journal of transportation and statistics - Research and Innovative ...

Volume 7 Numbers 2/3, 2004ISSN 1094-8848JOURNAL OF TRANSPORTATIONAND STATISTICSU.S. Department of TransportationResearch and Innovative Technology AdministrationBureau of Transportation Statistics

JOURNAL OF TRANSPORTATIONAND STATISTICSJOHN V. WELLS Editor-in-Chief [through October 2004]PEG YOUNG Editor-in-ChiefDAVID CHIEN Associate EditorCAESAR SINGH Associate EditorMARSHA FENN Managing EditorJENNIFER BRADY Data Review EditorVINCENT YAO Book Review EditorDORINDA EDMONDSON Desktop PublisherALPHA GLASS Editorial AssistantLORISA SMITH Desktop PublisherMARTHA COURTNEY EditorEDITORIAL BOARDKENNETH BUTTON George Mason UniversityTIMOTHY COBURN Abilene Christian UniversitySTEPHEN FIENBERG Carnegie Mellon UniversityGENEVIEVE GIULIANO University of Southern CaliforniaJOSE GOMEZ-IBANEZ Harvard UniversityDAVID GREENE Oak Ridge National LaboratoryMARK HANSEN University of California at BerkeleyKINGSLEY HAYNES George Mason UniversityDAVID HENSHER University of SydneyPATRICIA HU Oak Ridge National LaboratoryT.R. LAKSHMANAN Boston UniversityTIMOTHY LOMAX Texas Transportation InstitutePETER NIJKAMP Free UniversityKEITH ORD Georgetown UniversityALAN PISARSKI ConsultantROBERT RAESIDE Napier UniversityJEROME SACKS National Institute of Statistical SciencesTERRY SHELTON U.S. Department of TransportationKUMARES SINHA Purdue UniversityCLIFFORD SPIEGELMAN Texas A&M UniversityPETER STOPHER University of SydneyPIYUSHIMITA (VONU) THAKURIAH University of Illinois at ChicagoDARREN TIMOTHY U.S. Department of TransportationMARTIN WACHS University of California at BerkeleyC. MICHAEL WALTON The University of Texas at AustinSIMON WASHINGTON University of ArizonaJOHN V. WELLS U.S. Department of TransportationThe views presented in the articles in this journal are those of the authors andnot necessarily the views of the Bureau of Transportation Statistics. Allmaterial contained in this journal is in the public domain and may be used andreprinted without special permission; citation as to source is required.

A PEER-REVIEWED JOURNALJOURNAL OF TRANSPORTATIONAND STATISTICSVolume 7 Numbers 2/3, 2004ISSN 1094-8848U.S. Department of TransportationResearch and Innovative Technology AdministrationBureau of Transportation Statistics

U.S. Department ofTransportationNORMAN Y. MINETASecretaryResearch and InnovativeTechnology AdministrationERIC C. PETERSONDeputy AdministratorBureau of TransportationStatisticsRICK KOWALEWSKIDeputy DirectorWILLIAM J. CHANGAssociate Director forInformation SystemsMARY J. HUTZLERAssociate Director forStatistical ProgramsThe Journal of Transportation and Statistics releasesthree numbered issues a year and is published by theBureau of Transportation StatisticsResearch and Innovative Technology AdministrationU.S. Department of TransportationRoom 7412400 7th Street SWWashington, DC 20590USAjournal@bts.govSubscription informationTo receive a complimentary subscription:mail Product OrdersBureau of Transportation StatisticsU.S. Department of TransportationRoom 4117400 7th Street SWWashington, DC 20590USAphone 202.366.DATAfax 202.366.3640internet www.bts.dot.govInformation Serviceemail answers@bts.govphone 800.853.1351Bureau of Transportation StatisticsOur mission: To lead in developing transportationdata and information of high quality and toadvance their effective use in both public andprivate transportation decisionmaking.Cover and text designCover photoSusan JZ HoffmeyerPhoto by Elliott LinderClose-up of Olivier Huc’s motorcycleThe Secretary of Transportation has determined that thepublication of this periodical is necessary in the transaction ofthe public business required by law of this Department.ii

JOURNAL OF TRANSPORTATION AND STATISTICSVolume 7 Numbers 2/32004ContentsLetter from the Editor-in-Chief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vPapers in This IssueUnregistration Rates for On-Road Vehicles in CaliforniaTheodore Younglove, Carrie Malcolm, Thomas D. Durbin, Matthew R. Smith,Alberto Ayala, and Sandee Kidd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1A Bayesian Network Model of Two-Car AccidentsMarjan Simoncic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Development of Prediction Models for Motorcycle Crashes at Signalized Intersectionson Urban Roads in MalaysiaS. Harnen, R.S. Radin Umar, S.V. Wong, and W.I. Wan Hashim. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Effects of Extreme Values on Price Indexes: The Case of the Air Travel Price IndexJanice Lent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Estimating Link Travel Time Correlation: An Application of Bayesian Smoothing SplinesByron J. Gajewski and Laurence R. Rilett . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Speeds on Rural Interstate Highways Relative to Posting the 40 mph Minimum Speed LimitVictor Muchuruza and Renatus N. Mussa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Book Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Data ReviewAmerica’s Freight Transportation Gateways: Connecting Our Nation to Places and Markets AbroadReviewed by Jennifer Brady . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Calls for Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Guidelines for Manuscript Submission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Reviewers for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Index for Volume 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107iii

Letter from the Editor-in-ChiefDear JTS Readers,This issue completes our seventh volume of JTS and seventh year of publication.As the new JTS Editor-in-Chief, I’d like to bring your attention to severalchanges in the journal that have taken place over the last few months. You cansee on the front cover of this issue that the Bureau of Transportation Statistics(BTS) is now an office of the new Research and Innovative TechnologyAdministration (RITA) within the Department of Transportation (DOT).RITA’s staff come several groups within DOT: the Research and Special ProgramsAdministration’s Office of Innovation, Research, and Education,including the Volpe National Transportation Systems Center in Cambridge,Massachusetts, and the Transportation Safety Institute in Oklahoma City; theSecretary’s Office of Intermodalism; and, of course, BTS. I do believe that JTSwill have the opportunity to flourish within this new research environment.In line with my vision for the journal, our editorial board endorsed a broaderscope for JTS. We are just starting to include more applied papers. In thefuture, some of these papers will be from authors within RITA, keeping you,the readers, informed regarding DOT data and research directions. In thisway, we hope to provide more information to researchers and planners, toapplied and theoretical statisticians and economists, and to readers involvedin numerous aspects of transportation analysis. As always, all papers we publishwill undergo a thorough peer review.You should note two new sections that make their appearance in this issue ofthe journal—Data Reviews and Book Reviews. Our Data Review editor,Jennifer Brady, will present synopses on new data releases from BTS. In thisissue, she gives us an interesting discussion of BTS’s America’s Freight TransportationGateways and the Gateways Resource CD, which contain detailedanalysis and data on merchandise trade into and out of the United States.Our second new section is Book Reviews. Vincent Yao, from the Institute forEconomic Advancement at the University of Arkansas at Little Rock, joins theJTS staff as this section’s editor and coordinator. Vincent’s contact informationappears at the beginning of the new section, so get in touch with him ifyou would like to author a book review in the future. We thank our editorialboard for suggesting these additions—I hope you find them of value in yourresearch.Finally, this issue includes two new calls for papers. The first is a general callfor papers reflecting our broader scope. The second is for a special JTS issueon transportation investment, which is scheduled for publication in 2007.Vincent Yao, along with Cletus C. Coughlin (Vice President and Deputyv

Director of Research, Federal Reserve Bank of St. Louis), and Randall W.Eberts (Executive Director of the W.E. Upjohn Institute for EmploymentResearch), will be guest co-editors. If you’re interested in submitting a paper,check out the details on page 99.BTS, as part of RITA, continues to pursue its mission of making the best possibletransportation data available to improve the quality of transportationdecisionmaking. JTS authors play a key role in this process by providing highqualityquantitative analysis as it applies to transportation issues. I hope thearticles in the journal, along with the new sections that appear in this issue,will provide valuable information that you can use in your work.PEG YOUNG, Ph.D.Editor-in-Chiefvi

Unregistration Rates for On-Road Vehicles in CaliforniaTHEODORE YOUNGLOVE 1CARRIE MALCOLM 1THOMAS D. DURBIN 2*MATTHEW R. SMITH 3ALBERTO AYALA 4SANDEE KIDD 51 Statistical Consulting Collaboratory,University of California, Riverside, CA 925212 Bourns College of Engineering, Center forEnvironmental Research and Technology (CE-CERT),University of California, Riverside, CA 925213 P.O. Box 38Genesee Depot, WI 531274 Research Division, California Air Resources Board,1001 I Street, Sacramento, CA 958125 Planning and Technical Support Division,California Air Resources Board,9500 Telstar Ave., El Monte, CA 91731Email addresses:* Corresponding author T.D. Durbin—durbin@cert.ucr.eduT. Younglove—tyoung@ucr.eduC. Malcolm—ctmalcolm@sbcglobal.netM. Smith—superengineer@netscape.comA. Ayala—aayala@arb.ca.govS. Kidd—skidd@arb.ca.govABSTRACTMotivated by the need to develop regional air pollutioncontrol strategies, a comprehensive field studywas conducted throughout California to characterizethe unregistration rate of light-duty vehicles inthe state. Based on an analysis of more than 98,000vehicle records, the average unregistered rate wasfound to be 3.38 ± 0.13%. This included vehiclesunregistered for a period of less than three months(2.41% of the total), vehicles unregistered betweenthree months and two years (0.95% of the total),and vehicles unregistered for more than two years(0.03% of the total). About half of the counties hadunregistration rates between 2% and 4%, withmost counties’ rates below 5%. The unregisteredfleet was more heavily weighted toward older vehiclesthan the registered fleet. Department of MotorVehicles (DMV) unregistration rates were comparedwith the field study rates. DMV estimates rangedfrom 6.2% to 7.5%, which were higher than thoseobtained in the field study. It was also found that1.7% of the vehicles identified in the survey wereregistered outside the state or the country.KEYWORDS: Motor vehicle registration, motor vehicleemissions.1

