63% 10.57% 10.62% 

Home-based shopping 72,502 17,073 89,575 22.54% 20.07% 22.02% 

Home-based 

social/recreational 44,671 10,901 55,572 13.89% 12.81% 13.66% 

Other home-based 69,748 18,760 88,508 21.68% 22.05% 21.76% 

Not home-based 100,576 29,357 129,933 31.27% 34.50% 31.94% 

All 322,080 85,183 407,262 

Excluding “Not 

Ascertained” 321,683 85,087 406,769 100.00% 100.00% 100.00% 

Weighted Weighted Weighted Aggregate 

Person Trips Persons Households Rates 

Trips per Person 406,769,000,000 277,203,000 1467.40 

Trips per Dwelling Unit 406,769,000,000 107,365,000 3788.66 

Average Household Size 2.59 Census 2000 

Trip Distribution 

2001 NHTS 

Average Person Trip 

Duration (Minutes) 

TD Person Trip Duration (Mean) 


area 

Trip purpose 


Not Ascertained 

16.51 8.59 15.3 

Home-base work 24.42 24.64 24.46 


Home-based 

social/recreational 23.94 25.32 24.22 


Not home-based 19.84 19.94 19.86 

All 19.43 21.12 19.78 




Mode Choice/Auto Occupancy 

2001 NHTS 

Avg. Vehicle Occupancy 

(Persons) 

TD Vehicle Occupancy (Mean) 


area 


Trip purpose 

Not Ascertained 1.17 1.23 1.18 

Home-base work 1.09 1.1 1 .1 

Home-based shopping 1.8 1.8 1 .8 

Home-based 

social/recreational 1.95 1.89 1 .94 

Other home-based 1.67 1.76 1 .7 

Not home-based 1.73 1.66 1 .71 

All 1.63 1.62 1 .63 




Trip Assignment 

2001 NHTS 

Person Trips (Time-of-Day) 


Trip purpose 

Not 

Ascertained 

Homebase 

work 

Homebased 

shopping 

Home-based 

social/recreational 

Other 

homebased 

Not 

homebased 


Travel day trip start time, 

military 


Hour of 00:00 . 409 237 526 218 284 1,673 0.95% 0.26% 0.95% 0.25% 0.22% 0.41% 

Hour of 01:00 . 170 64 294 123 162 812 0.39% 0.07% 0.53% 0.14% 0.12% 0.20% 

Hour of 02:00 . 114 44 217 76 123 574 0.26% 0.05% 0.39% 0.09% 0.09% 0.14% 

Hour of 03:00 . 103 28 61 69 62 322 0.24% 0.00% 0.11% 0.08% 0.05% 0.08% 

Hour of 04:00 . 567 125 70 187 624 1,572 1.31% 0.14% 0.13% 0.21% 0.48% 0.39% 

Hour of 05:00 . 2,039 315 291 639 648 3,932 4.72% 0.35% 0.52% 0.72% 0.50% 0.97% 

Hour of 06:00 . 4,594 805 721 2,770 1,658 10,548 10.64% 0.90% 1.30% 3.13% 1.28% 2.59% 

Hour of 07:00 . 6,076 1,730 1,262 10,844 5,003 24,916 14.07% 1.93% 2.27% 12.25% 3.85% 6.12% 

Hour of 08:00 . 3,387 2,945 1,516 8,745 5,752 22,345 7.84% 3.29% 2.73% 9.88% 4.43% 5.49% 

Hour of 09:00 . 1,304 4,796 1,856 5,263 6,538 19,756 3.02% 5.35% 3.34% 5.95% 5.03% 4.85% 

Hour of 10:00 . 728 6,690 2,389 4,898 9,332 24,037 1.69% 7.47% 4.30% 5.53% 7.18% 5.90% 

Hour of 11:00 . 1,066 7,374 2,588 4,293 11,766 27,088 2.47% 8.23% 4.66% 4.85% 9.06% 6.65% 

Hour of 12:00 . 1,747 6,780 2,686 4,697 14,404 30,314 4.05% 7.57% 4.83% 5.31% 11.09% 7.44% 

Hour of 13:00 . 1,342 6,840 2,794 3,932 11,600 26,508 3.11% 7.64% 5.03% 4.44% 8.93% 6.51% 

Hour of 14:00 . 1,860 6,529 3,103 7,154 11,288 29,934 4.31% 7.29% 5.58% 8.08% 8.69% 7.35% 

Hour of 15:00 . 3,111 6,786 3,775 8,820 11,446 33,939 7.20% 7.58% 6.79% 9.97% 8.81% 8.33% 

Hour of 16:00 . 3,920 7,254 4,705 5,601 10,145 31,624 9.08% 8.10% 8.47% 6.33% 7.81% 7.77% 

Hour of 17:00 . 4,600 7,218 5,456 6,016 9,070 32,360 10.65% 8.06% 9.82% 6.80% 6.98% 7.95% 

Hour of 18:00 . 2,128 7,524 5,660 4,738 7,020 27,070 4.93% 8.40% 10.18% 5.35% 5.40% 6.65% 

Hour of 19:00 . 971 6,440 5,193 3,105 4,967 20,676 2.25% 7.19% 9.34% 3.51% 3.82% 5.08% 

Hour of 20:00 . 695 4,555 3,980 2,658 3,684 15,573 1.61% 5.09% 7.16% 3.00% 2.84% 3.82% 

Hour of 21:00 . 742 2,933 3,203 2,015 2,433 11,326 1.72% 3.27% 5.76% 2.28% 1.87% 2.78% 

Hour of 22:00 . 822 1,101 1,915 1,091 1,240 6,170 1.90% 1.23% 3.45% 1.23% 0.95% 1.52% 

Hour of 23:00 . 685 462 1,312 556 686 3,701 1.59% 0.52% 2.36% 0.63% 0.53% 0.91% 

All 493 43,181 89,575 55,572 88,508 129,933 407,262 100.00% 99.97% 100.00% 100.00% 100.00% 99.88% 

10.6% 22.0% 13.7% 21.8% 31.9% 406,769 100.00% 




2001 NHTS 

Vehicle Trips (Time-of-Day) 

Travel Day Vehicle Trips (Millions) 

Trip purpose 

Not 

Ascertained 

Homebase 

work 

Homebased 

shopping 

Home-based 

social/ 

recreational 

Other 

homebased 

Not 

homebased 


Travel day trip start time, 

military 


Hour of 00:00 . 346 123 263 150 144 1,026 0.95% 0.23% 1.20% 0.34% 0.19% 0.44% 

Hour of 01:00 . 130 37 156 86 90 500 0.36% 0.07% 0.71% 0.20% 0.12% 0.21% 

Hour of 02:00 . 92 30 129 45 48 345 0.25% 0.06% 0.59% 0.10% 0.06% 0.15% 

Hour of 03:00 . 90 18 33 52 40 233 0.25% 0.00% 0.15% 0.12% 0.05% 0.10% 

Hour of 04:00 . 505 78 37 132 428 1,180 1.39% 0.14% 0.17% 0.30% 0.56% 0.51% 

Hour of 05:00 . 1,735 288 158 330 492 3,003 4.76% 0.53% 0.72% 0.76% 0.64% 1.29% 

Hour of 06:00 . 3,922 679 288 1,212 1,251 7,351 10.76% 1.26% 1.31% 2.78% 1.63% 3.15% 

Hour of 07:00 . 5,202 1,329 546 4,264 3,439 14,780 14.27% 2.46% 2.49% 9.79% 4.47% 6.34% 

Hour of 08:00 . 2,832 2,084 648 3,835 3,908 13,308 7.77% 3.86% 2.95% 8.81% 5.08% 5.71% 

Hour of 09:00 . 1,075 3,212 896 2,963 4,314 12,459 2.95% 5.95% 4.08% 6.80% 5.60% 5.35% 

Hour of 10:00 . 591 4,287 995 2,716 5,761 14,349 1.62% 7.95% 4.53% 6.24% 7.48% 6.16% 

Hour of 11:00 . 909 4,588 1,101 2,354 7,321 16,273 2.49% 8.50% 5.02% 5.41% 9.51% 6.98% 

Hour of 12:00 . 1,518 4,109 1,117 2,555 8,569 17,868 4.17% 7.62% 5.09% 5.87% 11.13% 7.67% 

Hour of 13:00 . 1,190 4,217 1,158 2,158 6,980 15,703 3.27% 7.82% 5.28% 4.96% 9.07% 6.74% 

Hour of 14:00 . 1,577 3,892 1,200 2,905 6,422 15,996 4.33% 7.21% 5.47% 6.67% 8.34% 6.86% 

Hour of 15:00 . 2,581 4,003 1,341 3,530 6,356 17,811 7.08% 7.42% 6.11% 8.11% 8.26% 7.64% 

Hour of 16:00 . 3,296 4,345 1,662 3,082 5,994 18,378 9.04% 8.05% 7.57% 7.08% 7.79% 7.89% 

Hour of 17:00 . 3,874 4,262 1,862 3,138 5,304 18,439 10.63% 7.90% 8.49% 7.21% 6.89% 7.91% 

Hour of 18:00 . 1,759 4,162 2,149 2,590 3,652 14,313 4.83% 7.71% 9.79% 5.95% 4.74% 6.14% 

Hour of 19:00 . 790 3,453 1,813 1,752 2,448 10,256 2.17% 6.40% 8.26% 4.02% 3.18% 4.40% 

Hour of 20:00 . 551 2,336 1,453 1,488 1,827 7,655 1.51% 4.33% 6.62% 3.42% 2.37% 3.29% 

Hour of 21:00 . 619 1,563 1,418 1,141 1,185 5,927 1.70% 2.90% 6.46% 2.62% 1.54% 2.54% 

Hour of 22:00 . 699 608 857 684 626 3,474 1.92% 1.13% 3.91% 1.57% 0.81% 1.49% 

Hour of 23:00 . 562 254 661 379 382 2,238 1.54% 0.47% 3.01% 0.87% 0.50% 0.96% 

All 164 36,445 53,957 21,941 43,542 76,981 233,030 100.00% 99.97% 100.00% 100.00% 100.00% 99.93% 


