Transit Modeling Guidance by Rosella Picado and ... - FSUTMSOnline

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Building the Household Classification Start with a regional distribution of households (seed) Find, for each TAZ, the marginal distributions: • Number of 1p, 2p, 3p, 4p+ households • Number of 0w, 1w, 2w, 3+w households • Number of low inc, med inc, high inc households Apply Iterative Proportional Fitting (IPF) to fit the seed distributions to the marginal distributions
Building the Household Classification 100% Marginal distributions may be developed with a classification model, Or they can be prepared as a model input Percent of Households 90% 80% 70% 60% 50% 40% 30% 1-Person 2-Person 3-Person 4+-Person User-specified option 20% 10% 0% 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 Average Household Size
Page 1 and 2:
Transit Modeling Update presented t
Page 3 and 4: Transit Working Group Vidya Mysore,
Page 5 and 6: Project Overview On-Board Rider Sur
Page 7 and 8: Principles of Model Calibration and
Page 9 and 10: Case for the Project Workshop Pilot
Page 11 and 12: New PT Features in Cube Voyager 6.0
Page 13 and 14: Examples of Drive-Access Coding Aer
Page 15 and 16: Examples of Drive-Access Coding Net
Page 17 and 18: Drive-Access Generation in 5.x Hist
Page 19 and 20: Drive-Access Generation Examples in
Page 21 and 22: Drive-Access Generation Examples in
Page 23 and 24: Drive-Access Generation in 6.x Cube
Page 25 and 26: Enhanced Drive-Access Generation Ex
Page 27 and 28: Output Files by PT AUTOCON Process
Page 29 and 30: Enhanced Fares along with BestPathO
Page 31 and 32: Fare Process in 5.x Output example
Page 33 and 34: Enhanced Fare Process in 6.x Exampl
Page 35 and 36: Enhanced Fare Process in 6.x Exampl
Page 37 and 38: Enhanced Transit Line Keywords in 6
Page 43 and 44: Transit Modeling Update TRIP MARKET
Page 45 and 46: Proposed New Trip Purposes HB Colle
Page 47 and 48: Proposed New Trip Purposes NHB Work
Page 49 and 50: Key Travel Market Attributes Model
Page 51 and 52: Key Travel Market Attributes FTA Re
Page 53: Building the Household Classificati
Page 57 and 58: Household Workers Classification Mo
Page 59 and 60: Transit Modeling Update AUTO OWNERS
Page 61 and 62: Proposed Auto Ownership Model Multi
Page 63 and 64: Mixed Household / Employment Densit
Page 65 and 66: Sample Auto Ownership Coefficients
Page 67 and 68: Trip Production Model Home-Based Wo
Page 69 and 70: Transit Modeling Update TRIP ATTRAC
Page 71 and 72: Trip Attraction Model Linear regres
Page 73 and 74: Transit Modeling Update SPECIAL TRI
Page 75 and 76: Example 1: Portland Air Passenger T
Page 77 and 78: Transit Modeling Update TIME OF DAY
Page 79 and 80: FSUTMS Approach to Time of Day Effe
Page 81 and 82: Implications for other model compon
Page 83 and 84: FSUTMS Diurnal Factors Developed fr
Page 85 and 86: Travel Time Feedback: Why? Ensure c
Page 87 and 88: Transit Modeling Update TRIP DISTRI
Page 89 and 90: Destination Choice Advantages Sensi
Page 91 and 92: Destination Choice Model Multinomia
Page 93 and 94: Trip Market Stratification At least
Page 95 and 96: Attraction Balancing Whether to sha
Page 97 and 98: Destination Choice Estimation Requi
Page 99 and 100: Destination Choice Estimation Sampl
Page 101 and 102: Destination Choice Estimation Size
Page 103 and 104: Destination Choice The mode choice
Page 105 and 106:
Transit Modeling Update TRANSIT NET
Page 107 and 108:
Good transit coding practices … R
Page 109 and 110:
Good transit coding practices … R
Page 111 and 112:
Transit network coding Access Conne
Page 113 and 114:
Path building and skimming Path Bui
Page 115 and 116:
Transit Modeling Update MODE CHOICE
Page 117 and 118:
Trip market segmentation Household
Page 119 and 120:
Tier B/C Cities Mode Choice Nesting
Page 121 and 122:
FSUTMS Nesting Coefficients 0.8 0.5
Page 123 and 124:
Nested Logit Model: How? Choice Aut
Page 125 and 126:
Logit Model Curve 1 Auto Probabilit
Page 127 and 128:
Coordinating DC and MC Calibration
Page 129 and 130:
Trip Generation EE Trips Visitor &
Page 131 and 132:
Build Hwy Networks & Skims Build Pe
Page 133 and 134:
Trip Generation EE Trips HH Classif
Page 135 and 136:
Travel time feedback
Page 137 and 138:
Trip Distribution Destination choic
Page 139 and 140:
Model Inputs: What’s New? Zonal D
Page 141 and 142:
Model Parameters: What’s New? Pea
Page 143 and 144:
Catalog Keys: What’s New? Auto oc
Page 145 and 146:
Deliverables Technical Memoranda
Page 147:
Deliverables Workshops • Case for
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