INTRODUCTION1 EMFAC is short for Emission FACtor.2 MOBILE = Mobile Source Emission Factor Model.Development of regional air pollution control strategiesrequires accurate estimation of the regionalemissions inventory. Understanding and accuratelyportraying the in-use vehicle population is one ofthe most important aspects of obtaining accurateemissions inventory estimates. The registered vehiclepopulation accounts for a majority of the vehicleson the road, however, unregistered and out-of-statevehicles represent an important proportion of thetotal inventory as well. Given that the unregisteredvehicle population likely includes a higher percentageof older vehicles with emissions too high to meetInspection and Maintenance (I&M) requirements,these vehicles may have a disproportionate effect onthe emissions inventory.To date, limited information is available on thecontribution of unregistered vehicles to the emissionsinventory. The California Air ResourcesBoard’s (CARB’s) EMFAC 1 model provides estimatesof current emissions for on-road motor vehiclesin the state, based primarily on the populationof vehicles registered with the Department of MotorVehicles (DMV). Unregistered vehicle estimates area recent addition to the EMFAC vehicle population.In making these estimates, CARB examined DMVrecords and found that approximately 7.4% of thetotal records for passenger cars were unregistered(CARB 2000). For the EMFAC2000 model, CARBadded approximately 4.5 million vehicles to the inuseCalifornia vehicle population to account forvehicles in the process of being registered and thosethat were unregistered. CARB also estimated that0.56% of the vehicle population that was chronicallyunregistered (unregistered for a period of morethan 2 years) contribute 1% to the emissions inventory,depending on the pollutant and year beingmodeled (CARB 2000). CARB did not determinethe total contribution from the unregistered vehiclepopulation as a whole, however.For modeling emissions inventories, states outsideof California use the U.S. Environmental ProtectionAgency’s MOBILE 2 model. MOBILE doesnot explicitly include the contribution of unregisteredvehicles in its emissions rate estimates,although provisions are made for states to incorporateunregistered vehicles in their calculations.Most previous studies of the population ofunregistered vehicles have focused on California.Hunstad (1999) conducted a study to characterizeuninsured motorists and provide estimates of thenumber of uninsured and unregistered vehicles.Hunstad examined DMV records and other estimatesof unregistration, including studies by theCalifornia Energy Commission, estimates based onCalifornia Highway Patrol violations, DMV driver’slicense records, estimates based on surveys, andfatal accident reports. Using these collective studies,Hunstad came up with average yearly estimates ofbetween 8.5% and 11.7% for unregistered vehicles.Dulla et al. (1992) examined license plate number(LPN) records of vehicles in parking lots in thelate 1980s and found total unregistration ratesranging from 8.3% to 9.3% with chronically unregisteredvehicles (i.e., greater than 2 years) representingabout 0.56% of the in-use fleet. Although theimpact of unregistered vehicles on emissions inventoriesin other states is important, limited informationexists on unregistration rates outside ofCalifornia. North Carolina is one state that hasdeveloped a database from accident reports to provideestimates of the vehicle-miles traveled in urbanareas by vehicles registered outside of the area(Norowzi 2003).Data that are available on the population ofunregistered vehicles still have considerable limitations.DMV database sources do not, for example,provide a good indication of whether the vehiclestravel on the road. The DMV files can contain vehiclesthat may have become inoperative or may belocated outside of the county of record. Becausethese vehicles are not part of the in-use fleet thatwould be operated in the designated area, theirinclusion would result in an overestimate of theactual on-road fleet’s emissions.The most recent on-road unregistered vehiclepopulation study was conducted over a decade agoin California (Dulla et al. 1992). Since that time,California added a requirement that vehicle ownersshow proof of insurance before a vehicle’s registrationcan be issued or renewed. Furthermore, I&M2 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

testing procedures have been increased from an idleto a dynamometer test at 15 mph and 25 mph inareas that do not meet air quality standards. Thepossibility exists that these two requirements maycontribute to the increased number of unregisteredvehicles in the state, especially poorly maintainedvehicles that cannot pass a smog test.Given the potentially significant emissions inventoryimpact and the limitations of the current unregisteredvehicle population estimates, improving theunderstanding of both the number and types ofunregistered vehicles on the road is important. Theobjective of this work was to obtain a better understandingof the population and characteristics ofunregistered vehicles. As the primary component ofthis study, a statewide field survey was conducted toprovide an estimate of the unregistered vehicle population.As part of the survey, a database of morethan 98,000 vehicle LPNs was obtained. This surveyrepresents the most comprehensive study ofvehicle unregistration rates to date and encompassesall regions of California. In addition to the totalunregistration rate, the following information wascollected: a breakdown of the time period of unregisteredstatus into instantaneous (less than 3 months),long term (3 months to 2 years) and chronic(more than 2 years) categories by county forCalifornia; characteristics of unregistered vehicles including,but not limited to, model year; and the percentage and identity in each county ofnon-California vehicles or vehicles that originatedout of county.Durbin et al. (2002) presents the detailed results ofthis survey.METHODOLOGYA comprehensive field survey was conducted todetermine the population of unregistered vehicles inCalifornia. The survey involved photographinglicense plates of vehicles that were parked in commercialparking lots to obtain registration and otherinformation. Data were collected between June andDecember 2000.A county-based stratified random sample of allCalifornia counties was conducted, with the samplingpopulation and number of sites in each of thelarger counties proportional to the county population.To ensure that the sample was demographicallyrepresentative, each county was resolved to thezip code level, with zip codes selected randomlyfrom the list of all zip codes in each county. Withinthe selected zip codes, as many commercial parkinglots were surveyed as possible. Where security, sampleteam safety, or geographic size prevented completesampling, locations were geographicallybalanced across the zip code. To ensure a reasonabledistribution of destination types, a minimum numberof sites in each county was sampled. In total,409 zip codes were sampled during the field study,out of a total of 1,586 zip codes in California at thetime of study. Because the zip codes includednational parks and forests, sampling in those areaswas performed as appropriate for a given county.The sites for this study were restricted to destinationsrather than residences. This provided a highprobability that the vehicles captured in the surveywere driven on a regular basis. Sites were alsoselected to represent a variety of different destinations.The primary collection sites were shoppingmalls, businesses, and retail stores, although therange of sites also included park and ride lots, medicalfacilities, and others. Overall, the different sitetypes did not show significant variation in unregistrationrates. In particular, all but one of the sitetypes where at least 1,000 samples were collectedhad unregistration rates within 1% of the overallaverage (Durbin et al. 2002). It should be noted thatthis site selection and sampling methodology mightnot be fully representative of all vehicles distributedthroughout the state. Families with multiple vehicles,for example, may use a specific vehicle forsome applications such as shopping, while othervehicles may be used for longer trips or other tasks.Consideration was given to sampling vehiclesduring actual driving on the road (i.e., with a licenseplate recorder) to obtain a broader cross section ofvehicles. Because the primary purpose of this studywas for support of development of the CARBEMFAC model, this approach was not chosen. Inparticular, it was thought that the fleet obtainedYOUNGLOVE, MALCOLM, DURBIN, SMITH, AYALA & KIDD 3

from sampling on the road would be more heavilyweighted by vehicle-miles traveled and would beless consistent with the in-use population data presentlyused in CARB’s EMFAC from the DMV.To collect the data at each specific site, one to twophotographers, using digital cameras with a reloadtime of less than a second between shots, took pictureswhile riding in a car as it slowly movedthrough a parking lot. Data from the photographicrecords were entered and compiled into a spreadsheetalong with other information, such as the dateand time of the site visit and a description of the siteand its location (city, county, and zip code). Thevehicle make and model were determined forroughly half of the vehicles photographed. The datawere validated by double entry and cross-checking a5% subsample as well as random spot checks ofindividual vehicles. The error rate for data entry ofLPNs was consistently below 1%.Given the nature of the rapid data collection inthe field and the need to get large numbers ofrecords, a percentage of the LPN photographs collectedin the field were unreadable. The overallunreadable LPN rate averaged about 15% with arange from 1% or 2% to over 40% in some zipcodes. Sites surveyed during rain events or near sundownmade up the highest percentages of unreadableLPNs. Field teams generally attempted tocollect 10% to 20% more photographs for each zipcode to compensate for the expected unreadablepercentage.RESULTSCounty Unregistration RatesThe field survey brought in more than 98,000records. Table 1 presents data analysis for all countieswith more than 1,000 samples and for the entirefield survey sample population. (See Durbin et al.(2002) for detailed records for all counties).Vehicles were considered to be unregistered if theyear sticker was 1999 or older, regardless of themonth, and registered if the year sticker was 2001.For vehicles with year 2000 stickers, the month ofregistration was evaluated against the time periodwhen the vehicle was identified to determine theregistration status. The percentage unregistered wascalculated by dividing the number of unregisteredFIGURE 1 Percentage of Unregistered Vehicles inLarge and All CountiesNumber of counties181614121086420All countiesLarge counties(>300,000 population)0–1 1–2 2–3 3–4 4–5 >5% unregistered vehiclesvehicles by the sum of registered vehicles, unregisteredvehicles, and dealer plates (registration is paidat the time of vehicle purchase, so vehicles withdealer plates were considered registered).The overall average unregistration rate for allsurveyed vehicles was 3.38 ± 0.13%, where theuncertainty represents the 95% confidence intervalbased on the sampling statistics (Vollset 1993). Theunregistration rate ranged from 0% to 6.45% fordifferent counties. Figure 1 presents the data for allthe surveyed counties and for the most populouscounties (population > 300,000). These data showthat roughly 50% of the counties have unregistrationrates ranging between 2% and 4%. Nearly allcounties had unregistration rates below 5%. In general,larger counties had slightly higher unregistrationrates than the overall distribution, withunregistration rates in larger counties generallyranging from 2% to 5%.Counties with unregistration rates of less than1% tended to be smaller, with sample sizes of fewerthan 500 vehicles. In some small counties, the fielddata contained no unregistered vehicles. AlpineCounty has the highest rate of unregistered vehiclesat 6.45%, but this figure may be due in part to thesmall sample size for that county. Data show thatCalaveras, San Diego, and Madera counties havethe next-highest unregistration rates of 5.22%,4.99%, and 4.51%, respectively.A one-way ANOVA test showed highly statisticallysignificant differences in registration ratesbetween different counties in the study (p < 0.0001).4 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 1 Registration Rates by CountyLongterm Chronic Dealer FrontCounty Total Registered Unregistered InstantaneousLos Angeles 25,835 20,153 749 535 211 3 317 2,146 15 214 3 2,238 3.53San Diego 9,584 7,226 385 300 83 2 110 708 3 230 22 900 4.99Orange 9,468 7,421 251 171 76 4 169 665 18 138 4 802 3.20San Bernardino 4,417 3,115 117 72 44 1 89 314 11 148 0 623 3.52Riverside 4,262 3,026 144 83 60 1 94 238 6 123 0 631 4.41Santa Clara 4,109 3,084 91 55 33 3 74 271 6 43 0 540 2.80Alameda 3,529 2,688 72 54 18 0 52 238 1 38 0 440 2.56Sacramento 3,337 2,573 90 50 37 3 53 233 12 37 0 339 3.31San Francisco 2,840 2,154 100 74 25 1 33 226 3 85 0 239 4.37San Mateo 2,705 2,112 75 54 19 2 35 144 8 60 0 271 3.38Contra Costa 2,597 1,929 42 30 12 0 44 131 3 21 0 427 2.08Ventura 2,073 1,574 61 48 13 0 43 120 1 22 0 252 3.64Fresno 2,059 1,652 67 53 14 0 33 146 0 2 0 159 3.82Santa Barbara 1,464 1,144 37 27 10 0 20 74 0 32 0 157 3.08Sonoma 1,404 1,100 28 17 11 0 7 54 1 7 0 207 2.47Kern 1,401 1,042 46 37 9 0 16 130 1 4 0 162 4.17Stanislaus 1,329 1,076 40 29 11 0 2 94 1 8 0 108 3.58All counties 98,817 75,168 2,677 1,906 749 22 1,302 7,091 108 1,599 111 10,761 3.38NoplateOut ofstateOut ofcountry Unknown%unregKey: County = the county where the data was collected, not necessarily the county where the vehicle was registered.Registered = vehicle had a current California registration.Unregistered = vehicle had an expired California registration.Front = vehicle’s LPN was captured from the front of the vehicle. Because California vehicles only have registration tags for rear license plates, no registration information is available from the front plate.Dealer = vehicle’s LPN was a paper plate or a dealership plate of a newly purchased vehicle being used until the issued license plate is received.Unknown = vehicles for which either the photographic quality prevented identification of the month if the vehicle had a registration year of 2000 or the year sticker was missing.YOUNGLOVE, MALCOLM, DURBIN, SMITH, AYALA & KIDD 5