Appendix D 

Model Validation Summary of Available Standards



Table D.1 Checklist of Available Validation Standards from Literature: Trip Generation 


Population/Employment Ratio 40-60% Iowa DOT Peer Review (38) 

Person Trips/Person 3.64 – 3.87 Validation and Reasonableness (14) 

Person Trips/Person (Urban) 2.54 University of Wisconsin (16): Kentucky Statewide Model/NPTS 

Person Trips/Person (Rural) 2.57 University of Wisconsin (16): Kentucky Statewide Model/NPTS 

Person Trips/HH 8.5 – 10.5 University of Tennessee (58) 

Person Trips/HH 6.8 – 12.4 Validation and Reasonableness (14), NCHRP 365 (15) 

Person Trips/DU 14.1/14.5/11.8/7.6 Calibration and Adjustment 6) – population sizes: 50-100/100-250/250- 

750/750k+ 

Person Trips/DU 9.2/9.0/8.6/8.5 NCHRP 365 (15) – population sizes: 50k-200k/200k-500k/500k-1M/1M+ 

Vehicle Trips/DU 9.15 VTRC (28) 

Resident/Commercial Neighborhood Trips 78.5%/21.5% VTRC (28) 

HBW Person Trips/Employee 1.29 – 1.40 Validation and Reasonableness (14) 

TAZs/Population 1 TAZ/1k Population Iowa DOT Peer Review (38) 

Florida DOT, Systems Planning Office D-1



Table D.1 Checklist of Available Validation Standards from Literature: Trip Generation (continued) 


Person Trips/TAZ 25k or less Iowa DOT Peer Review (38) 

Percent Trips by Purpose – HBW a 18% – 27% University of Tennessee (58) 

Percent Trips by Purpose – HBNW 47% – 54% University of Tennessee (58) 

Percent Trips by Purpose – NHB 22% – 31% University of Tennessee (58) 

Percent Trips by Purpose – HBW a 17% – 23% Validation and Reasonableness (14), NCHRP 365 (15) 

Percent Trips by Purpose – HBNW 52% – 60% Validation and Reasonableness (14), NCHRP 365 (15) 

Percent Trips by Purpose – NHB 23% – 25% Validation and Reasonableness (14), NCHRP 365 (15) 

Unbalanced Attractions/Productions 0.90-1.10 Validation and Reasonableness (14) 

External-External Trip Percentages b 21% Calibration and Adjustment 6) – population size: 50-100k 

External-External Trip Percentages b 15% Calibration and Adjustment 6) – population sizes: 100-250k 

External-External Trip Percentages b 10% Calibration and Adjustment 6) – population sizes: 250-750k 

External-External Trip Percentages b 4% Calibration and Adjustment 6) – population sizes: 750k+ 

External-External Trip Percentages b 5-20% NCHRP 365 (15) – sample of 6 different models of different population 

sizes 

External-External Trips – Interstate b 77%/67%/46% NCHRP 365 (15) – population sizes: 25k/50k/100k 

External-External Trips – Pr Arterial b 40%/30%/9% NCHRP 365 (15) – population sizes: 25k/50k/100k 

External-External Trips – Mi Arterial b 24%/13%/0% NCHRP 365 (15) – population sizes: 25k/50k/100k 

Percent External Trips b 10-20% Iowa DOT Peer Review (38) 

a 

b 

HBW percents listed here might be considered outdated. 

External-external percents are very general; values outside these ranges are not necessarily indicative of a validation problem. 

D-2 Florida DOT, Systems Planning Office



Table D.2 Checklist of Available Validation Standards from Literature: Trip Distribution 


Average Free-Flow Speeds – Urban 50/20-35/15/10 Validation and Reasonableness (14) – 

Freeway/Arterial/Collector/Centroid Conn 

Average Free-Flow Speeds – Suburban 55/25-40/20/15 Validation and Reasonableness (14) – 


Average Free-Flow Speeds – Rural 60/35-45/25/20 Validation and Reasonableness (14) – 


Average Free-Flow Speeds – CBD 60/45/35,35,30/15 NCHRP 365 (15) – Freeway/Expy/Pr Div,Ma Div,Mi 

Arterial/Collector 

Average Free-Flow Speeds – Suburban 60/45/45,45,35/30 NCHRP 365 (15) – Freeway/Expy/Pr Div,Ma Div,Mi 


Average Free-Flow Speeds – Rural 60/55/50,45,35/30 NCHRP 365 (15) – Freeway/Expy/Pr Div,Ma Div,Mi 


Terminal Times – Urban 2 (Prod), 4 (Attr) Validation and Reasonableness (14) – Initial Terminal Times 

Terminal Times – Suburban 1 (Prod), 2 (Attr) Validation and Reasonableness (14) – Initial Terminal Times 

Terminal Times – Rural 1 (Prod), 1 (Attr) Validation and Reasonableness (14) – Initial Terminal Times 

Terminal Times – CBD 5 NCHRP 365 (15) 

Terminal Times – CBD Fringe 4 NCHRP 365 (15) 

Terminal Times – Urban 3 NCHRP 365 (15) 

Terminal Times – Suburban 2 NCHRP 365 (15) 




Table D.2 Checklist of Available Validation Standards from Literature: Trip Distribution (continued) 


Terminal Times – Rural 1 NCHRP 365 (15) 

Average Trip Length – HBW a 11.2 – 35.4 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBSH a 8.6 – 18.7 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBSC 8.9 – 15.9 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBO a 10.4 – 17.3 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBNW 10.6 – 15.3 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – NHB a 8.1 – 17.1 mins. Validation and Reasonableness (14) – Comparison Among Cities 

Average Trip Length – HBW a 15 – 20 mins. Calibration and Adjustment 6) – Large Populations (unspecified) 

Average Trip Length – HBNW 13 – 17 mins. Calibration and Adjustment 6) – Large Populations (unspecified) 

Average Trip Length – NHB a 6-17 mins. Calibration and Adjustment 6) – Large Populations (unspecified) 

Average Trip Length – Transit Trips +/-5% FDOT Model Update Task H 4) Acceptable Error 

Mean Trip Length 



+/-3% of 

observed 

visual 

comparison 

+/- 5% of 

observed 



Cambridge Systematics’ Model Validation Seminar (59) 

Coincidence Ratios by Purpose GT 65%-70% Cambridge Systematics’ Model Validation Seminar (59) 




Table D.2 Checklist of Available Validation Standards from Literature: Trip Distribution (continued) 


Percent Intrazonal 3-5% of total 

trips 



Ratio of Balanced 

Productions/Attractions 

+/-3% of 

observed 

+/-5% of 

observed 

Iowa DOT Peer Review (38) 

Massachusetts Statewide Model Validation Memo – CS (24) 


+/-10% Iowa DOT Workshop (17) 

a 

See also NHTS statistics found in Phase I Model Parameters report; NHTS Add-On will provide better Florida-specific benchmarks. 




Table D.3 Checklist of Available Validation Standards from Literature: Mode Choice 


Auto Occupancy Rates – HBW 1.11/1.12/1.13/1.11 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k- 

999k/1M+ 

Auto Occupancy Rates – HBSH 1.44/1.48/1.45/1.48 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k- 

999k/1M+ 

Auto Occupancy Rates – HBSR 1.66/1.72/1.66/1.69 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k- 

999k/1M+ 

Auto Occupancy Rates – HBO 1.67/1.65/1.65/1.66 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k- 

999k/1M+ 

Auto Occupancy Rates – NHB 1.66/1.68/1.66/1.64 NCHRP 365 (15) – population sizes: 50-199K/200-499k/500k- 

999k/1M+ 

Total Area Transit Trips +/-1% FDOT Model Update Task H 4) Acceptable Error 

Trips Entering the Central Area +/-2.5% FDOT Model Update Task H 4) Acceptable Error 

Mode Splits 

Elasticity of demand with respect 

to level of service variables 

IVT parameter 

Within two 

percentage points of 

calibration targets 

SCAG model validation memo (34) 

-0.6 to -0.1 SCAG model validation memo (34) 

-0.02 to -0.03 FTA 

-0.01 to -0.05 CS summary of existing U.S. models 




Table D.3 Checklist of Available Validation Standards from Literature: Mode Choice (continued) 


Ratio: OVT/IVT parameters 

2 to 3 FTA 

1.5 to 3 HBW CS summary of existing U.S. models 

2 to 7 NW CS summary of existing U.S. models 

Implied value of time 

25%-33% of income FTA 

$2 to $5 HBW CS summary of existing U.S. models 

$0.20 to $5 NW CS summary of existing U.S. models 




Table D.4 Checklist of Available Validation Standards from Literature: Trip Assignment 


Freeway Volume-over-Count +/- 7% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Principal Arterial Volume-over-Count +/- 10% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Minor Arterial Volume-over-Count +/- 15% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Collector Volume-over-Count +/- 25% Calibration and Adjustment (6), Validation and Reasonableness (14), 


Frontage Rd Volume-over-Count +/- 25% Calibration and Adjustment (6), University of Tennessee (58), NARC (8) 

Freeway Peak Volume-over-Count 75% of links @ +/- 

20% 

Freeway Peak Volume-over-Count 50% of links @ +/- 

10% 

Major Arterial Pk Volume-over-Count 75% of links @ +/- 

30% 

Major Arterial Pk Volume-over-Count 50% of links @ +/- 

15% 

Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10) 

Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10) 

Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10), 


Central Contra Costa Transit Authority, NARC (8), Oregon DOT (10), 


Freeway Volume-over-Count +/- 7% Cambridge Systematics’ Model Validation Seminar (60) – Massachusetts 





Table D.4 Checklist of Available Validation Standards from Literature: Trip Assignment (continued) 


Arterial Volume-over-Count +/- 15% Cambridge Systematics’ Model Validation Seminar (60) – Massachusetts 


Collector Volume-over-Count +/- 20% Cambridge Systematics’ Model Validation Seminar (60) – Massachusetts 


Freeway Volume-over-Count +/- 6% Michigan DOT Urban Model Calibration Targets-1993, University of 


Principal Arterial Volume-over-Count +/- 7% Michigan DOT Urban Model Calibration Targets-1993, University of 


Minor Arterial Volume-over-Count +/- 10% Michigan DOT Urban Model Calibration Targets-1993, University of 


Collector Volume-over-Count +/- 20% Michigan DOT Urban Model Calibration Targets-1993, University of 


VMT % Distribution – Freeways a 18-23%/33-38%/40% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 


VMT % Distribution – Pr. Arterials a 37-43%/27-33%/27% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 


VMT % Distribution – Mn. Arterials a 25-28%/18-22%/18-22% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – Calibration 







VMT % Distribution – Collectors a 12-15%/8-12%/8-12% Small(50-200k)/Medium(200k-1M)/Large Urban Areas(1M+) – 

Calibration and Adjustment (6), UT(58) 

Assigned VMT-over-Count Areawide +/-5% FDOT Model Update Task C (3), Iowa DOT Peer Review (38), Validation 

and Reasonableness (14) 

Assigned VHT-over-Count Areawide +/-5% FDOT Model Update Task C (3), Iowa DOT Peer Review (38) 

Assigned VMT-over-Count by FT/AT/NL +/-15%,+/-25% FDOT Model Update Task C (3): +/- 15% VMT>100k, +/-25% 

VMT20k, +/-25% VHT 50k 

Screenline Volume/Count +/- 20% FDOT Model Update Task C (3): Screenline volume < 50k 

Screenline Volume/Count by Volume +/- 20%-65% Figure contained in Calibration and Adjustment (6), Validation and 


Screenline Volume/Count +/- 10% Massachusetts (24), BMC (35), Iowa DOT Peer Review (38), Memphis (61) 

Cutline Volume/Count +/- 15% Memphis (61) 






Cutline Volume/Count +/- 10% Iowa DOT Peer Review (38), Cambridge Systematics’ Model Validation 

Seminar (59) – MI Standards 

Screenline Volume/Count +/- 5% Cambridge Systematics’ Model Validation Seminar (59) – Michigan 


Screenline Volume/Count +/- 10% – +/- 20% Cambridge Systematics’ Model Validation Seminar (59) – FHWA 


Deviation from Counts: LT 1,000 AADT 200% Michigan DOT Urban Model Calibration Targets-1993, University of 








Deviation from Counts: 10,000-25,000 

AADT 


AADT 

20% Michigan DOT Urban Model Calibration Targets-1993, University of 


15% Michigan DOT Urban Model Calibration Targets-1993, University of 







Deviation from Counts: GT 50,000 AADT 10% Michigan DOT Urban Model Calibration Targets-1993, University of 


Deviation from Counts: LT 1,000 AADT 60% Validation and Reasonableness (14), University of Tennessee (58) 





AADT 


AADT 

25% Validation and Reasonableness (14), University of Tennessee (58) 

22% Validation and Reasonableness (14), University of Tennessee (58) 

Deviation from Counts: GT 50,000 AADT 21% Validation and Reasonableness (14), University of Tennessee (58) 

RMSE: LT 5,000 AADT 116% Oregon DOT (10), University of Tennessee (58) – percentages rounded to 

whole numbers 


whole numbers 


whole numbers 


whole numbers 







whole numbers 


whole numbers 

RMSE Areawide





RMSE: 25,000-50,000 AADT 15% Accuracy Standards for FSUTMS Model Validation (50) 

RMSE: GT 50,000 AADT 10% Accuracy Standards for FSUTMS Model Validation (50) 

RMSE by Volume Group 25%-100% FDOT Model Update Task C (3): 25% or less if Volume>50k 

Percent Error – Freeways (8/6/4 lanes) 13%/18%/29% FDOT Model Update Task C (3) 

Percent Error – Div Arterials (8/6/4 lanes) 13%/17%/25% FDOT Model Update Task C (3) 

Percent Error – Undiv Arterials 

(4/2 lanes) 

34%/56% FDOT Model Update Task C (3) 

Percent Error – One Ways (4/3/2 lanes) 13%/17%/25% FDOT Model Update Task C (3) 

Percent Error –





Transit Ridership: 2k-5k Passengers/Day +/- 35% FDOT Model Update Task H 4) Acceptable Transit Assignment Error 






Transit Ridership: >20,000 Passengers/Day +/- 15% FDOT Model Update Task H 4) Acceptable Transit Assignment Error 


a 

b 

General guidance; comparison against observed VMT is a better measure. 

Standard might be difficult to achieve on screenlines with low transit volumes. 




Table D.5 Ranges of Model Statistics from Model Validation Reports 


Florida Statistics Non-Florida Statistics 


Persons/DU (or HH) 2.23 2.66 2.50 2.69 22, 33 (NERPM updated), 35, 39, 46, 47, 48, 51 

Person Trips/DU (or HH) 7.31 11.49 8.43 9.09 22, 27, 33 (NERPM updated), 35, 39, 40, 46, 47, 48, 51, 54 

Vehicle Trips/HH (or DU) 6.72 8.70 35, 41, 49 

Person Trips/Person 3.28 3.77 3.64 3.64 22, 33 (NERPM updated), 46, 47, 48, 

Person Trips/TAZ 7,435 7,435 35 

Vehicle Trips/Person 3.00 3.00 41, 

Person Trips/Employee 6.17 13.11 4.97 4.97 22, 33 (NERPM updated), 46, 47, 48, 54, 

Employment/Population 0.33 0.33 51, 

Percent Trips by Purpose – HBW 11.56% 19.30% 12.69% 25.00% 21, 22, 33 (NERPM updated), 39, 40, 46, 47, 48, 49, 52, 54, 55 

Percent Trips by Purpose – HBSH 9.84% 20.74% 9.54% 11.30% 21, 22, 33 (NERPM updated), 46, 47, 48, 52, 54, 55, 

Percent Trips by Purpose – HBSR 9.00% 12.68% 5.56% 11.70% 22, 33 (NERPM updated), 39, 46, 47, 48, 52, 54, 55, 

Percent Trips by Purpose – HBSC 5.00% 7.20% 5.09% 10.90% 21, 22, 39, 46, 52, 54 

Percent Trips by Purpose – HBO a 14.00% 28.41% 17.29% 39.00% 21, 22, 33 (NERPM updated), 39, 40, 46, 47, 48, 49, 52, 54, 55 

Percent Trips by Purpose – NHB b 18.27% 35.25% 18.00% 32.92% 21, 22, 33 (NERPM updated), 39, 40, 46, 47, 48, 49, 52, 54, 55 

Percent Trips by Purpose – Truck-Taxi c 1.86% 11.00% 9.00% 11.00% 22, 33 (NERPM updated), 39, 40, 46, 47, 48, 49, 54, 55 

Percent Trips by Purpose – IE d 0.39% 7.85% 9.00% 14.01% 22, 33 (NERPM updated), 39, 40, 46, 47, 48, 49, 54, 55 




Table D.5 Ranges of Model Statistics from Model Validation Reports (continued) 




External-External Trip Percentages (Range) 0.00% 89.90% 0.00% 55.00% 33, 45, 46, 47, 48, 54 

Terminal Times – CBD 3 5 2 6 21, 22, 33, 40, 46, 47, 48, 52, 54 

Terminal Times – CBD Fringe 2 4 2 5 22, 33, 40, 46, 47, 48, 52, 54 

Terminal Times – Residential 1 2 1 1 22, 33, 46, 47, 48, 54 

Terminal Times – OBD 1 2 1 2 22, 33, 40, 46, 47, 48, 54 

Terminal Times – Rural 1 1 1 1 22, 33, 40, 46, 47, 48, 52, 54 

Average Trip Length – HBW 15.42 27.98 12.05 42.50 21, 22, 27, 33 (NERPM up), 35, 37, 40, 46, 47, 48, 46, 49, 51, 54 

Average Trip Length – HBSH 12.58 18.09 10.40 16.60 21, 22, 27, 33 (NERPM updated), 35, 46, 47, 48, 46 f , 54 

Average Trip Length – HBSR 12.37 19.03 11.36 11.36 22, 33 (NERPM updated), 46, 47, 48, 54 

Average Trip Length – HBSC 13.86 14.03 6.82 15.30 21, 22, 27, 35, 37, 46, 46 f , 54 

Average Trip Length – HBO a 12.54 20.25 7.98 19.20 21, 22, 27, 33 (NERPM up), 35, 37, 40, 46, 47, 48, 46 f , 49, 51, 54 

Average Trip Length – NHB b 10.15 18.75 6.40 18.30 21, 22, 27, 33 (NERPM updated), 35, 37, 40, 45, 46, 47, 48, 49, 51, 54 

Average Trip Length – Truck-Taxi c 13.99 17.54 11.50 19.62 22, 33 (NERPM updated), 35, 46, 47, 48, 49, 54 

Average Trip Length – IE d 26.17 58.01 27.80 41.23 22, 33 (NERPM updated), 35, 46, 47, 48, 49 

Percent Intrazonal – HBW 1.09% 3.16% 2.40% 7.05% 33 (NERPM updated), 40, 46, 47, 48 

Percent Intrazonal – HBSH 3.63% 11.09% 9.96% 9.96% 33 (NERPM updated), 46, 47, 48 








Percent Intrazonal – HBSR 4.14% 11.22% 21.07% 21.07% 33 (NERPM updated), 46, 47, 48 

Percent Intrazonal – HBSC N/A N/A 11.17% 11.17% 46 

Percent Intrazonal – HBO a 2.95% 5.20% 6.50% 11.20% 33 (NERPM updated), 40, 46, 47, 48 

Percent Intrazonal – NHB b 4.69% 8.90% 7.20% 8.04% 33 (NERPM updated), 40, 46, 47, 48 

Percent Intrazonal – Truck-Taxi c 4.13% 9.05% 3.07% 3.07% 33 (NERPM updated), 46, 47, 48 

Percent Drive Alone – HBW 77.26% 83.94% 71.00% 81.30% 21, 22, 33 (NERPM updated), 39, 47, 48 