The individual zip code data contained a widerrange of unregistration rates than the overall countydata. Kern, San Diego, Santa Cruz, and San Benitocounties each had one zip code with an unregistrationrate above 20%, although most of these zipcodes had limited sample sizes. Only the San Diegozip code contained a significant sample size (507).In analyzing the data, correlations between theunregistered vehicle population percentage anddemographic variables such as household incomewere examined. Using data obtained from the2000 census, regression analyses revealed theinfluence of household income, total county population,and renter/owner percentage on unregistrationrates at both the county and zip codelevel, using the unregistration rate as a dependentvariable. Zip codes with fewer than 25 vehicleswere removed from this analysis, because thesmall number of samples makes estimation of therate of registration highly variable. A statisticallysignificant regression (p = 0.004, R 2 = 0.250) forhousehold income was found at the county level.This regression was not statistically significant onthe zip code level, however, and the low R 2 valueof regression at the county level indicates that therelationship does not explain the majority of thevariability observed in the unregistration rate.Statistically significant regressions of at least the90% confidence level were found between unregistrationrates and population at the county level(p = 0.096, R 2 = 0.049) and the registration zipcode level (p = 0.007, R 2 = 0.009) but not the zipcode where the vehicle was observed (p = 0.658).Again, the low R 2 values indicate that none ofthese relationships can account for most of theobserved variability in unregistration status,although such research may be of interest in thefuture.The overall 3.38% unregistered rate was brokendown by the length of time a vehicle wasunregistered. A total of 2.41% of the Californialicensed vehicles were classified as instantaneous(2 years) unregistered accounted for 0.03% of theCalifornia licensed vehicles.A subset of vehicles operated in California wereregistered in other states or countries. As shown intable 1, this represents approximately 1.7% of thevehicles in this survey. In general, higher percentagesof out-of-state vehicles were found in the bordercounties and in counties having zip codes with wellknowntourist attractions. Border counties such asDel Norte, Sierra, Nevada, Alpine, Inyo, Imperial,and Kings County in the Central Valley all had relativelyhigh proportions (>10%) of out-of-state vehicles.(Durbin et al. (2002) provide a map of out-ofstatevehicle percentages by county.)Comparisons with DMV DataThe data obtained from the field study were crossreferencedwith two different DMV databases todetermine the DMV registration status of the vehiclesidentified in the field survey. Because the fieldsurvey included only vehicles used typically on theroad, this should represent a more accurate estimateof the unregistered population than a straight DMVrun that could include a mixture of vehicles usedinfrequently or not at all. The DMV databases usedfor this comparison correspond to late 1998 and2001.Table 2 provides a comparison of the DMVresults and the field survey registration status for theApril 2001 DMV database. The results for the 1998DMV database were similar and are presented elsewhere(Durbin et al. 2002). Overall, the DMV databaseshows a similar profile of unregistered vehicles,with roughly half as many long-term unregisteredvehicles in comparison with instantaneous unregisteredvehicles and smaller numbers of chronicallyunregistered vehicles. Differences were observed,however, for the various registration categoriesbetween the DMV database and the field study. Forexample, a number of vehicles identified as registeredin the field were unregistered in the DMVdatabase and vice versa.Several possible explanations exist for the vehiclesobserved to be registered in the field survey, butsubsequently found to be unregistered. First, differencesbetween the time of the field survey and thetime of the DMV run may account for the discrepancy.In particular, vehicles registered at the time ofthe field study may have subsequently fallen out of6 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 2 Cross-Tabulation of Registration Status with the 2001 DMV DatabaseField study status TotalDMVregisteredDMVunregisteredDMVinstantaneousDMV longtermDMVchronicRegistered 70,418 65,425 4,993 3,630 1,156 207Unregistered 2,459 2,124 335 107 201 27Instantaneous 1,771 1,636 135 20 106 9Long term 670 480 190 86 93 11Chronic 18 8 10 1 2 7Front 6,627 6,092 535 307 193 35Unknown 9,153 8,397 756 136 573 47Total 88,657 82,038 6,619 4,180 2,123 316registration. The fact that a majority of the vehiclesin this category were found to be instantaneouslyunregistered supports this conclusion. A second possibilityis that some vehicles identified as registeredin the field had stolen tags or switched LPNs,although this probably represents a smaller fractionof the vehicles.Interestingly, many of the vehicles found to belong-term or chronically unregistered in the fieldstudy were also found to be registered. For vehiclesfound to be unregistered in the field but registeredwith the DMV, it is probable that the vehicle wasregistered subsequent to the field study. This wouldaccount for the large percentage of instantaneouslyunregistered vehicles in the field study that werefound to be subsequently registered with the DMV.Given the small number of vehicles in this category,it is possible that the owners of these vehicles simplydid not adhere their registration stickers. Anothersubset of vehicles were long-term unregistered in thefield study but instantaneously unregistered in theDMV. This subset of vehicles could be attributed toa combination of factors including an unattached orstolen sticker in conjunction with a subsequent lateregistration.Differences also exist in the total unregistrationrate between the two DMV databases and the fieldstudy results. For the 2001 DMV database run, forexample, the unregistration rate was approximately7.5% compared with the 3.38% obtained from thefield results. The 1998 DMV database shows ahigher estimated unregistration rate of 6.2%. Itshould be noted that since the data entry error ratefor the field study records was typically below 1%,we anticipate that this accounts for only a smallportion of the observed discrepancy.Unregistered Vehicle Model YearDistributionsThe observed LPNs from the field study were alsocross-referenced with DMV records to obtain themodel year for all vehicles having readable LPNs.Figure 2 presents a model year distribution for allobserved vehicles. The model year distribution isheavily weighted to newer vehicles, as expected.Figure 3 shows the model year for vehicles unregisteredfor more than 3 months (i.e., long-term andchronic unregistered vehicles), using data from thefield study. Figure 4 shows the percentage unregisteredfor each model year category. For model year2000 vehicles, 1 out of 10 had a long-term unregisteredstatus, causing the percentage for that modelyear to be high. When compared with figure 2, theunregistered vehicle population is more heavilyweighted to the older model years.Comparison with Inspection andMaintenance DataTo better understand the relationship betweenunregistration rates and the requirements that vehiclespass an I&M test for registration, field studyrecords were cross-referenced with I&M records.To do this, the LPNs for all registered and unregisteredvehicles (over 90,000) were sent to the Bureauof Automotive Repair (BAR). BAR provided I&Mtest results from mid-1996 to the present for allvehicles with a license plate match. The observedplates were matched with data from both theBAR90 and BAR97 programs.YOUNGLOVE, MALCOLM, DURBIN, SMITH, AYALA & KIDD 7

FIGURE 2 Model Year of all Observed Vehicles7,000 Number of vehicles Model year6,0005,0004,0003,0002,0001,00001972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000FIGURE 3 Model Year of all Unregistered Vehicles for Less Than 3 Months70 Number of vehicles Model year60504030201001972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000Note: An older version of the DMV database was used to determine the model year, hence, information on1999 and 2000 model-year vehicles was limited.The BAR90 and BAR97 programs represent twolevels of I&M programs in California. The BAR90program, an older emissions test, uses a two-speedidle test. The BAR90 program is used only in areasof California that do not require enhanced I&M.The BAR97 program, in use since 1998 in enhancedI&M areas of California, employs an acceleratedsimulation mode dynamometer test at 15 mph and25 mph.BAR rates data as “good” or “bad.” Bad filescan indicate unknown or aborted tests or recordswith incorrect entries such as the vehicle identificationnumber (VIN). Because the data were matchedbased on LPNs instead of VINs, these bad recordswere initially included and the aborted andunknown tests were subsequently separated out.The cross-tabulation of the I&M data and theobserved data yielded 66,436 matching records.8 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

FIGURE 4 Percentage of Total Vehicles, by Model Year that are Unregistered forMore Than 3 Months12 % of vehicles Model year10864201972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000Bad data accounted for only 1,400 records out ofthe 66,436 BAR matches.Table 3 presents a comparison of registration statusand I&M test results. The I&M test results arebroken into six categories: passed, failed, gross polluter,tampered, test aborted, and unknown (whereno results were recorded). The results show themajority of the vehicles in all categories passed theirlast I&M. A large number of unknown and abortedI&M results come from the BAR-classified baddata. Overall, the percentage of vehicles passing thelast I&M drops with the length of time since thevehicle was last registered. A chi-square test of independencebetween smog check results and unregistrationstatus showed that the differences in theoverall failure rate for the registered vehicles andinstantaneous and long-term unregistered vehicleswere statistically significant at the 0.0001 level.Chronically unregistered vehicles were not includedin this chi-square test due to the small number ofsamples. The test indicates that smog check resultsare not independent of observed registration status,with a higher proportion of unregistered vehiclesfailing the smog check test. Interestingly, three vehiclesthat were chronically unregistered whenobserved by the survey team in 2000 had sincetaken and passed an I&M test in 2001. One vehicleidentified as unregistered in the field study wastested and found to be a gross polluter in 2000,indicating that the lack of registration for the vehiclemay have been related to failing the I&M.The I&M failures rates for the matched vehiclesfrom the survey are less than the average failure ratefor all vehicles taking the test in the state, which isslightly above 15% (CARB/BAR 2004). Because thefleet surveyed represents vehicles driven on a moreregular basis, it is probable that the survey vehiclesare newer than recorded during I&M. Furthermore,vehicles that are model years 1991 or newer havefailure rates below the statewide average, whilevehicles from the early 1980s have more than doublethe statewide average for failed inspections(CARB/BAR 2004). Vehicles that initially fail theI&M test can also pass a retest after repair to obtainregistration.DISCUSSION AND CONCLUSIONSOverall, the results of this study showed a generallyconsistent average statewide unregistration rate ofbetween 3.4% to 7.5%, with an additional 1.7% ofthe vehicles registered out of state. The DMV andfield studies also yielded similar distributions oflargely instantaneous unregistered vehicles, withsmaller numbers of chronic and long-term unregisteredvehicles. Although differences were observedin the specific unregistered populations, both showYOUNGLOVE, MALCOLM, DURBIN, SMITH, AYALA & KIDD 9

TABLE 3 Cross-Tabulation of BAR Smog Check Test Results with Registration StatusFieldPassed FailedGrosspolluter Tampered Aborted Unknownstatus Number # % # % # % # % # % # %Registered 51,974 50,423 97.0 682 1.3 250 0.48 25 0.1 391 0.8 203 0.4Instantaneous 1,449 1,385 95.6 30 2.1 13 0.90 2 0.1 12 0.8 7 0.5Long term 601 549 91.4 18 3.0 19 3.16 2 0.3 10 1.7 3 0.5Chronic 13 11 84.6 1 7.7 1 7.69 0 0.0 0 0.0 0 0.0Front 4,921 4,716 95.8 91 1.9 44 0.89 4 0.1 44 0.9 22 0.5Unknown 7,387 7,015 95.0 157 2.1 66 0.89 13 0.2 83 1.1 53 0.7Total 66,346 64,100 979 393 46 540 288Key: BAR = Bureau of Automotive Repair.trends that, on a continuous basis, approximately3% to 8% percent of the on-road vehicle populationis unregistered and needs to be accounted for inemissions inventory models.The results of this study can be compared withthose from a limited number of other estimates.BAR has conducted some analyses of remote sensingdevice (RSD) data collected throughout the stateas part of various studies over the years (Amlin2002). From this data, BAR found that vehiclesunregistered for a period of more than one yearcomprised approximately 0.6% to 0.7% of the onroadfleet. Because the RSD data would tend to bemore heavily weighted toward vehicle-miles traveled(VMT), the BAR estimate includes an adjustmentto compensate for older unregistered vehiclesthat would be driven fewer miles. The BAR studyalso found that the VMT for unregistered vehicles issignificantly lower than the VMT for registeredvehicles.For comparison with these results, the percentageof vehicles unregistered for more than one year wasdetermined using the present data. Of the vehicles inthe field study, 0.15% were unregistered for aperiod of more than one year, which was less thanthe BAR estimates. BAR also examined the DMVdatabase and found that between 5.1% and 5.5%of the DMV population had delinquent registrationbut renewed at some point in time.Dulla et al. (1992) conducted an earlier parkinglot survey of unregistered vehicles in the SouthCoast Air Basin of Southern California during1989. In their field study, these researchers foundthat approximately 9.3% of vehicles had expiredlicense plates, with fewer than 2% of the vehiclesunregistered for more than one year. These valuesare higher than the 3.38% overall unregistrationwith less than 1% unregistered for more than oneyear found in the present study.In looking more specifically at the counties sampledin the Dulla et al. study, the unregistered rates inLos Angeles, Orange, Riverside, and San BernardinoCounties in this field study were 3.53%, 3.20%,4.41%, and 3.52%, respectively; also lower thanthose found by Dulla et al. It is worth noting thatthe sample population in the Dulla et al. study wastargeted more toward older vehicles, because thevehicles randomly sampled in parking lots are likelyto be newer than the universe of vehicles found inthe DMV database, and older vehicles are typicallydriven less. In fact, the sample population includeda larger portion of older vehicles than the DMVpopulation. Since the DMV data include a largerpercentage of older vehicles than the on-road population,it is possible this estimate is biased high relativeto the on-road population. In this regard, forvehicles less than 10 years of age, Dulla et al. (1992)found the unregistered population was 6.9%.Hunstad (1999) also provided some estimates ofunregistered vehicles based on available databasesources including California Energy Commissiondata, California Highway Patrol (CHP) citations,driver’s license records, surveys, and fatal accidentreports. Estimates based on vehicle-not-at-fault infatal accidents, CHP violations, and DMV recordsfor vehicles unregistered less than one year were allin the range of 8% to 12% on an annual basis.Analysis of driver’s license citations, on the other10 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

hand, indicated a 3% to 4% unregistration rate.Citations appearing on driver’s license records differsomewhat from other citation sources in that onlyconvictions appear on an individual’s driver’s licenserecord, not citations that are ruled unjustified by thecourt. In each of the categories examined by Hunstad,a number of factors could bias this estimateupward or downward. In general, these data shouldprovide a rough estimate of the unregistered percentage.Not-at-fault fatal accident victims data areprobably one of the less biased data sources, butinclude a large fraction of unknown license platesthat had to be accounted for in the estimated unregisteredpercentage. This unknown category is forvehicles for which there is no identifying information,perhaps because the vehicle left the scene, orfor which no DMV records could be found.Surveys of unregistered ownership were also analyzedwith unregistration rates found to be between7% and 16%. For these surveys, it is important tonote that estimates based on ownership of unregisteredvehicles would be biased high relative to onroadpopulation estimates if the vehicles are notdriven on a regular basis. Data collected in surveystend to support this hypothesis.The results of the present study indicate that thepopulation of unregistered vehicles was biasedtoward older vehicles relative to the total on-roadfleet. To this extent, it is estimated that unregisteredvehicles would make a disproportionate contributionto the emissions inventory on a populationbasis. Smog check records also indicate a higher percentageof failures for unregistered vehicles. Themajority of the unregistered vehicles in all categorieswere found to pass their last smog check, however,indicating this is probably not the most significantcontribution to the unregistered vehicle population.Given the differences in unregistration ratesfound for various methodologies, further researchshould be conducted in this area. In particular, ourresults indicate there could be a range of as much as3% to 8% for estimates of unregistered vehicles,with a wider range of estimates when other studiesare also considered. With the higher emissions ratesthat can be assumed for the older unregistered vehiclefleet, this could represent a considerable uncertaintyin emissions inventory estimates.Within this study, more detailed analysis andcomparison between the field study results and theDMV results could provide some important insightsinto this issue. In particular, for vehicles whose registrationstatus does not match between the fieldstudy and the DMV results, the individual LPNrecords could be analyzed to better understandthese differences. The analysis of the individualvehicle records would allow a better determinationof whether the differences can be attributed to variationsin the time period of the population snapshots, stolen stickers or license plates, errors in thedatabase, or other factors.ACKNOWLEDGMENTSThe authors wish to thank the following individualsfor their valuable contributions to this project: Data collection—Hugo Galdamez, John Gaskins,Shayna Golbaf, Beth Lee, Don Pacocha, AlisonRowlen, Judy Swineford, and Sylvester Wheeler. Data entry—Jennifer Anderson, Warren Katzenstein,Lindsey Miller, Shelly Miller, Andrea Ruiz,Jessica Walters, and Ryan Wicks. Department of Consumer Affairs, Bureau ofAutomotive Repair, smog check data—DavidAmlin, Mark Duewel, Kathy Runkle, and GregSweet.The authors also wish to thank the California AirResources Board for their financial support undercontract number 99-318 and the South Coast AirQuality Management District for its financialsupport.REFERENCESAmlin, D. 2002. California Bureau of Automotive Repair, personalcommunication.California Air Resources Board (CARB). 2000. Technical SupportDocument for a Public Meeting to Consider Approvalof Revisions to the State’s On-Road Motor Vehicle EmissionsInventory. May.California Air Resources Board and Bureau of AutomotiveRepair. 2004. Evaluation of California’s Smog Check Program,presentation to the California Inspection and MaintenanceReview Committee, January. Available at http://www.imreview.ca.gov/presentations/index.html.Dulla, R.D., Y. Horie, and S. Sidawi. 1992. Unregistered VehicleStudy—Phase 1, report prepared for the California AirYOUNGLOVE, MALCOLM, DURBIN, SMITH, AYALA & KIDD 11

Resources Board (Contract No. A866-163) by SierraResearch Inc., Sacramento, CA.Durbin, T.D., T. Younglove, C. Malcolm, and M.R. Smith.2002. Determination of Non-Registration Rates for On-Road Vehicles in California: Final Report, prepared for theCalifornia Air Resources Board (Contract No. 99-318) bythe University of California, Riverside, Bourns College ofEngineering, Center for Environmental Research and Technology.March.Hunstad, L. 1999. Estimating Uninsured Vehicle and UnregisteredVehicle Rates: Sensitivity to Data and Assumptions,report by the California Department of Insurance. July.Norowzi, B. 2003. North Carolina Department of Transportation,Office of Statewide Planning, personnel communication.Vollset, S.E. 1993. Confidence Intervals for a Binomial Proportion.Statistics in Medicine 12:809–824.12 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

A Bayesian Network Model of Two-Car AccidentsMARJAN SIMONCICInstitute for Economic ResearchKardeljeva pl. 17Ljubljana, SloveniaEmail: simoncicm@ier.siABSTRACTThis paper describes the Bayesian network methodfor modeling traffic accident data and illustrates itsuse. Bayesian networks employ techniques fromprobability and graph theory to model complex systemswith interrelated components. The model isbuilt using two-car accident data for 1998 from Slovenia,and inferences are made from the modelabout how knowledge of the values of certain variablesinfluences the probabilities for values of othervariables or outcomes (e.g., how seat-belt use affectsinjury severity). An advantage of the Bayesian networkmethod presented here is its complexapproach where system variables are interdependentand where no dependent and independent variablesare needed.INTRODUCTIONThis paper presents a Bayesian network model oftwo-car accidents based on different factors thatinfluence accident outcomes. The outcomes examinedare “fatality or serious injury” and “other outcomes.”Influencing factors include:1. road characteristics (e.g., roadway, pavement),2. traffic flow characteristics,KEYWORDS: Road accidents, modeling, Bayesian networks,machine learning.13

FIGURE 1 Example of a Bayesian NetworkSeasonX1Road conditionsX3WeatherX2Number of journeysX4Average speedX5Danger levelX6Number of casualtiesX7If we interpret the Bayesian network in probabilisticterms, the related joint distribution functionover a given domain can be written (described by nvariables) with the product 3 :Px ( 1 , x 2 ,...,x n ) = P( x i pa( x i ))i = 1(1)where X i is the variable and x i is its value; Pa (X i ) isthe set of variables that represents X i ’s parents 4 andpa(X i ) is a vector of actual values for all parents of X i .Let us note here the general validity of the chain ruleformula:Px ( 1 , x 2 ,...,x n ) = Px ( 1 )Px ( 2 x 1 ) •From our example in figure 1, we have:Px ( 1 , x 2 ,...,x 7 ) = P( x 1 )Px ( 2 x 1 ) •n∏Px ( 3 x 1 , x 2 )... P( x n x 1 , x 2 ,...,x n – 1 )Px ( 3 x 1 , x 2 )Px ( 4 x 1 , x 2 )Px ( 5 x 3 ) •Px ( 6 x 3 , x 5 )Px ( 7 x 4 , x 6 )Aside from the global semantics reflected in equation(1), there is also a local meaning related to aBayesian network. From figure 1, we see:Px ( 4 x 1 , x 2 , x 3 ) = P( x 4 x 1 , x 2 )where X 4 is independent of the variable X 3 given X 1and X 2 (reflecting the fact that X 3 is not among theparents of X 4 ). These local semantics are very usefulfor constructing a Bayesian network. Here, onlydirect causes (or predispositions) are selected as the3 The probability of the event A is denoted by P(A).4 Node A is the parent of node B if there is a link from Atoward B in the graph.parents of a given variable, which leads to the automaticfulfillment of local independence conditions.Links in Bayesian networks may have differentmeanings. If we have a link from node A to node B,this could mean:1. A causes B,2. A partially causes or predisposes B,3. B is an imperfect observation of A,4. A and B are functionally related, or5. A and B are statistically correlated.This paper employs the second meaning of a link.Bayesian networks for a certain domain can beused for inference purposes. With the network infigure 1, we will illustrate the meaning of inferenceand also show the difference between a Bayesiannetwork model and better known classical models,such as logistic regression. After a product specification(equation (1)) of a joint probability distributionis obtained, the probability of any event in thedomain can be expressed. Conditional events wherecertain variables have known values are especiallyinteresting. This type of probabilistic inference iscalled a belief update. An example for the domainrepresented in figure 1 is the following:PX ( 3 = ′slippery′ X 7 = ′high′)PX ( 7 = ′high′,X 3 = ′slippery′)= ------------------------------------------------------------------------------------- PX ( 7 = ′high′)=Px ( 1, x 2 , x 3 = ′slippery′,x 4 , x 5 , x 6 , x 7 = ′high′ )∑∑∑∑∑X 1X 2X 4X 5X 6/ ∑∑∑∑∑∑Px( 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 = ′high′ )X 1X 2X 3X 4X 5X 6SIMONCIC 15