Percent One Passenger – HBW 10.35% 16.78% 7.70% 10.10% 21, 22, 33 (NERPM updated), 39, 45, 48 

Percent Two+ Passengers – HBW 3.14% 6.24% 2.20% 3.55% 21, 22, 33 (NERPM updated), 39, 45, 48 

Percent Transit – HBW e 0.39% 1.23% 6.20% 15.30% 21, 22, 33 (NERPM updated), 39, 45, 48 

Percent Drive Alone – All Trips 42.96% 55.11% 33.30% 48.40% 21, 22, 27, 33 (NERPM updated), 39, 48 

Percent One Passenger – All Trips 39.50% 30.06% 12.10% 26.50% 21, 22, 33 (NERPM updated), 39, 48 

Percent Two+ Passengers – All Trips 17.30% 21.14% 7.30% 20.70% 21, 22, 33 (NERPM updated), 39, 48 

Percent Shared Ride – All Trips N/A N/A 42.50% 42.50% 27 

Percent Transit – All Trips e 0.24% 0.54% 3.08% 16.40% 21, 22, 27, 33 (NERPM updated), 35, 38, 48 

Note: 

“NERPM Updated” indicates statistics referenced from latest version of Cube-Voyager model rather than taken directly from TRANPLAN 

validation report. 

a HBO includes a variety of special trip purposes, depending on the model, airport, college, and shop (e.g., MTC). 

b NHB includes combined purposes for NHB Work and NHB Nonwork, where appropriate; PSRC model refers to these trips as Work-Other and Other- 

Other. 

c Truck-Taxi includes all commercial vehicle trips (might not be included in percentage totals for models that only summarize person trip percentages 

(e.g., MTC). 

d Internal-External trips include vehicle trips with one trip end inside and one trip end outside the model boundary; some models do not include IE as a 

separate trip purpose. 

e Percent transit includes nonmotorized trips, where these are separately accounted for (e.g., MTC-10.5%, PSRC-6.7%). 

f ARC model documentation only provides average trip lengths by income group for home-based trips; lower and upper ends of range included where 

appropriate. 




Table D.6 Ranges of Model Statistics from Model Validation Reports 


Florida Statistics 

Non-Florida Statistics 


Auto Occupancy Rates – HBW 1.10 1.12 1.06 1.16 27, 35, 37, 40, 45, 46, 47, 48, 51, 52 

Auto Occupancy Rates – HBSH 1.43 1.54 1.27 1.96 21, 27, 35, 46, 47, 48, 52 

Auto Occupancy Rates – HBSR 1.43 1.54 1.72 2.47 46, 47, 48, 52 

Auto Occupancy Rates – HBSC N/A N/A 1.00 2.45 27, 35, 37, 46, 52 

Auto Occupancy Rates – HBO 1.43 1.54 1.30 2.07 21, 27, 35, 37, 40, 45, 46, 47, 48, 51, 52 

Auto Occupancy Rates – NHB 1.12 1.65 1.17 1.96 21, 27, 35, 37, 40, 45, 46, 47, 48, 51, 52 

Freeway/Expressway Volume-over-Count 0.98 1.07 0.86 1.15 22, 27, 33 (NERPM updated), 40, 44, 46, 48, 49, 52, 54 

Divided/Principal Arterial Volume-over- 

Count a 

Undivided/Minor Arterial Volume-over- 

Count a 

0.97 1.06 0.89 1.02 22, 27, 33 (NERPM updated), 40, 44, 46, 47, 48, 49, 52, 54 

0.93 1.04 0.77 1.07 22, 27, 33 (NERPM updated), 40, 44, 46, 47, 48, 49, 52, 54 

Collector Volume-over-Count b 0.85 0.98 0.37 1.05 22, 27, 33 (NERPM updated), 40, 44, 46, 47, 48, 49, 52, 54 

One-Way Volume-over-Count 0.97 1.39 0.71 0.71 22, 33 (NERPM updated), 46, 48, 54 

Ramp Volume-over-Count 0.90 1.33 N/A N/A 22, 33 (NERPM updated), 46, 48, 54 

HOV Lane Volume-over-Count 1.10 1.10 N/A N/A 54 

Toll Road Volume-over-Count 0.96 1.00 N/A N/A 22, 33 (NERPM updated), 46, 48, 54 








CBD Volume-over-Count 0.96 1.20 0.88 0.93 22, 33 (NERPM updated), 46, 47, 48, 54 

CBD Fringe Volume-over-Count 0.90 1.03 0.91 0.91 22, 33 (NERPM updated), 46, 47, 48, 54 

Residential Volume-over-Count 0.99 1.02 0.97 1.04 22, 33 (NERPM updated), 46, 47, 48, 54 

OBD Volume-over-Count 0.95 1.06 0.91 0.91 22, 33 (NERPM updated), 46, 47, 48, 54 

Rural Volume-over-Count 0.97 1.07 0.91 1.12 22, 27, 33 (NERPM updated), 37, 46, 47, 48, 54 

Screenline Volume/Count 0.70 2.01 0.69 1.22 22, 27, 33 (NERPM up), 35, 37, 39 (part), 40, 41, 44, 45, 46, 47, 48, 54, 55 

Screenline Volume/Count – Externals 0.95 1.05 1.00 1.00 22, 27, 33 (NERPM updated), 44, 46, 47, 48 

Cutline Volume/Count – All (if different) N/A N/A 0.88 1.13 27 

Nonscreenline Volume/Count (if available) 0.98 1.01 N/A N/A 22, 33 (NERPM updated), 47, 48, 54 

Assigned VMT-over-Count Areawide 1.00 1.02 0.95 1.00 33 (NERPM updated), 35, 37, 47, 48, 53, 54, 55 

Assigned VMT-over-HPMS Areawide N/A N/A 0.86 0.86 35 

Assigned VMT per Person N/A N/A 25.97 25.97 51 

Assigned VMT per HH 70.81 70.81 62.20 62.20 41, 54 

Assigned VHT-over-Count Areawide 1.00 1.03 N/A N/A 33 (NERPM updated), 47, 48, 55, 55 

Assigned VMT per HH 1.51 1.51 N/A N/A 54 

Assigned volume-over-Count Areawide 0.98 1.02 0.93 1.03 21, 33 (NERPM updated), 37, 40, 41, 44, 47, 48, 49, 52, 54, 55 








RMSE: LT 5,000 AADT c 45.000% 80.528% 14.800% 106.600% 22, 33 (NERPM updated), 37, 40, 41, 44, 45, 46, 47, 48, 54, 55 

RMSE: 5,000-10,000 AADT 28.000% 37.520% 32.740% 51.100% 22, 33 (NERPM updated), 37, 40, 41, 44, 45, 46, 47, 48, 54, 55 




RMSE: 40,000-50,000 AADT 2.000% 15.014% 7.200% 25.800% 22, 33 (NERPM updated), 37, 40, 41, 44, 45, 46, 48, 55 

RMSE: 50,000-60,000 AADT 10.990% 14.260% 3.500% 25.800% 22, 33 (NERPM updated), 37, 40, 44, 45, 46, 48, 55 

RMSE: 60,000-70,000 AADT 6.800% 13.520% 8.010% 25.800% 22, 33 (NERPM updated), 37 (60k+), 40, 44, 45, 48, 55 

RMSE: 70,000-80,000 AADT 8.170% 18.197% 3.500% 25.800% 22, 33 (NERPM updated), 44, 45, 48, 55 

RMSE: 80,000-90,000 AADT 4.900% 5.740% 4.200% 11.100% 22, 44, 45 (80k+), 55 

RMSE: 90,000-100,000 AADT 9.890% 10.100% 4.200% 4.200% 22, 44, 55 

RMSE: GT 100,000 AADT N/A N/A 2.600% 2.600% 44 

RMSE Areawide 24.000% 33.846% 23.400% 43.300% 22, 33 (NERPM updated), 35, 37, 40, 41, 45, 46, 48, 51, 52, 54 

Estimated-over-Observed Transit Trips 0.94 1.11 1.02 1.30 21, 22, 27, 33, 39, 55 

a Some models categorize arterials into principle and minor arterials instead of divided and undivided. 

b Some models report separate collector and local street statistic. 

c Some models used different RMSE volume groups (e.g., Knoxville) or functional class (Boise) so lows and highs might reflect subcategories of those 

listed. 


Appendix E 

Supplemental Model Metrics



Table E.1 

Supplemental Model Metrics 


Total 

Population 

Total 

Households 

Total 

Employment 

Number 

of TAZs 

Person 

Trips 

Total Trip 

Productions 

(with NHB, TT, 

IE) 

Home- 

Based Trip 

Productions 

Home-Based 

Unbalanced 

Trip 

Attractions 

Total VMT 

Employment 

/Population 

Persons/ 

Dwelling 

Unit 

Persons/ 

TAZ 

Person 

Trips/ 

TAZ 

Person Trips per 

Population 

Person 

Trips/DU 

HBW 

Person 

Trips/Emp 

Unbalanced 

Attractions/ 

Productions 

VMT/ 

Person 

VMT/ 

Household 

Bay County Validation (Panama 

City) 

168,535 84,889 74,927 239 720,236 826,220 566,036 516,862 4,088,663 0.44 1.99 705 3,014 4.27 8.48 1.74 0.91 24.26 48.16 

CFRPM_IV (Orlando) 2,944,843 1,185,759 1,629,349 3,700 10,355,764 11,944,368 7,089,019 12,155,335 84,320,904 0.55 2.48 796 2,799 3.52 8.73 1.71 28.63 71.11 

NERPM (Jacksonville) 1,079,345 424,986 530,372 1,312 3,757,219 4,208,597 2,694,480 4,170,337 31,556,792 0.49 2.54 823 2,864 3.48 8.84 1.55 29.24 74.25 

Polk County TPO 481,360 185,249 207,070 630 1,637,033 1,950,064 1,159,642 1,747,144 12,859,351 0.43 2.60 764 2,598 3.40 8.84 1.48 1.51 26.71 69.42 

TBRPM-TR1 (Tampa) a 2,407,329 1,033,987 1,319,002 2,100 8,021,765 8,138,287 6,339,653 6,347,487 57,814,304 0.55 2.33 1,146 3,820 3.33 7.76 1.38 1.00 24.02 55.91 

TC2000-TR2 (Treasure Coast) 434,000 181,449 159,110 700 2,046,934 2,085,717 N/A N/A 12,847,935 0.37 2.39 620 2,924 4.72 11.28 29.60 70.81 

Boise2002 (Boise) 432,175 158,374 241,411 534 2,037,200 2,037,200 1,418,480 1,976,359 0.56 2.73 809 3,815 4.71 12.86 1.36 1.39 

CHATT_FR1 (Chattanooga) 393,138 157,356 286,643 416 1,267,211 1,663,393 834,728 1,694,779 12,775,491 0.73 2.50 945 3,046 3.22 8.05 0.85 2.03 32.50 81.19 

MACOG2030cali (South Bend) 444,509 169,651 308,905 446,753 658,093 N/A 10,545,630 0.69 2.62 1.01 6.15 1.13 23.72 62.16 

Nashville_TDM (Nashville) 1,206,665 471,298 833,862 1,440 4,050,195 4,482,137 2,317,768 2,900,872 31,796,875 0.69 2.56 838 2,813 3.36 8.59 1.55 1.25 26.35 67.47 

Memphis 1,499,124 592,122 533,378 1,237 3,115,313 2,464,584 2,423,139 26,881,500 0.36 2.53 1,212 2,518 2.08 5.26 1.47 0.98 17.93 45.40 

Atlanta 2,027 118,149,790 3.21 8.15 

Charleston, SC 572,373 209,968 261,488 628 1,600,407 1,873,159 1,086,765 971,507 14,297,164 0.46 2.73 911 2,548 2.80 7.62 0.89 24.98 68.09 

Knoxville 761,346 310,412 457,796 717 2,608,824 2,925,041 1,695,159 24,159,507 0.60 2.45 1,062 3,639 3.43 8.40 0.82 31.73 77.83 

a Used VMT number from 2000 model as 1999 model VMT estimate did not appear to be correct (1999 model used for all other statistics in TBRPM). 

Florida DOT, Systems Planning Office E-1



E-2 Florida DOT, Systems Planning Office

Appendix F 

Draft Calibration-Validation Outline/Checklist



Draft Calibration-Validation Outline/Checklist 

Table F.1 

Type of Model Application: Regional and MPO LRTPs, and Congestion Management Plans 

Models with Transit Networks 

Model Step 

Model Statistic to Evaluate 


Low High 

Accuracy 

Standard 

Recommended Comparisons and 

Calculation Methods/Comments 

Input Data Socioeconomic Data Cube, GIS Visual and Statistical 

Comparisons/Checks 

Document checks for resident, employment 

Persons/DU (or HH) 2.00 2.70 N/A NHTS > 2.46 FL – 2.59 U.S. 

Employment/Population Ratio 0.35 0.75 N/A 

Autos/DU (or HH) 1.75 2.10 N/A 

Approximate Population/TAZ N/A 3,000 N/A Recommendations from TAZ White Paper 


Cube, GIS visual and statistical comparisons/checks Check hwy network, prohibitors, tolls, paths 

Highway Speed Data ensure logical hierarchy by AT/FT/NL; survey chk Will provide acceptable ranges in sep. table 

Transit Network Data chk access links; chk routing against GIS data Checks for transit network, access, paths 

Terminal Times – CBD 3 5 N/A 

Ranges have been narrowed to exclude outliers 

Terminal Times – CBD Fringe 2 4 N/A 

Terminal Times – Residential 1 2 N/A 

Florida DOT, Systems Planning Office F-1



Table F.1 


Models with Transit Networks (continued) 




Low High 

Accuracy 




Terminal Times – OBD 1 3 N/A 

Terminal Times – Rural 1 1 N/A 

Trip Generation Person Trips/Person 3.3 4.0 N/A 

Person Trips/DU (or HH) 8.0 10.0 N/A 

Modify the generation script for the Olympus 

model to automatically calculate all aggregate 

rates; NHTS values added 

HBW Person Trips/Employee 1.20 1.55 N/A 

Person Trips/TAZ N/A 15,000 N/A Recommendations from TAZ White Paper 

Percent Trips by Purpose – HBW 12% 20% N/A 

Percent Trips by Purpose – HBSH 10% 20% N/A 

Compare against other similar models; if 

household travel survey data are available, 

include comparisons against survey statistics 

Percent Trips by Purpose – HBSR 9% 12% N/A 

Percent Trips by Purpose – HBSC 5% 8% N/A Most FL models don’t include this purpose 

Percent Trips by Purpose – HBO 14% 28% N/A 

Percent Trips by Purpose – NHB 20% 33% N/A 

These purposes are further sub-categorized in 

some models 

Unbalanced Attractions/Productions (% Diff.) N/A N/A +/-10-50% Review differences in total trips by purpose 

F-2 Florida DOT, Systems Planning Office



Table F.1 






Low High 

Accuracy 




Trip Distribution Average Trip Length – HBW (minutes) 15 28 N/A 

Average Trip Length – HBSH (minutes) 10 18 N/A 

Average Trip Length – HBSR (minutes) 11 19 N/A 

Average Trip Length – HBSC (minutes) 9 16 N/A 

Same comments on percent trips by purpose 

apply to average trip length; strong preference 

for comparing against HH travel surveys rather 

than range; FDOT should decide if range of 

average trip lengths is desired for areas that do 

not have recent HH travel survey data… could 

recommend comparisons to other similar 

models, NHTS, CTPP instead 

Average Trip Length – HBO (minutes) 10 20 N/A 

Average Trip Length – NHB (minutes) 10 18 N/A 

Average Trip Length – Truck-Taxi (minutes) 12 20 N/A 

Average Trip Length – IE (minutes) 27 45 N/A 

Mean Trip Length, Observed Total Trips by 

Purp. 

Trip Length Frequency Distribution by 

Purpose 

N/A N/A +/-3% of 

observed 

N/A N/A Visual 

comparison 

Comparisons of trip length frequency 

distributions require processed data from HH 

travel survey for same region 

Coincidence Ratios by Purpose N/A N/A GT 65%-70% 

Percent Intrazonal – HBW 1% 4% N/A 

Percent Intrazonal – HBSH 3% 9% N/A 




Percent Intrazonal – HBSR 4% 10% N/A 




Table F.1 






Low High 

Accuracy 




Percent Intrazonal – HBSC 10% 12% N/A Most FL models don’t include this purpose 

Percent Intrazonal – HBO 3% 7% N/A 

Percent Intrazonal – NHB 5% 9% N/A 




Percent Intrazonal – Total Trips 3% 5% N/A 

Percent Intrazonal, Observed Total Trips N/A N/A +/-3%-+/-5% Requires household survey data 

Mode Choice Total Area Transit Trips +/-1% +/-2% N/A Need estimate of daily linked transit trips 

HBW trips between Districts 

compare model trip table against CTPP or HH survey District level O-D check for reasonableness 

Mean Trip Length, Observed Transit Trips N/A N/A +/-5-15% of obs. Need statistically valid sample of transit riders 

Mode Splits by Calibration Targets N/A N/A +/-2% Document source of calibration targets 

Elasticity of demand with respect to level of 

service variables 

-0.10 -0.70 N/A 

IVT parameter – HBW -0.01 -0.05 N/A 

IVT parameter – HBNW -0.007 -0.033 N/A 

IVT parameter – NHB -0.01 -0.05 N/A 

Ratio: OVT/IVT parameters – HBW 1.5 3.0 N/A 

Ratio: OVT/IVT parameters – HBNW 2.0 6.0 N/A 

Ratio: OVT/IVT parameters – NHB 2.0 7.0 N/A 




Table F.1 






Low High 

Accuracy 




Implied value of time – % of income $2.00 $7.00 N/A 

Implied value of time – HBW $0.50 $5.00 N/A 

Implied value of time – NW $0.20 $5.00 N/A 

Percent Drive Alone – HBW 70% 85% N/A 

Percent One Passenger – HBW 9% 16% N/A 

Ranges provided for small-medium MPO areas 

for comparison against CTPP/JTW data for the 

specific region being modeled; additional 

checking for large MPOs 

Percent Two+ Passengers – HBW 2% 6% N/A 

Percent Transit – HBW 0.5% 10% N/A 

Percent Drive Alone – All Trips 35% 50% N/A 

Percent One Passenger – All Trips 15% 35% N/A 

Ranges provided for small-medium MPO areas 

for comparison against CTPP/JTW data for the 

specific region being modeled; additional 

checking for large MPOs 

Percent Two+ Passengers – All Trips 8% 20% N/A 

Percent Transit – All Trips 0.2% 9% N/A 




Table F.1 

Type f Model Application: Regional and MPO LRTPs, and Congestion Management Plans 





Low High 

Accuracy 




Highway 

Assignment 

Freeway Volume-over-Count N/A N/A +/-7% 

Divided Arterial Volume-over-Count N/A N/A +/-10% 

Undivided Arterial Volume-over-Count N/A N/A +/-15% 

Collector Volume-over-Count N/A N/A +/-20% 

Volume-over-count ratios are calculated and 

visually displayed by link; standards assume 

higher order facilities should achieve higher 

level of statistical validity; concerns over 

validity of summing volume/count ratios; 

original standards used principal and minor 

arterial categories. 