For illustrative purposes, we have assumed that onepossible value of the variable X 3 (road conditions) is“slippery.” This variable can also take on other values.A similar description holds for variable X 7 .This expression can be further simplified, but this isunnecessary here.Let us now illustrate the difference between theBayesian network model and the classical logisticregression (for logistic regression see Agresti(1990) or Hosmer and Lemeshow (2000)). Themost significant difference is that with logisticregression the model’s dependent and independentvariables must be chosen; while, with theBayesian network model, all variables are treatedequally. The logistic regression has a response (ordependent) variable Y that is a categorical variablewith J (J ≥ 2) classes and a vector X (with pcomponents) of explanatory (or independent)variables that are also categorical 5 variables.Here, Y could be the number of casualties (withY = 1 for “high” and Y = 0 for “other”). Thecomponents of vector X could be the six othervariables from figure 1. The generalized logitmodel can be put in the following way:log PY ( = 1 X)PY----------------------------- =( = 0 X)β + ∑ β x 0 k kk=1(2)If the attributes X are also 0/1 variables, then thefollowing formula is valid 6 :P( Y = 1 x 1, x 2, ..., x k= 1...,x , p )P( Y = 0 x 1, x 2, ..., x k= 0 ,...,x p )-------------------------------------------------------------------------------------------------------------------------------------------------------------------PY ( = 0 x 1, x 2, ...,x k= 1 , ...,x p )PY ( = 1 x 1, x 2, ...,x k= 0 , ...,x p )= exp( β k );k = 1, 2 , ...,pThe expression is called the odds ratio and allowsan easy interpretation of the estimated parameters. 7In the logit model for figure 1, is the oddsthat the number of casualties will be high in the circumstancesgiven by variable x k = 1 relative to theodds that the number of casualties will not be highin the circumstances given by variable x k = 0.5 In a general logistic regression, they are not limited toonly these types of variables.6 A similar interpretation is possible if we have categoricalvariables with more than two values.7 Explanatory variables can be interdependent. Theirinterdependence plays a role in the estimation of theseparameters (see chapter 2 in Hosmer and Lemeshow2000).pexp( β k )It is obvious that the model shown in equation(2) does not explicitly take into account eventualinterdependence between variables of X, nor does itallow for an estimation of other probabilities thatcould be of interest (e.g., the belief update given asan example for the network in figure 1). Interdependencesamong variables in a Bayesian network areexplicit and represent a distinguishing feature of themethod.The general problem of computing posteriorprobabilities (or of a belief update) for large andstructurally more complex Bayesian networks iscomputationally very demanding (more precisely:NP-hard). The computational burden was the reasonthat the inference in Bayesian networks was initiallylimited only to special types of structures,namely tree-structured networks. Later, efficientalgorithms were proposed for more general types ofnetwork structures (Lauritzen and Spiegelhalter1988; Zhang and Poole 1996).Formal Definition of Bayesian NetworksBayesian networks contain qualitative (structural)and quantitative (probabilistic) parts. The qualitativepart is based on statistical independence statementsand can be represented by a directed acyclicgraph. The nodes are related to random variables ofinterest for a given domain, while the links correspondto a direct influence among the variables. Thequantitative part is captured by local probabilitymodels, given by a set of conditional probability distributions.Both the qualitative and quantitativeparts of the Bayesian network uniquely representthe joint probability distribution over a domain.The definitions follow.Definition 1. A Bayesian network B is a triplet (X,A, P) where:1. X is a set of nodes2. A is a set of links that, together with X, representa directed acyclic graph:G = (X, A)3. P = { P( x pa( x)):xεX}where Pa(X) is the set of parents of X, and pa(x) isits instantiation. 8 P stands for probability.8 When the state of a variable is known, we say that it isinstantiated. We have an instantiation of a set of variablesif each variable is instantiated (Jensen 2001).16 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

It is clear that P is the set of conditional probabilitiesfor all variables, given their parents. From definition1, the conclusion can be drawn that nodes and variablesare used interchangeably. Variables in a Bayesiannetwork are called nodes when we speak aboutthe graph.Graph G corresponding to a Bayesian networkhas to be acyclic. If cycles were allowed, the feedbackinfluence would be enabled. It is well knownthat feedback cycles are difficult to model quantitativelyand no calculus has been developed for theBayesian network to cope with these.The notion of conditional independency is abasic concept of Bayesian networks. We say that(random) variables A and B are independent giventhe variable C if the following is true:P(A|B,C) = P(A|C)This means that if the value of variable C is known,then knowledge of B does not alter the probabilityof A.The Bayesian network provides a graphic representationof many independency relationships thatare embedded in the underlying probability model.No formal definitions are provided here, but itshould be understood that the mathematical conceptionof d-separation is fundamental relative toindependence (Jensen 2001).The next definition gives the global interpretationof Bayesian networks.Definition 2. The prior joint probability P B of aBayesian network B is defined by the followingexpression:∏P B ( X) = Pxpax ( ( ))x ∈ XThe factorization in definition 2 rests on a set oflocal independence assumptions, asserting that eachvariable is independent of its predecessors 9 in thenetwork, given its parents. The opposite is also true.We can use the interdependence in constructingBayesian networks from expert opinion, becauseselecting as parents all the direct causes of a givenvariable satisfies the local conditional independenceconditions (Pearl 2000).For the Bayesian network from figure 1, the priorjoint probability is equal to:9 A is a predecessor of B if a directed path (a sequence oflinks) exists from A to B.P B ( x) = P B ( X 1 = x 1 , X 2 = x 2 ,...,X 7 = x 7 )= P( X 1 = x 1 ) PX ( 2 = x 2 X 1 = x 1 )•PX ( 3 = x 3 X 1 = x 1 , X 2 = x 2 ) •PX ( 4 = x 4 X 1 = x 1 , X 2 x )•When we have a joint probability distributiondefined on a set of variables X, we can calculate theprobability distribution of any subset S of X. Thiscalculation is called marginalization and is very usefulin inference exercises on Bayesian networks.Definition 3. Let S be a subset of the set of variablesX. The marginal probability P B (S) is definedbyLet us now suppose that some variables have specificvalues. In our example from figure 1, variablesX 7 and X 3 may be observed to have values “high”(X 7 ) and “slippery” (X 3 ). If Y⊆X is the set of variableswith actual (observed) values, Y 0 is the correspondingvector of values and X 1 ⊆ X is the set ofvariables of interest ( X 1 ⊆ X–Y), then the followingdefinition of posterior probability is useful.Definition 4. The posterior probabilityP B ( X 1 Y=Y 0 ) of X 1is defined by the expressionP B ( X 1 Y= Y 0 ) P B ( X 1 , Y Y 0= -------------------------------------= )P B ( Y Y )THE MODEL= 2PX ( 5 = x 5 X 3 = x 3 ) •PX ( 6 = x 6 X 3= x 3 , X 5 x )•= 5PX ( 7 = x 7 X 4 = x 4 , X 6 x ) •∑P B ( S) = P B( y)y∈X–S= 0= 6DataThis paper focuses on road accidents in which twocar drivers were involved. The empirical part isbased on data from the road accidents databaseassembled by the Slovenian Ministry of the Interiorfrom police reports. For the model, 1998 data containing36,704 Slovenian police accident reportswere used. From this total, 17,558 (48%) were ofthe selected type. To illustrate the risk of Sloveniandrivers being involved in a two-car accident, somebasic data show that, in 1998, 797,855 cars wereregistered in Slovenia (the country has 2 millioninhabitants). Because we are looking at accidentsinvolving two cars, we know that approximatelySIMONCIC 17

4% of the Slovenian car fleet was involved in accidentsof this type that year.Table 1 presents data on two-car accidents forselected variables. Variables from Accident_type toCause (the first column of table 1) are related to theaccident, while variables from Age to Injury arerelated to the drivers. 10 The share of accidents thatresulted in a fatality or serious injury of at least oneperson is 1.9%. Over 70% of accidents occur inbuilt-up areas and more than half happen in goodweather and under normal traffic conditions.Among participants, the lion’s share corresponds todrivers 25 to 64 years old, yet the share of driversunder 25 years of age is also relatively high (23%).For drivers involved in accidents, a significant proportionhas less than one year of driving experience(12.9%). Only a small share of drivers involved inaccidents was intoxicated (4.3%).Bayesian Network EstimationA Bayesian network for a given domain can be estimatedusing different approaches. This paper uses atemplate model that should not vary from one problemto another. Our purpose here is to estimate afixed Bayesian network over a given set of variables,obtained by a combination of expert judgment andempirical data. Specifications for some alternativepossibilities for estimating a Bayesian network arepresented below.A difficult part of building a Bayesian network isquantifying probabilities, which can be derivedfrom various sources:1. from domain experts (subjective probabilities),2. from published statistical studies,3. derived analytically, or4. learned directly from raw data.This paper uses the last option, mainly because ofthe availability of a relatively large database.Sometimes the process of learning the structureof a Bayesian network (if necessary) may be evenmore difficult than quantifying probabilities.According to the structure, models can be classifiedas those with a known structure or those with an10 Passengers are taken into account only indirectly. Afatal accident may mean that both drivers were onlyinjured, but at least one passenger was killed.unknown structure. We experimented with bothoptions.There are basically two different approaches tolearning the structure of a Bayesian network fromdata: 1) search and scoring methods and 2) dependencyanalysis methods. In the first approach, differentscoring criteria are used for evaluatingcompeting structures. Two of the well-known methodsof this type are the Bayesian scoring method(Cooper and Herskovits 1992) and the minimumdescription length method (Lam and Bacchus1994). Because learning a Bayesian network structureby a search and score approach is NP-hard, differentheuristic searches have been proposed.Algorithms from the second group try to discoverthe dependences among variables from data andthen use them to infer the structure. During this process,a conditional independence test, usually basedon the concept of mutual information of two nodes(variables), X and Y, is usedP e ( xy , )IXY ( , ) = ∑ P e ( xy , )ln ---------------------------x,y P e ( x)P e ( y)In this expression, P e denotes the observed relativefrequencies in the dataset. Conditional mutualinformation is defined analogously:PIXYZ ( , ) e ( xy , z)= ∑ P e ( xyz , , )ln -------------------------------------x, y,z P e ( xz)P e ( yz)Z can be a single node or a set of nodes. Mutualinformation I is non-negative and equal to 0 whenX and Y are conditionally independent. The higherthe mutual information, the stronger the dependencebetween X and Y. In heuristic algorithms acertain threshold ε is usually used: if I(X,Y) issmaller than ε , then X and Y are taken as marginallyindependent. Similarly, if I(X,Y|Z) is smallerthan ε,we consider X and Y as conditionally independentgiven Z.All these methods can be expected to find thecorrect structure only when the probability distributionof the data satisfies certain assumptions. Butgenerally both types of methods find only approximationsfor the true structure.According to the available data, models for learningBayesian networks can be classified into thosewith complete data available or those with incompletedata available. In the first case, all variables are18 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 1 Data on Two-Car Accidents in Slovenia: 1998Variable Values FrequencyRelativefrequency (%)Accident_type Fatality or serious injury 654 1.9Other 34,462 98.1Alco12 1 No 32,164 91.6Yes 2,952 8.4Weekday After weekend 5,442 15.5Before weekend 6,338 18.0Weekend 9,140 26.0Workday 14,196 40.4Settlement No 10,156 28.9Yes 24,960 71.1Weather Bright 20,088 57.2Cloudy 9,094 25.9Fog 454 1.3Rainy 4,264 12.1Snow 882 2.5Other 334 1.0Traffic Dense 5,530 15.7(Grid)lock 122 0.3Normal 20,214 57.6Sparse 8,830 25.1Unknown 420 1.2Roadway Dry 24,142 68.8Ice 546 1.6Other 220 0.6Slippery 1,938 5.5Snow 698 2.0Wet 7,572 21.6Night No 26,658 75.9Yes 8,458 24.1Cause HI Inappropriate speed 4,956 14.1OS Other 1,472 4.2PD Failing to give way 7,244 20.6PR Wrong overtaking 1,520 4.3PV Car maneuvers 8,296 23.6SV Wrong side/direction 4,754 13.5VR Unsuitable safety distance 6,874 19.6Age ≤ 24 8,008 22.825–64 25,538 72.765–inf 1,570 4.5Experience 0–1 4,530 12.91–5 7,000 19.96–10 7,235 20.611–inf 16,351 46.6Alcohol No 33,593 95.7Yes 1,523 4.3At-fault driver No 16,651 47.4Yes 18,465 52.6Sex Female 9,036 25.7Male 26,080 74.3Belt use No 4,342 12.4Yes 30,774 87.6Injury Fatality or serious injury 247 0.7Other 34,869 99.31 Alco12 (defined for each accident) is determined by the value for the variable Alcohol (defined for both driversinvolved in the accident). Its value is "Yes" if at least one of the two drivers was intoxicated and is "No" otherwise.Source: Database of the Slovenian Ministry of the Interior.SIMONCIC 19