Frontage Rd Volume-over-Count N/A N/A +/-25% 


Major Arterial Pk Volume-over-Count 75% of links @ +/-30%; 50% of links @ +/-15% 

Assigned VMT-over-Count Areawide N/A N/A +/-3% 

Assigned VHT-over-Count Areawide N/A N/A +/-3% 

Assigned VMT-over-Count by FT/AT/NL N/A N/A +/-12% 

Only applicable for time-of-day or peak-period 

models; only standards found thus far 

Volume-over-count ratios using VMT from 

model volumes and counts; statistics are 

summed for FT/AT/NL; concerns over validity 

of using VHT without speed data/validation 

Assigned VHT-over-Count by FT/AT/NL N/A N/A +/-12% 




Table F.1 






Low High 

Accuracy 




Percent Freight Truck VMT 1.0% 5.0% N/A 

Percent Nonfreight Truck VMT 1.5% 15.0% N/A 

Requires classification counts to validate; 

loaded network must include 1-2 truck purps; 

stats from FHWA/CS Comm Vehicle Study 

Percent Total Commercial VMT (Truck+Other) 3.5% 25.0% N/A 

VMT per HH: Small/Large Urban 60 75 N/A 

VMT per Person: Small/Large Urban 24 32 N/A 

Screenline Volume/Count < 35k N/A N/A +/-20% 

Screenline Volume/Count 35k-70k N/A N/A +/-15% 

Presently requires off-line calculation; should be 

added to Olympus model scripts 

Should distinguish between screenlines and 

cutlines in Olympus model; vary by volume 

Screenline Volume/Count > 70k N/A N/A +/-10% 

Screenline Volume/Count – External Cordon N/A N/A +/-1% 

Cutline Volume/Count N/A N/A +/-15% 




Table F.1 






Low High 

Accuracy 




RMSE: LT 5,000 AADT N/A N/A +/-45%-+/-100% 

RMSE: 5,000-9,999 AADT N/A N/A +/-35%-+/-45% 

RMSE: 10,000-14,999 AADT N/A N/A +/-27%-+/-35% 

Ensure consistency in calculation of RMSE by 

direction; confirm if these values should be 

adjusted further to account for two-way 

volumes; wide variation in acceptability 

throughout the U.S. 

RMSE: 15,000-19,999 AADT N/A N/A +/-25%-+/-30% 

RMSE: 20,000-29,999 AADT N/A N/A +/-15%-+/-27% 

RMSE: 30,000-49,999 AADT N/A N/A +/-15%-+/-25% 

RMSE: 50,000-59,999 AADT N/A N/A +/-10%-+/-20% 

RMSE: 60,000+ AADT N/A N/A +/-10%-+/-19% 

RMSE Areawide N/A N/A +/-35%-+/-45% 

Percent Error –



Table F.1 






Low High 

Accuracy 




Transit 

Assignment 

Estimated-over-Observed Transit Boardings +/- 3% +/- 9% N/A requires estimate of daily boardings by route 

Acceptable Error – Transit Screenlines N/A N/A +/-10%-+/-20% Olympus scripting – format for transit 

“counts” 

Transit Ridership: 20,000 Passengers/Day +/- 20% +/- 15% N/A 





Table F.2 

Type of Model Application: Regional and MPO LRTPs, and Congestion Management Plan 

Highway Only Models 




Low High 

Accuracy 


*Recommended Comparisons and 


Input Data Socioeconomic Data Cube, GIS visual and statistical comparisons/checks 

Persons/DU (or HH) 2.00 2.70 N/A 



Approximate Population/TAZ 1,200 3,000 N/A 


Cube, GIS visual and statistical comparisons/checks 

Highway Speed Data ensure logical hierarchy by AT/FT/NL; survey chk 




Table F.2 


Highway Only Models (continued) 




Low High 

Accuracy Standard 











Person Trips/TAZ N/A 15,000 N/A 







Table F.2 






Low High 




Percent Trips by Purpose – HBSC 5% 8% N/A 



Unbalanced Attractions/Productions (% Diff.) N/A N/A +/-50% 












Table F.2 






Low High 





Purp. 

Trip Length Frequency Distribution by 

Purpose 

N/A N/A +/-3% of observed 

N/A N/A Visual comparison 





Percent Intrazonal – HBSC (if applicable) 10% 12% N/A 




Percent Intrazonal, Observed Total Trips N/A N/A +/-3%-+/-5% 

Mode Choice HBW trips between Districts Compare model trip table against CTPP or HH survey 




Table F.2 






Low High 




Auto Occupancy Rates – HBW 1.05 1.10 N/A NHTS; compare against region’s 

JTW/CTPP 

Auto Occupancy Rates – HBSH 1.50 1.80 N/A 

Auto Occupancy Rates – HBSR 1.70 1.95 N/A 

Derived from NHTS FL and U.S. with 

some consideration of NCHRP 365 

Auto Occupancy Rates – HBO 1.65 1.95 N/A 

Auto Occupancy Rates – NHB 1.60 1.90 N/A 

Vehicle Trips/DU 9.00 9.30 N/A +/-0.15 from mean 

Highway 

Assignment 


Divided/Principal Arterial Volume-over- 

Count 

Minor/Undivided Arterial Volume-over- 

Count 

N/A N/A +/-10% 

N/A N/A +/-15% 






Table F.2 






Low High 








VMT per HH: Small-Medium Urban 60 75 N/A 

VMT per Person: Small-Medium Urban 24 32 N/A 







RMSE: 5,000-9,999 AADT N/A N/A +/-35%-+/-45% 

RMSE: 10,000-14,999 AADT N/A N/A +/-27%-+/-35% 




Table F.2 






Low High 




RMSE: 15,000-19,999 AADT N/A N/A +/-25%-+/-30% 

RMSE: 20,000-29,999 AADT N/A N/A +/-15%-+/-27% 

RMSE: 30,000-49,999 AADT N/A N/A +/-15%-+/-25% 

RMSE: 50,000-59,999 AADT N/A N/A +/-10%-+/-20% 

RMSE: 60,000+ AADT N/A N/A +/-10%-+/-19% 


* Percent Error –




Table F.3 

Type of Model Application: FTA New Starts Projects 




Low High 

Accuracy 




Input Data Socioeconomic Data Cube, GIS visual and statistical 

comparisons/checks 

Document checks for resident, employment 




Approximate Population/TAZ 1,200 3,000 N/A 


Cube, GIS visual and statistical 

comparisons/checks 

Highway Speed Data ensure logical hierarchy by AT/FT/NL; survey chk Highway/transit speed/delay survey preferred 

Transit Network Data chk access links; chk routing against GIS data 







Table F.3 

Type of Model Application: FTA New Starts Projects (continued) 




Low High 

Accuracy 













Percent Trips by Purpose – HBSC 5% 8% N/A 



Unbalanced Attractions/Productions (% Diff.) N/A N/A +/-50% 







Table F.3 





Low High 

Accuracy 










Purp. 

N/A N/A +/-3% of 

observed 

Trip Length Frequency Distribution by Purpose N/A N/A Visual 

comparison 





Percent Intrazonal – HBSC 10% 12% N/A 






Table F.3 





Low High 

Accuracy 





Percent Intrazonal, Observed Total Trips N/A N/A +/-3%-+/-5% 

Mode Choice Total Area Transit Trips +/-1% +/-2% N/A 

Trips between Districts 

compare model trip table against CTPP or HH 

survey 

Mean Trip Length, Observed Transit Trips N/A N/A +/-5% of 

observed 

Mode Splits by Calibration Targets N/A N/A +/-2% 

Elasticity of demand with respect to level of 

service variables 

-0.10 -0.70 

IVT parameter-HBW -0.02 -0.03 

Ratio: OVT/IVT parameters-HBW 2.0 3.0 

Ratio: OVT/IVT parameters – HBNW 2.0 3.0 

Ratio: OVT/IVT parameters – NHB 2.0 3.0 

Implied value of time – % of income 25% 33% 

Implied value of time – HBW $2.00 $7.00 




Table F.3 





Low High 

Accuracy 




Implied value of time – NW $0.20 $5.00 $0.50 Low HBNW 

Percent Drive Alone – HBW 70% 85% N/A 

Percent One Passenger – HBW 9% 16% N/A 

Percent Two+ Passengers – HBW 2% 6% N/A 

Percent Transit – HBW 0.5% 10% N/A 

Percent Drive Alone – All Trips 35% 50% N/A 

Percent One Passenger – All Trips 15% 35% N/A 

Percent Two+ Passengers – All Trips 8% 20% N/A 

Percent Transit – All Trips 0.2% 9% N/A 

Highway 

Assignment 



VMT per HH: Small/Large Urban N/A N/A +/-12% 

VMT per Person: Small/Large Urban N/A N/A +/-12% 






Table F.3 





Low High 

Accuracy 








RMSE: 5,000-9,999 AADT N/A N/A +/-35%-+/-45% 

RMSE: 10,000-14,999 AADT N/A N/A +/-27%-+/-35% 

RMSE: 15,000-19,999 AADT N/A N/A +/-25%-+/-30% 

RMSE: 20,000-29,999 AADT N/A N/A +/-15%-+/-27% 

RMSE: 30,000-49,999 AADT N/A N/A +/-15%-+/-25% 

RMSE: 50,000-59,999 AADT N/A N/A +/-10%-+/-20% 

RMSE: 60,000+ AADT N/A N/A +/-10%-+/-19% 


Transit 

Assignment 

Estimated-over-Observed Transit Boardings +/- 3% +/- 9% N/A 

Acceptable Error – Transit Screenlines N/A N/A +/-10%-+/-20% 

Transit Ridership:



Table F.3 





Low High 

Accuracy 




Transit Ridership: 1k-2k Passengers/Day +/- 100% +/- 65% N/A 




Transit Ridership: >20,000 Passengers/Day +/- 20% +/- 15% N/A 

* Comments only provided where different from “LRTP Transit” tab, to eliminate duplication. 





Table F.4 

Type of Model Application: Subarea Studies, Comprehensive Plans, Campus Master Plans, 

Sector Plans, and Special Area Plans 




Low High 

Accuracy 




Input Data Socioeconomic Data Cube, GIS visual and statistical comparisons/checks document checks for resident, employment 


compare subarea and regional statistics 



Approximate Population/TAZ 1,200 3,000 N/A consider for zone splitting 

Highway Network Data Cube, GIS visual and statistical comparisons/checks check within subarea 

Transit Network Data chk access links; chk routing against GIS data only if subarea includes transit network lines 













Table F.4 


Sector Plans, and Special Area Plans (continued) 




Low High 

Accuracy 
















Percent Intrazonal – Total Subarea Trips 3% 5% N/A 

Mode Choice Total Subarea Transit Trips +/-1% +/-2% N/A only if subarea includes transit network lines 

Mode Splits by Calibration Targets 

compare model trip table against CTPP or HH 

survey 

only if subarea includes transit network lines 




Table F.4 






Low High 

Accuracy 




Highway 

Assignment 








Major Arterial Pk Volume-over-Count 75% of links @ +/-30%; 50% of links @ +/-15% 


check within area that has peak hour model 

estimates 






only relevant if subarea is port/airport 


Percent Total Commercial VMT 

(Truck+Other) 

3.5% 25.0% N/A 







Table F.4 






Low High 

Accuracy 








RMSE: 5,000-9,999 AADT N/A N/A +/-35%-+/-45% 

RMSE: 10,000-14,999 AADT N/A N/A +/-27%-+/-35% 

RMSE: 15,000-19,999 AADT N/A N/A +/-25%-+/-35% 

RMSE: 20,000-29,999 AADT N/A N/A +/-15%-+/-27% 

RMSE: 30,000-49,999 AADT N/A N/A +/-15%-+/-25% 

RMSE: 50,000-59,999 AADT N/A N/A +/-10%-+/-20% 

RMSE: 60,000+ AADT N/A N/A +/-10%-+/-19% 


Percent Error –




Table F.5 

Type of Model Application: Corridor/Toll Feasibility Studies, Interstate Master Plans, 

IJRs/IMRs, PD&E Studies, Final Design Studies 




Low High 

Accuracy 




Input Data Socioeconomic Data Cube, GIS visual and statistical comparisons/checks Document checks for resident, employment 

Approximate Population/TAZ 1,200 3,000 N/A Consider for zone splitting near corridor 

Highway Network Data Cube, GIS visual and statistical comparisons/checks Check within subarea surrounding corridor 

Highway Network Paths and Travel Times Cube path traces between nodes and zones Check reasonableness/speed-delay check 

Transit Network Data chk access links; chk routing against GIS data Only if corridor includes transit network 

lines 


Depends on size of study area 




Person Trips for adjacent TAZs assess reasonableness of zonal trip estimates Check for reasonableness based on LU types 




Table F.5 


IJRs/IMRs, PD&E Studies, Final Design Studies (continued) 




Low High 

Accuracy 













Desire Line Analyses Assess reasonableness of distribution patterns Check for reasonableness – local knowledge 












Table F.5 






Low High 

Accuracy 




Mode Choice Total Subarea Transit Trips +/-1% +/-2% N/A Only if corridor includes transit network 

lines 

Highway 

Assignment 


Check for links along corridor and 

intersecting roadways 





Select Link Analysis reasonableness check: surveys, local knowledge Assess corridor trip movements/diversion 

Select Zone Analysis reasonableness check: surveys, local knowledge Review for nearby activity centers 

Review Ramp and/or Turn Volumes reasonableness check: counts, local knowledge Check for reasonableness against counts 


Compare subarea and regional statistics 







Table F.5 






Low High 

Accuracy 





Only relevant if major freight corridor 


Percent Total Commercial VMT 

(Truck+Other) 

3.5% 25.0% N/A 






Check within subarea; add more cutlines if 

special corridor traffic counts are available 







Table F.5 






Low High 

Accuracy 






RMSE: 5,000-9,999 AADT N/A N/A +/-35%-+/-45% 

RMSE: 10,000-14,999 AADT N/A N/A +/-27%-+/-35% 

RMSE: 15,000-19,999 AADT N/A N/A +/-25%-+/-35% 

RMSE: 20,000-29,999 AADT N/A N/A +/-15%-+/-27% 

RMSE: 30,000-49,999 AADT N/A N/A +/-15%-+/-25% 

RMSE: 50,000-59,999 AADT N/A N/A +/-10%-+/-20% 

RMSE: 60,000+ AADT N/A N/A +/-10%-+/-19% 


Percent Error –




Table F.6 

Type of Model Application: DRIs, Concurrency Applications, and Other Site Impact Studies 

Mostly Future Year Model Checks 




Low High 

Accuracy 




Input Data Socioeconomic Data Cube, GIS visual and statistical comparisons/checks Document checks for resident, employment 

Approximate Population/TAZ 1,200 3,000 N/A Check site impact TAZs and surrounding 

TAZs 

Highway Network Data Cube, GIS visual and statistical comparisons/checks Verify only committed roadways are included 

Highway Network Paths and Travel Times Cube path traces between nodes and zones Check reasonableness, poss. speed-delay 

Transit Network Data chk access links; chk routing against GIS data Only if site is served by transit 

Trip Generation Person Trips for site impact TAZs confirm consistency between model and ITE trips Check against ITE Trip Generation 

Person Trips/TAZ N/A 15,000 N/A Consider zone splitting of adjacent zones 

Trip Distribution Check distribution of site-generated trips logic check: DRI trips to nearby and distant areas Summarize by district 

Mode Choice Total Vehicle Trips for site impact TAZs confirm consistency between model and ITE trips Summarize by district 




Table F.6 

Type of Model Application: DRIs, Concurrency Applications, and Other Site Impact Studies 

Mostly Future Year Model Checks 




Low High 

Accuracy 




Highway 

Assignment 


Check along corridor(s) accessing site 





Select Link Analysis reasonableness check: surveys, local knowledge Assess corridor trip movements/diversion 

Select Zone Analysis reasonableness check: surveys, local knowledge Review dispersion of site-generated trips 

Review Ramp and/or Turn Volumes reasonableness check: counts, local knowledge Check for reasonableness near to site 


Check along corridors leading to site 





Comments only provided where different from “LRTP Transit” tab, to eliminate duplication. 

* 


Appendix G 

Calculation of Percent Highway Assignment Errors by 

Volume Group



Calculation of Percent Highway Assignment Errors by Volume Group 

Table G.1 Calculation of Percent Highway Assignment Errors by Volume Group 

Initial Thresholds – Mid Count 


Volumes Based on Standards 

Statistic Low High Low Med High 

Iowa 


Mid-Range 

Count Over/Under FTYPE LANES 

Percent Error –




Initial Thresholds – High Count 




Iowa 

Standards High-End Count Over/Under FTYPE LANES 

Percent Error –




Revised Thresholds – Mid Count 




Iowa 


Mid-Range 


Percent Error –




Revised Thresholds – High Count 




Iowa 


Mid-Range 


Percent Error –

Appendix H 

Best Practices Bibliography



Best Practices Bibliography 

1. Travel Model Improvement Program; List Available for Exchange of Information Relevant 

to TMIP: Correlation Coefficient versus Coefficient of Determination; various authors; 

January 2008. 

2. Transportation Consulting Group; State University System Transportation Study, BOR- 

052; prepared for the State of Florida Board of Regents, July 1993. 

3. Barton-Aschman Associates, Inc. and Transportation Research Board; Report 365 – Travel 

Estimation Techniques for Urban Planning; National Cooperative Highway Research 

Program, 1998. 

4. Cambridge Systematics, Inc.; FSUTMS-Cube Framework Phase I: Default Model Parameters; 

prepared for Florida DOT Systems Planning Office, October 2006. 

5. Post, Buckley, Schuh, and Jernigan, Inc.; Highway Network (HNET) Procedural 

Enhancements Study: Final Technical Report; prepared for prepared for Florida DOT 

Systems Planning Office, March 1998. 

6. Post, Buckley, Schuh, and Jernigan, Inc.; Highway Network (HNET) Procedural 

Enhancements Study: Final User’s Manual; prepared for prepared for Florida DOT 

Systems Planning Office, March 1998. 

7. Cambridge Systematics, Inc. in Association with AECOM Consult; A Recommended 

Approach to Delineating Traffic Analysis Zones in Florida; prepared for Florida DOT 

Systems Planning Office, September 2007. 

8. Florida DOT Systems Planning Office and Cambridge Systematics, Inc.; FSUTMS 

Powered by Cube/Voyager Data Dictionary; Prepared for Florida Model Task Force, 

December 2005. 

9. Cambridge Systematics, Inc. in Association with Fehr & Peers; Wasatch Front Regional 

Council Model Sensitivity Testing Final Report; prepared for Utah Department of 


10. Schimpeler-Corradino Associates; Urban Transportation Model Update Task B: Review 

and Refinement of Standard Trip Generation; prepared for Florida DOT; June 1980. 

11. Florida Department of Transportation; Site Impact Handbook; April 2007. 

12. Institute of Transportation Engineers; Trip Generation 7 th Edition; 2003. 

Florida DOT, Systems Planning Office H-1

Appendix I 

Best Practices Model Validation Worksheet



Florida DOT, Systems Planning Office I-1



I-2 Florida DOT, Systems Planning Office






I-4 Florida DOT, Systems Planning Office




Appendix J 

Federal Transit Administration Guidance on Travel 

Demand Forecasting for New Starts Projects



Federal Transit Administration Guidance on Travel Demand 

Forecasting for New Starts Projects 

Introduction 

FTA conducts periodic workshops on Travel Forecasting for Transit New Starts Applications. 

The goal of these workshops is to share with project sponsors and their model consultants 

how FTA evaluates travel forecasts. Furthermore, the workshops serve as a forum for 

FTA to establish acceptable modeling procedures, inputs, and outputs essential for producing 

reliable forecasts that are sensitive to socioeconomic and level-of-service changes. 

The material presented in this section is a synthesis of the information that FTA provided 

during the September 2007 travel forecasting workshop in St. Louis, Missouri. 1 

FTA Requirements 

FTA provides guidance on the following key aspects of travel forecasting for New Starts: 

1. Properties of Travel Models; 

2. Rider Surveys; and 

3. Calibration and Validation. 

The subsections that follow discuss FTA’s requirements on each of these items. 