observed for all instances in the database while, inthe second case, values for some variables may bemissing or some variables may not even be observed(hidden variables). Because the available databaseused for this paper contains complete data, the firstpossibility is relevant.Variables Considered in the ModelSome conditions of an accident may be called exogenous.They are tied to the accident and happenwithout the volition or action of the driversinvolved. Variables from table 1 in this category are:1. weather condition,2. weekday,3. settlement (whether an accident occurs in abuilt-up area or not), and4. daytime (whether an accident occurs duringthe night or day).These external conditions influence some internaland objective conditions also tied to the accident,such as traffic and the roadway. For each accident,these conditions are also exogenous. 11Besides these internal and objective conditions,there are also internal subjective (and not volitional)conditions that relate to the drivers involved:1. age and sex,2. driving experience,3. intoxication (alcohol), and4. use of a seat belt.Objective and subjective internal conditions influencethe cause of an accident. The particularcause further influences the outcome of the accident.Here, only two types of accident outcomesare considered: a fatality or serious injury, andother. 12 Subjective internal conditions and thecause of an accident influence the type of driverinjury.Different network structures can reflect theseconditions. In the process of finding a suitablenetwork structure, we experimented with Power-Constructor. PowerConstructor (Cheng et al.2001) is a computer program that can estimate11 It is assumed that an individual driver does not significantlyinfluence traffic conditions.12 The variable Accident_type is related to the accident,while the variable Injury is related to the driver. This presentsno problem for an analysis with Bayesian networks.the Bayesian network structure if a database ofcases is available. The method (Cheng et al. 1997)used in PowerConstructor for comparing competingstructures is of the dependency analysis typeand requires O(n 4 ) conditional independencetests (n being the number of variables). The programis able to take into account additionalrestrictions on variables (e.g., partial ordering,forbidden links, roots, leaves, and causes andeffects).For this research, external variables and thevariables related to the driver (e.g., age, sex, andexperience) were among the root nodes (links canonly point out of such nodes). Variables relating tothe type of accident and the drivers’ injuries wereput among the leaf nodes (links can only point intosuch nodes). The variable related to the fault of thetwo drivers involved was also put among theleaves. PowerConstructor produced results prettymuch as anticipated, except for some links thatwere missing.Our anticipation was also based on some relevantfindings from the literature. Kim (1996) analyzedthe differences between male and femaleinvolvement in motor vehicle collisions in Hawaiiand found that male drivers are:1. 4 times more likely than female drivers to notwear a seat belt,2. 3.6 times more likely than female drivers to beinvolved in alcohol-related collisions,3. 2 times more likely than female drivers to beinvolved in speed-related collisions, and4. 1.3 times more likely than female drivers to beinvolved in head-on collisions.For the relationship between road accident severityand recorded weather, Edwards (1998) based herconclusions on data from police reports and foundthat:1. accident severity decreases in rain as comparedwith good weather,2. accident severity in fog shows geographicalvariation, and3. evidence for accident severity in high winds isinconclusive.20 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

FIGURE 2 The Network StructureExperienceAt_faultAlcoholAgeSexBelt_useInjuryNightAlco12WeekdayRoadwayWeatherCauseTrafficSettlementAccident_typeIt is also well known that older drivers are morelikely to be killed if involved in a fatal crash thanyounger drivers. Based on these results and commonsense, additional restrictions for PowerConstructorincluded the following links:1. Age → Injury (older drivers are expected to bemore prone to serious injuries than youngerdrivers)2. Seat belt → Injury (drivers not wearing a seatbelt are likely to be more vulnerable)3. Experience → At-fault driver (drivers with littledriving experience are more likely to be atfault)4. Sex → Seat belt use5. Sex → Alcohol6. Alcohol → At-fault driverThe resulting network is presented in figure 2. It isevident that only a small number of all theoreticallypossible interdependences was found to beimportant.SIMONCIC 21

Weekday, daytime, and weather conditions influencetraffic. An assumption was made that the shareof intoxicated drivers is greater for accidents thathappen at night than during the day. Only weatherinfluences road conditions. 13 The type of accidentand the use of a seat belt also depend on whether anaccident happens in a built-up area or not (settlementvariable). A smaller share of drivers wearing aseat belt in built-up areas was expected.Figure 2 also takes into account the differentcharacteristics of drivers. Drivers with little drivingexperience are more likely to be at fault in an accidentthan more experienced ones. There are also significantdifferences between men and women, withwomen being more likely to use seat belts than men.On average, older drivers are more prone to seriousinjuries than younger ones.The central variable in figure 2 is the cause of anaccident, 14 which is influenced by road, weather,and traffic conditions and by the variable related todriver intoxication. Finally, the outcome of an accident(defined as the most serious injury to participantsin an accident) is largely conditioned by thecause of the accident.13 New variables could have been added here but werenot in order to maintain a more manageable total numberof variables.14 This is partly conditioned by the large number of possiblestates (seven) and by the method used in Power-Constructor.The estimated structure seems plausible, but adifferent one may also be acceptable. The scoringfunctions used in the optimizing approach couldshed some light on the quality of the estimatedBayesian network. Furthermore, the Kullback-Leibler measure of divergence could be used. Itsvalue could be computed for the structure at handbut would only be of interest when comparing twoor more specific structures. By presenting the mostprobable explanation (MPE), the correspondingprobability, and the relative frequency obtainedfrom the database, the statistical quality of the givennetwork can be seen. MPE is given by the mostprobable configuration of values for all variables inthe Bayesian network. For the estimated structure,the MPE is given by the following values forvariables:Night = No; Weekday = Wrk (working day);Weather = Bright; Settlement = Yes; Experience =E11–Inf (driver’s experience of 11 years or more);Sex = Male; Age = A25–64; Seat_belt = Yes; Alcohol= No; Alco12 = No; Roadway = Dry; Traffic =Norm (normal); Cause = PV (car maneuvers); Atfault_driver= No; Injury = Oth (other then fatalityor serious injury); Accident_type = OthGiven the estimated structure of the Bayesian networkand the conditional probabilities for eachnode, the probability of the MPE can be computedas shown below.P(MPE) = P(Night = No) • P(Weekday = Wrk) • P(Weather = Bright) • P(Settlement = Yes) •P(Experience = E11–Inf) • P(Sex = Male) • P(Age = A25–64) •P(Roadway = Dry|Weather = Bright) •P(Traffic = Norm|Weather = Bright, Weekday = Wrk, Night = No) •P(Belt_use = Yes|Sex = Male, Settlement = Yes) •P(Alcohol = No|Night = No, Weekday = Wrk, Sex = Male) •P(Alco12 = No|Night = No, Weekday = Wrk) •P(At-fault_driver = No|Experience = E11–Inf, Alcohol = No) •P(Cause = PV|Roadway = Dry, Traffic = Norm, Weather = Bright, Alco12 = No) •P(Injury = Oth|Age = A25–64, Belt_use = Yes) •P(Accident_type = Oth|Settlement = Yes, Cause = PV) = 0.0018122 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

FIGURE 3 Estimated Unconditional Probabilities of the Bayesian NetworkExperienceE0–1 12.9E1–5 19.9E6–10 20.6E11–INF 46.6AgeA1–24 22.8A25–64 72.7A65–INF 4.47FemaleMaleSex25.774.3AlcoholNo 95.7Yes 4.32Alco12No 91.6Yes 8.35NoYesBelt_use12.487.6NoYesFosOthAt_fault47.452.6Injury1.0798.9NightNo 75.9Yes 24.1WeekdayAft 15.5Bef 18.0Wkn 26.0Wrk 40.4RoadwayDry 68.7Ice 1.57Oth 0.64Slip 5.52Snow 1.99Wet 21.6HIOSPDPRPVSVVRCause13.74.6020.54.7823.513.519.3BrightCloudyFogOtherRainySnowWeather57.225.91.300.9512.12.51DensLockNormSparUnknTraffic15.70.5157.425.01.36Note: See table 1 for explanation of variables.SettlementNo 28.9Yes 71.1FosOthAcc_type1.8798.1An examination of databases for 1998 and 1999 producedthe following relative frequencies for MPE:P e (1998) = 94 / 35116 = 0.00268P e (1999) = 103 / 39950 = 0.00258It is obvious that even the most likely explanation hasa small probability of its appearance. A comparisonof P(MPE) and P e (MPE) can serve as an indication ofthe quality of the estimated Bayesian network.Figure 3 presents probabilities (also called beliefs)estimated from the database of accidents for 1998and based on the assumption of the network structuregiven in figure 2. Values of variables related tothe different nodes are self-explanatory. Let us recallthe abbreviation used for accident type and injury: 1)Fos means a fatality or serious injury, and 2) Othmeans other (less serious) outcomes. (Abbreviationsfor values related to the variable Cause are explainedin table 1.) Figure 3 shows only the unconditionalprobabilities that correspond to each node (and notthe conditional probabilities discussed earlier).INFERENCE IN THE BAYESIAN NETWORKThe discussion here focuses on only three tables withspecific inference results. For the inference process,Netica software (Norsys 1997) was used, and itproved to be very convenient and effective. Resultsare presented in tables 2 to 4 where predeterminedSIMONCIC 23

TABLE 2 Inference Results: Evidence for Accident TypeProbabilitiesAcc_typeCause(HI)Cause(OS)Cause(PD)Cause(PR)Cause(PV)Cause(SV)Cause(VR)Settl(No)Night(Yes)Alco12(Yes)Fos 0.279 0.034 0.276 0.065 0.092 0.229 0.026 0.612 0.253 0.106Oth 0.134 0.046 0.204 0.048 0.238 0.133 0.196 0.283 0.241 0.083Key: Fos = fatality or serious injury, HI = inappropriate speed, OS = other, Oth = other, PD = failing to give way, PR = wrong overtaking,PV = car maneuvers, Settl = settlement, SV = wrong side/direction, VR = unsuitable safety distance.Note: For definition of Alco12, see table 1.TABLE 3 Inference Results: Evidence for Intoxication VariablesProbabilitiesIntoxCause(HI)Cause(OS)Cause(PD)Cause(PR)Cause(PV)Cause(SV)Cause(VR)Sex(Male)Night(Yes)At_fault(Yes)Yes 0.204 0.050 0.180 0.069 0.129 0.254 0.113 0.909 0.752 0.879No 0.131 0.045 0.208 0.046 0.245 0.125 0.201 0.753 0.206 0.510Key: HI = inappropriate speed, OS = other, PD = failing to give way, PR = wrong overtaking, PV = car maneuvers, SV = wrong side/direction,VR = unsuitable safety distance.TABLE 4 Inference Results: Evidence for Some Exogenous VariablesProbabilitiesExogenousvariablesInjury(Fos)At_fault(Yes)Acc_type(Fos)Alcohol(Yes)Alco12(Yes)Risky values 0.015 0.605 0.046 0.145 0.245Nonrisky values 0.008 0.497 0.009 0.004 0.043Key: Fos = fataility or serious injury.Note: For definition of Alco12, see table 1.values for a selected categorical variable (or variables)are given in the first column and probabilities forvariables of interest are seen in other columns.Table 2 shows inference results based on evidencefor the variable related to the type of accident. Inferenceresults are presented only for variables Cause,Settlement, Night, and Alco12. The probability thatthe cause of the accident is inappropriate speed (HI)is 0.279 in the case of accident type “Fos” (fatalityor serious injury) and 0.134 for the accident type“Oth” (less severe injury). The odds ratio is therefore2.1. Only a slightly smaller odds ratio is foundfor cause SV (wrong side/direction); a similar oddsratio for the Settlement variable (2.2); smaller oddsratios for variables Night and Alco12; and oddsratios smaller than 1 for cause PV (car maneuvers),OS (other), and VR (safety distance).Table 3 reports the inference results based on theevidence for the intoxication variables (Alcohol andAlco12). The probability of an accident taking placeat night is 0.752 if drivers are intoxicated and 0.206if they are not. The odds ratio is, therefore, 3.7.Odds ratios are also high for variables Sex, At_fault,and Cause (for the values related to inappropriatespeed and driving on the wrong side of the road).Inference results based on the evidence for someexogenous variables are presented in table 4. Theresults shown correspond to a risky situation (drivingat night, outside built-up areas, on the weekend,and in rainy weather) and to risky demographic variables(young and inexperienced drivers, i.e., malesless than 25 years of age and less than 1 year of drivingexperience). Nonrisky values were defined withthe opposite values for binary variables. For other(non-binary) variables, the following values wereused: age between 24 and 65, driving experiencemore than 11 years, and for the weekday the workingday. Odds ratios are especially high for the typeof accident and intoxication variables.While more inference results and a complete pictureof the influence on all variables are available,this paper presents only the more interesting variablesbecause the primary aim is to illustrate thecapabilities of Bayesian networks in this domain ofknowledge. A more indepth analysis of inferenceresults could be used for detecting any weaknesses24 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

in the Bayesian network and for improving its structure.By using data for more than one year, theresults become more reliable. New variables canalso be added, for example, actual data on trafficflows on the road sections on which accidents occuror other specific characteristics of roads and regions.CONCLUSIONSThis paper deals with road accidents involving twocar drivers. A model of such accidents is presentedto capture the interrelations between different relevantvariables. To this end, Bayesian networks thathave proved their modeling capabilities in differentknowledge domains were used. The paper firstintroduces Bayesian networks on a small exampleand then formally defines them. After presentingdata on two-car accidents for Slovenia in 1998, astructure is proposed based on knowledge of thedomain and on computer experiments. For thisstructure the corresponding probabilities were estimatedfrom the available database. We then demonstratehow the estimated Bayesian network can beused for drawing inferences. Inference results areconsistent with expectations as far as the directionof influence is concerned.The estimated Bayesian network can be regardedas a compact and structured representation of thegiven database of two-car accidents. This representationrelates to specific types of accidents in a givencountry and year. It also enables different inferences,but other methods, such as logistic regression,should also be used.Based on the research presented here, we feel thatBayesian networks can be fruitfully applied in thedomain of road-accident modeling. Compared withother well-known statistical methods, the mainadvantage of the Bayesian network method seemsto be its complex approach where system variablesare interdependent and where no dependent andindependent variables are needed. The method’schief weakness is the somewhat arbitrary search foran appropriate network structure. Nevertheless, theresults shown here are encouraging and point topossible directions for improvement, such as includingmore variables and larger datasets that covermore years. Extending the Bayesian network (withgood performance results) into a decision networkis another possibility.ACKNOWLEDGMENTSThe Ministry of Science and Education of theRepublic of Slovenia supported this research.Thanks go to anonymous referees for suggestionson improving this paper and to Jie Cheng for providinghis PowerConstructor software for use withthe data. Any errors, however, remain ours alone.REFERENCESAgresti, A. 1990. Categorical Data Analysis. New York, NY:Wiley & Sons.Cheng, J., D.A. Bell, and W. Liu. 1997. Learning Belief Networksfrom Data: An Information Theory Based Approach.Proceedings of the Sixth ACM International Conference onInformation and Knowledge Management.____. 2001. Learning Belief Networks from Data: An EfficientApproach Based on Information Theory. Available at http://www.cs.ualberta.ca/~jcheng/bnpc.htm, as of January 24, 2005.Cooper G.F. and E. Herskovits. 1992. A Bayesian Method forthe Induction of Probabilistic Networks from Data. MachineLearning 9:309–347.Edwards, J.B. 1998. The Relationship Between Road AccidentSeverity and Recorded Weather. Journal of Safety Research29(4):249–262.Hosmer, D.W. and S. Lemeshow. 2000. Applied Logistic Regression.New York, NY: Wiley & Sons.Jensen, F.V. 2001. Bayesian Networks and Decision Graphs.New York, NY: Springer-Verlag.Jordan, M.I., ed. 1999. Learning in Graphical Models. Cambridge,MA: The MIT Press.Kim, K.E. 1996. Differences Between Male and Female Involvementin Motor Vehicle Collisions in Hawaii, 1986–1993.Proceedings from the Second National Conference. Availableat http://www.durp.hawaii.edu.Lam, W. and F. Bacchus. 1994. Learning Bayesian Belief Networks:An Approach Based on the MDL Principle. ComputationalIntelligence 10:269–293.Lauritzen, S.L. and D.J. Spiegelhalter. 1988. Local Computationswith Probabilities on Graphical Structures and TheirApplication to Expert Systems. Journal of the Royal StatisticalSociety B 50(2):157–194.Norsys Software Corp. 1997. Netica Application User's Guide.Vancouver, Canada.Pearl, J. 2000. Causality. Cambridge, UK: Cambridge UniversityPress.Zhang, N.L. and D. Poole. 1996. Exploiting Causal Independencein Bayesian Network Inference. Journal of ArtificialIntelligence Research 5:301–328.SIMONCIC 25

Development of Prediction Models for Motorcycle Crashes atSignalized Intersections on Urban Roads in MalaysiaS. HARNEN 1R.S. RADIN UMAR 2,*S.V. WONG 3W.I. WAN HASHIM 41 Road Safety Research CenterUniversiti Putra Malaysia, 43400 UPM Serdang,Selangor Darul Ehsan, Malaysia;and Department of Civil EngineeringUniversitas Brawijaya, Malang, East Java, Indonesia2 Road Safety Research Center andDepartment of Civil EngineeringUniversiti Putra Malaysia, 43400 UPM Serdang,Selangor Darul Ehsan, Malaysia3 Road Safety Research Center andDepartment of Mechanicaland Manufacturing EngineeringUniversiti Putra Malaysia, 43400 UPM Serdang,Selangor Darul Ehsan, Malaysia4 School of Civil EngineeringUniversiti Sains Malaysia,14300 Nibong Tebal,Seberang Perai Selatan, Pulau Pinang, MalaysiaABSTRACTBecause more than half of the motor vehicles inMalaysia are motorcycles, the safety of this form oftransportation is an important issue. As part of amotorcycle safety program, Malaysia became thefirst country to provide exclusive motorcycle lanes inthe hopes of reducing motorcycle crashes alongtrunk roads. However, little work has been done toaddress intersection crashes involving motorcycles.This paper provides models for predicting motorcyclecrashes at signalized intersections on urban roadsin Malaysia. A generalized linear modeling techniquewith a quasi-likelihood approach was adoptedto develop the models. Traffic entering the intersection,approach speed, lane width, number of lanes,shoulder width, and land use at the approach of theintersection were found to be significant in describingmotorcycle crashes. These findings should enableengineers to draw up appropriate intersection treatmentcriteria specifically designed for motorcyclelane facilities in Malaysia and elsewhere.INTRODUCTIONMotorcycle crashes continue to be a problem inboth developing and developed countries. Fatalityrates (measured in deaths per 10,000 registeredE-mail addresses:* Corresponding author—radinumx@eng.upm.edu.myS. Harnen—hsulistio@telkom.netS.V. Wong—wongsv@eng.upm.edu.myW.I. Wan Hashim—cewhwi@eng.usm.myKEYWORDS: Motorcycle crashes, generalized linearmodels, prediction model, intersection crashes, motorcyclecrash model.27

FIGURE 1 Motorcycle Crashes in Malaysia: 200090Number (thousands)8070DeathsInjuriesMotorcyclesinvolved60504030201001990 1991 1992 1993 1994 1995 1996 1997 1998 1999 20001990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000Deaths 1,826 2,062 2,307 2,416 2,946 3,362 3,778 3,760 3,409 3,410 3,519InjuriesMotorcyclesinvolved15,857 16,235 21,957 26,226 31,957 34,372 35,960 38,317 38,448 36,100 33,65127,845 29,225 39,272 48,511 58,921 66,508 73,268 80,100 77,298 76,032 79,816Source: Polis Di Raja Malaysia, Statistical Report: Road Accidents, Malaysia 2000 (Kuala Lumpur, Malaysia: 2002, Traffic Branch,Royal Malaysian Police).vehicles) in these crashes are much higher than innonmotorcycle 1 crashes. In the United States, theNational Highway Traffic Safety Administration(USDOT 2002) reported a fatality rate of 6.5 per100 million vehicle-miles traveled, and motorcyclistswere about 26.1 times as likely as passengercar occupants to die in a motor vehicle traffic crash.The Canadian rate was 4.7 in 1999, which rose to5.1 in 2000; the Canadian nonmotorcycle fatalityrate in 2000 was 0.7 (Transport Canada 2001).Similarly large rates have been reported in otherdeveloped countries: Australia’s rate was 6.2 in2001, an increase of about 9% from 2000 and morethan 4 times the fatality rate of other road users(ATSB 2002); the United Kingdom’s rate was 7.3 in2000, decreasing to 6.6 in 2001, about 10 times thefatality rate for passenger car occupants (DfT2002); Swedish (SI 2000), French, and German(OECD 2002) rates in 2000 were 4.1, 5.3, and 2.2,respectively.In developing countries, deaths and serious injuriesfrom motorcycle accidents constitute a largeportion of total road casualties especially in Asiancountries, because motorized two-wheelers make up1 Nonmotorcycle refers to all types of motorized vehiclesexcluding motorcycles.40% to 95% of their vehicle fleets. As a result, morethan half the road fatalities were riders or pillionpassengers.In Malaysia, motorcycles constitute more thanhalf the total vehicle population and contributemore than 60% of the casualties (deaths and seriousand slight injuries) in traffic crashes. In 2000,79,816 crashes involved motorcycles, an increase ofalmost three-fold from 1990. Of these, almost3,000 motorcyclists were killed every year duringthis period (figure 1). Moreover, motorcyclist casualtieswere much higher than those of occupants inother types of vehicles (figure 2).In an attempt to reduce casualties, exclusivemotorcycle lanes were constructed along majortrunk roads in the country. Since the implementationof this initiative, a number of studies (RadinUmar 1996; Radin Umar et al. 1995, 2000) havebeen carried out to evaluate the impact of theselanes on motorcycle crashes on highway links.Results indicate the lanes had a significant effect (p