FTA Requirements – Properties of Travel Models 

FTA’s requirements for the properties of travel models are fairly broad. FTA supports a 

localized approach to travel modeling and forecasting. The rationale for such a requirement 

is that there are no standard or “correct” methods that are universally applicable to 

all regions. Models will need to reflect the fact that each metropolitan area has unique 

conditions and must be responsive to local decision-making. 

Because models are used to forecast transit ridership, it is essential that they explain the 

current transit conditions and capture the tradeoffs between travel times and costs. These 

favorable properties are heavily dependent on the model calibration and validation procedures 

(discussed in the next subsection). In addition to capturing current conditions, 

the models will need to fulfill their ultimate objective of yielding reasonable forecasts. 

Specifically, FTA requires reasonable “deltas” for ridership that are consistent with the 

1 

Travel Forecasting for New Starts Proposals – September 2007: http://www.fta.dot.gov/printer_ 

friendly/planning_environment_7276.html. 

Florida DOT, Systems Planning Office J-1



underlying socioeconomic growth as well as level-of-service improvements. Unreasonably 

high or low ridership forecasts are clear indications that the model parameters may need 

further examination. 

The evaluation of a proposed New Starts transit project relies on the cost-effectiveness 

ratio of the project. The cost-effectiveness ratio relates the cost of the project to the 

expected benefits, usually expressed as time savings, from the project. Obviously, the 

estimated cost of the project is independent of the travel modeling procedures; however, 

the expected user benefits are inextricably linked to the modeling procedures and inputs. 

A major component of FTA’s guidance on model properties, therefore, relates to the user 

benefits implied by the model. FTA requires that models adequately support the case for 

a new transit project by capturing appropriate user benefits for various market segments. 

Further, the models should be amenable to an analysis of the primary causes of the 

benefits. 

FTA recognizes that a range of modeling approaches can be used to obtain the desired 

model properties. These approaches could include either the traditional trip-based models 

or the more advanced tour and activity-based models, as long as due attention is paid 

to the model properties and the implied user benefits. 

In summary, FTA recognizes good models based on coherent forecasts. Careful calibration 

and validation coupled with rigorous quality assurance checks will help achieve the 

ultimate objective of developing models to gain insights into performance and benefits of 

the alternatives. 

FTA Requirements – Rider Surveys 

Rider surveys are an important source of current transit information, and are crucial 

to calibrating models that reflect the current conditions accurately. Where possible, 

the FTA recommends surveys before and after project opening to get a time-varying 

picture of ridership patterns and also to evaluate the model predictions. In cases 

where only the older survey data are available, the usefulness of the data in 

explaining current patterns depends to a large extent on the rate of growth in the 

metropolitan area as well as on any major transit system changes in the area. To the 

extent that these changes are minimal, FTA deems the older data acceptable for current 

day predictions. 

The success of rider surveys in capturing the current transit travel patterns depends 

on the design of the surveys in terms of the sampling plan, the questionnaire, and the 

data items included in the questionnaire. 

FTA recommends that the sampling plan be designed with the transit markets in 

mind. The transit markets are determined not only by the socioeconomic attributes 

but also by the geographic attributes such as the area type of the origin and/or destination 

of the trip. Because these markets have different response rates and different 

travel patterns, FTA urges sample allocation and survey methods that account for 

these differences and improve overall response rates. 

J-2 Florida DOT, Systems Planning Office



FTA’s guidance on questionnaire design relates to the visual and interpretational 

aspects of the survey. Specifically, the FTA recommends that the surveys be simple 

in terms of layout, readability, and wording. Attention to these three aspects can 

help avoid round-trip reporting and can provide better data on trip origins and 

destinations. 

Successful surveys are succinct. Recognizing this, FTA has identified several key 

data items that must be included in the surveys and several others that either require 

the use of discretion or are simply unnecessary. Figures J.1, J.2, and J.3 show FTA’s 

comments on the usefulness of various commonly included traveler, trip, and other 

characteristics, respectively, in rider surveys. 

In addition to the rider surveys, the FTA recommends the use of other ridership data, 

where available to inform the modeling process. These data could include on-off 

counts and park-and-ride utilization counts. 

Figure J.1 FTA Comments on Frequently Included Traveler Characteristics 

Source: FTA Workshop on Travel Forecasting for New Starts. Session 3: Properties of Models, 

PowerPoint Slide 46. http://www.fta.dot.gov/printer_friendly/planning_environment_7276.html. 




Figure J.2 FTA Comments on Frequently Included Trip Characteristics 



Figure J.3 FTA Comments on Frequently Included Other Characteristics 






FTA Requirements – Calibration and Validation 

As indicated previously, FTA emphasizes that forecasts be based on models that are 

tested rigorously against current transit ridership patterns. The FTA requires that 

the model forecasts serve as a useful basis for quantifying and understanding user 

benefits from the proposed New Starts projects. The implications of a careful calibration 

and validation methodology are three-fold: first, it necessitates better current 

data; second, it calls for a better focus on transit markets; and third, it requires better 

tests and standards. 

FTA recommends that project sponsors take advantage of the funding and guidance 

opportunities available from FTA to collect good quality “before” and “after” survey 

data. The issue of better focus on transit markets can be achieved through an 

evaluation of model performance by each trip purpose, socioeconomic group, production-attraction 

area types, and transit access modes. FTA deems the matching of 

overall target totals as an insufficient measure of model calibration. The standards 

for model calibration must rely as much on behavioral significance as they do on statistical 

significance. FTA defines validation as a valid description of travel behavior 

as well as plausible forecasts of “deltas” for the future year. FTA recommends careful 

documentation of key transit markets, current transit modes, and calibration forecast 

to help evaluate the overall effectiveness of the model for New Starts analysis. 

FTA provides guidelines on the allocation of resources to the three important tasks of 

model development, calibration, and validation. FTA believes that most models 

allocate a majority of time and budget to the development task and not nearly as 

much to the calibration and validation tasks. On the contrary, FTA recommends that 

estimation be conducted only where necessary and that the testing (calibration and 

validation) task be fully funded. This will ensure the avoidance of spurious 

forecasts. 

FTA has provided guidance on specific properties of travel models to ensure proper 

calibration and validation. FTA believes that most travel models have one or more of 

the problems listed below: 

• Unusual coefficients in mode choice models; 

• Bizarre alternative-specific constants; 

• Path/mode-choice inconsistencies; 

• Accuracy of bus running times; and 

• Stability of highway-assignment results. 

FTA proposes the following general guidelines for mode choice model coefficients: 

• In-vehicle time coefficient for home-based work (HBW) trips must be preferably 

between -0.03 and -0.02. If the coefficient falls outside the range, FTA recommends 

further analysis; 




• In-vehicle time coefficient for nonhome-based (NHB) trips must approximately be the 

same as the CIVT for HBW trips; 

• In-vehicle time coefficient for home-based other (HBO) trips must be between 0.1 and 

0.5 times the CIVT for HBW trips; and 

• The coefficient of out-of-vehicle time must ideally be between two and three times the 

corresponding coefficient for in-vehicle time. 

Table K.1 shows a summary of suggested coefficients for various time and cost coefficients 

in a typical mode choice model. It must be noted that FTA does not require mode choice 

coefficients to be exactly the same as those shown in Table K.1, but wherever the coefficients 

differ significantly from the ranges specified above, FTA recommends further 

analysis for sources of error. 

Table J.1 

Reasonable Estimates of Mode Choice Model Coefficients 

Variables 

Coefficients 

Attribute Units HBW HBO NHB 

In-Vehicle Time for (Most) Transit Modes Minutes -0.020 -0.010 -0.020 

In-vehicle Time for Commuter Rail Minutes -0.016 -0.008 -0.016 

All Out-of-Vehicle Time Minutes -0.040 -0.020 -0.040 

Drive-Access Time Minutes -0.040 -0.020 -0.040 

Transfers Number -0.100 -0.050 -0.100 

Fare (Cents) Cents -0.003 -0.0015 -0.0015 

Source: FTA Workshop on Travel Forecasting for New Starts 2006. 

Since naïve calibration leads to bad alternative-specific constants and has the cascading 

effect of producing errors in trips and benefits, FTA suggests that modelers 

ask themselves if patterns across market segments are explainable. 

FTA also suggests that there be conformity between parameters used in transit path 

selection and mode choice utility expressions for transit choices. That is, the path 

building process must weigh the various travel time and cost components in a manner 

that is consistent with the relative values of the mode choice coefficients. The 

consequences of inconsistencies include the following: 

• Better paths may look worse in mode choice; and 

• Build alternatives may lose some trips and benefits. 




FTA requires that level of service estimates for transit (and highway) must: 

• Replicate current conditions reasonably well; 

• Predict defensible deltas by comparing conditions today versus the future; and 

• Predict defensible deltas when comparing conditions across alternatives. 

FTA recommends a careful analysis of highway and transit travel times between carefully 

selected origins and destinations to understand the quality of the model networks. Spurious 

values of travel time can distort the magnitude as well as the pattern of predicted trip 

making and can adversely affect the quality of project user benefits. 

Summary 

FTA’s requirements are geared towards reasonably accounting for current patterns and 

predicting reasonable future ridership for the proposed New Starts projects. FTA does not 

provide rigid targets for parameters in travel models. Rather, FTA recommends methods 

that can be used to ensure that models reflect current travel behavior and predict reasonable 

future patterns. 

FTA’s expectations from travel models and the New Starts process can be summarized as 

follows: 

• Coherent narrative of the model parameters, inputs and outputs; 

• Regular and early communication regarding model parameters and forecasts to ensure 

that the agency/sponsor is proceeding in the proper direction; 

• Reasonable model forecasts in light of the expected land use growth, service characteristics 

and other project-related attributes; and 

• Proper documentation and uncertainty analysis, which is directly related to the 

SAFETEA-LU requirement that asks FTA to provide the U.S. Congress with an 

assessment of contractor performance. FTA will rate contractors based on the following 

measures: 

- Comparison of predicted and actual ridership; 

- Quality of documentation; 

- Uncertainty analysis, including magnitude of impact; and 

- Before and after studies for various stages, including alternatives analysis, preliminary 

engineering, preproject construction, and two years after opening.

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