FIGURE 2 Motorcycle Rider Casualties Comparedwith Casualties for Occupants in OtherTypes of Vehicles: 2000Casualty rate Casualties (%)80806040200Fatality rate Injury rate Casualties0.21Bus Lorry Car MotorcycleKey: Fatality rate = fatalities per 10,000 registered vehicles; injuryrate = injuries per 10,000 registered vehicles; casualties (%): as apercentage of all casualties in traffic crashes in Malaysia.Source: Polis Di Raja Malaysia, Statistical Report: Road Accidents,Malaysia 2000 (Kuala Lumpur, Malaysia: 2002, Traffic Branch, RoyalMalaysian Police).ment criteria specifically designed for motorcyclelane facilities.Recent studies on traffic crash modeling haveused the generalized linear modeling (GLM)approach (McCullagh and Nelder 1989) with Poissonor negative binomial error structure. Thisapproach is widely accepted as more appropriate forthe characteristics of crashes (i.e., discrete, rare, andindependent) than the classical linear model basedon normal error structure with a constant variance.Crashes can be characterized by their mean numberper unit time and are simply represented by a Poissonrandom variable.Many researchers have reported the usefulness ofthe GLM approach in developing predictive modelsfor traffic crashes using either cross-sectional ortime series analysis (Griebe and Nielsen 1996;Mountain et al. 1996, 1998; Tarko et al. 1999; Vogtand Bared 1998; Vogt 1999; Radin Umar et al.1995, 2000; Radin Umar 1996; Bauer and Harwood2000; Saied and Said 2001; Taylor et al.2002). For example, an earlier study on crashes atintersections prepared for the Federal HighwayAdministration of the U.S. Department of Transportationin connection with the development of theInteractive Highway Safety Design Model (IHSDM)6040200(Bauer and Harwood 2000) provided direct inputinto the Accident Analysis Module of the IHSDM.The analysis included all collision types usingthree-year crash frequencies (1990 to 1992) andgeometric design, traffic control, and traffic volumedata from a database provided by the CaliforniaDepartment of Transportation. The analysis wasperformed using the SAS GENMOD procedure.The models were developed using the GLMapproach with a log-normal regression model and aloglinear regression model (a Poisson regression followedby a negative binomial regression model). Inthis study, the 10% significance level of the t-statisticof the parameter estimates was used to assess thesignificance of the fitted model. The explanatoryvariables (continuous and categorical) that followwere found to be significant in explaining crashes atintersections: major road ADT (average daily traffic) andminor road ADT, average lane width on major roads, number of lanes on major and minor roads, design speed of major roads, major-road right-turn and left-turn channelizations, access control on major roads, functional class of major roads, outside shoulder width on major roads, terrain, road lighting, minor-road right-turn channelization, major-road left-turn prohibition, and median on major roads.As an extension to our earlier analysis (Harnen etal. 2003a, 2003b), this paper presents the developmentof prediction models for motorcycle crashes atsignalized intersections along both the exclusive andnon-exclusive motorcycle lanes on urban roads inMalaysia. We used the GLM approach with Poissonerror structure to develop our models. The parameterestimates and tests of their significance werecarried out using GLIM 4 statistical software (NAG1994), which is specifically designed for fitting generalizedlinear models.HARNEN, RADIN UMAR, WONG & WAN HASHIM 29

THE DATASelected IntersectionsThe intersections studied were located on urban roadsin four districts of the state of Selangor, Malaysia. Thedata collected covered motorcycle crashes, traffic andpedestrian flow, approach speed, intersection geometry,number of legs, and land use. The intersectionswere selected based on the following conditionsbetween 1997 and 2000: a) only marginal change inland use; b) no major modifications or upgrading; c)an equal number of lanes on the corresponding majorand minor roads; d) only marginal change of signalcharacteristics, for example, signal timing and signalphasing; e) no access road within a 50-meter distancefrom the intersection stop lines; and f) intersectionsmust have had fatalities and/or serious and slight injuriesin crashes. Please note that while data were collectedon signal characteristics they are not analyzedhere. However, they will be included in future work.Based on the intersection files (142 signalized intersectionswith motorcycle crashes in the period 1997 to2000) extracted from the Microcomputer AccidentAnalysis Package (MAAP) database and visits to thesites to ensure that they met the requirements, 51intersections were chosen. In this study, motorcyclecrashes occurring within 50 meters of the correspondingstop lines of the intersection were classified asintersection crashes.Motorcycle Crash DataFour-year’s worth of motorcycle crash data on theselected intersections, from 1997 through 2000, werecollected from the police crash record form, POL 27(Pin 1/91). The POL 27 is designed for easy completion(Radin Umar et al. 1993) and is fully compatiblewith the MAAP database developed by the TransportResearch Laboratory (Hills and Baguley 1993). Datawere extracted from two complementary sources: theMAAP database for fatal and serious injury crashes,and the Computerized Accident Recording System(CARS 2000) database for slight injury crashes. Bothdatabases are based on the POL 27 record form. 22 The MAAP database is located at the Road SafetyResearch Center, Universiti Putra Malaysia, while theCARS 2000 database is located at the Traffic Branch,Royal Malaysian Police Headquarters.Traffic Flow DataIn this study, the estimated annual average dailytraffic (AADT) defines the traffic flow on eachselected intersection. Hourly traffic volume (disaggregatedby nonmotorcycles and motorcycles)was counted on major- and minor-roadapproaches and then converted to AADT byusing hourly, daily, and monthly factors. Thesefactors were determined based on 24-hour permanenttraffic count station and traffic censusdata, available from the Highway Planning Unit,Ministry of Works in Malaysia (HPU 2001a,2001b) and were developed using the methodproposed by McShane et al. (1998). The AADT isexpressed in terms of the number of nonmotorcyclesper day and motorcycles per day.Other Data UsedApproach speed and pedestrian flow were also consideredin this study. However, while these data werenot available in the database, they were collectedonsite following criteria used by Golias (1997) in anearlier study. The 85th percentile approach speed onmajor and minor roads was used to represent theapproach speed on each intersection. Arndt andTroutbeck (1998) also considered this characteristicin an earlier study on traffic crashes. The approachspeeds were measured at a 50-meter distanceupstream from the corresponding stop lines of theintersection and were counted for all vehicles movingduring the time the signal was green.Pedestrian flow at each intersection was definedas the total number of pedestrian crossings per hourcounted on major- and minor-road approaches. Itshould be noted that pedestrians per hour ratherthan pedestrians per day was used because therewas no supporting data to convert hourly pedestrianflow to annual average daily pedestrians (theAADT for pedestrians).Intersection geometry, number of legs, and landuse for each selected intersection were also observedonsite. Of the 51 selected intersections, 27 werethree-legged while 24 were four-legged. The landuse adjacent to the intersection was classified intotwo categories: commercial and noncommercialareas. A commercial area was defined as an areawith a concentration of offices, shops, and railwayand bus stations, while residential areas and unusedland come under the category of noncommercial30 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

FIGURE 3 Typical Layout of Intersection GeometrySHDW6LWn2LNn1Minor roadSHDW1LNm1LWm1Major roadLNm2LWm2SHDW2Key: LWm1, 2, 3, 4 = lane width on major road approaches;LWn1, 2 = lane width on minor road approaches;LNm1, 2 = number of lanes on major road approach;LNn1 = number of lanes on minor road approach;SHDW1, 2, 3, 4, 5, 6 = shoulder width on major- and minor-roadapproaches.SHDW3area. Of the 51 intersections, 33 were located incommercial areas and 18 were in noncommercialareas. Figure 3 shows a typical layout of intersectiongeometry considered in the study.MODEL DEVELOPMENTLWm3Major roadLWm4LNm1 LNm2Minor roadPrior to carrying out the statistical modeling, we didsome preliminary work to facilitate the modelingprocess. This included formulating the theoreticalmodels, specifying the error structure and link function,identifying the model variables, and definingthe goodness-of-fit and significance tests.Using our earlier analysis of motorcycle crashes atintersections (Harnen et al. 2003a, 2003b) and studiesof traffic crashes at intersections (Griebe and Nielsen1996; Vogt and Bared 1998; Vogt 1999; Bauer andHarwood 2000; Saied and Said 2001), we defined themodel structure and the variables included.Two separate models (Models 1 and 2) were proposed.These models used the same data and structurebut employed different explanatory variables. InModel 1, the response variable was the number ofmotorcycle crashes and the explanatory variablesSHDW4LNn1LWn1SHDW5were traffic flow (disaggregated by nonmotorcyclesand motorcycles both for major and minor roads),pedestrian flow, approach speed, lane width, numberof lanes, number of legs, shoulder width, and landuse. The continuous variables were identified as trafficflow, pedestrian flow, approach speed, lane width,and number of lanes, while the categorical variableswere number of legs with two-factor levels, shoulderwidth with three-factor levels, and land-use with twofactorlevels. In Model 2, the response variable wasmotorcycle crashes, while the explanatory variableswere traffic flow and shoulder width. Both trafficflow and shoulder width were continuous variables.The main differences in these two models are theexplanatory variables included. Model 2, which hasthree continuous variables, is simpler than Model 1and can be used further to establish major- andminor-road flow criteria for intersection treatment.This can be done by using the design curves relatingmajor- and minor-road flows and shoulder widthsdeveloped based on Model 2.Model 1, which has 13 variables (combination ofcontinuous and categorical), was aimed at givingmore room to engineers for analyzing the variablescontributing to motorcycle crashes. Software that isspecifically designed for Model 1 application couldmake it easier and faster to analyze the variablesand estimate motorcycle crashes.Taking the earlier studies on intersection crashmodeling into consideration, the theoretical modelscontaining all terms used in this study were formulatedas follows:Model 1MCA k 1 QNMm α 1QNMn α 2=• •wherez = β 1 SPEED + β 2 LWm + β 3 LWn + β 4 LNmModel 2QMm α 3QMn α 4• QPED α 5• • EXP z+ β 5 LNn + β 6 NL + β 7 SHDW + β 8 LU + eMCA k 2 Qmajor δ δ1 2EXPλ 1SHD + e=Qminor( )(1)(2)where MCA is motorcycle crashes per year. Descriptionsof all the explanatory variables are presentedHARNEN, RADIN UMAR, WONG & WAN HASHIM 31

in table 1. The k 1 , k 2 , α 1 , α 2 , α 3 , α 4 , α 5 , β 1 , β 2 ,β 3 , β 4 , β 5 , β 6 , β 7 , β 8 , δ 1 , δ 2 , and λ 1 are theparameters to be estimated and the (e) term is theerror representing the residual difference betweenthe actual and predicted models.Using a logarithmic transformation, the loglinearversion of the model is:Model 1Ln( MCA) = Ln( k) + α 1 Ln( QNMm) + α 2 Ln( QNMm)+ α 3 Ln( QMm) + α 4 Ln( QMn) + α 5 Ln( QPED)+ β 1 ( SPEED) + β 2 ( LWm) + β 3 ( LWn) + β 4 ( LNm)+ β 5 ( LNn) + β 6 ( NL) + β 7 ( SHDW) + β 8 ( LU) + e (3)Model 2Ln( MCA) = Ln( k) + δ 1 Ln( Qmajor)+ δ 2 Ln( Qminor) + λ 1 ( SHD) + e(4)To allow direct interpretation of the parameter estimatesproduced by GLIM 4, the flow functions inequations (3) and (4) need to be transformed using anatural logarithmic (Ln), while the others do not. Itshould be noted that the total four-year crash frequencieswere used to fit the models. However, byintroducing an offset variable in the fitting process,the final model would be able to estimate the numberof crashes per year. This approach has also beenimplemented in earlier studies on traffic crashes atintersections (Mountain et al. 1998) and motorcyclecrashes at intersections (Harnen et al. 2003a, 2003b).We based the model on the Poisson error structureand used the quasi-likelihood approach(McCullagh and Nelder 1989) to overcome the dispersionproblem. A loglinear cross-sectional modelwas employed with the link function specified as thelog (NAG 1994). This approach has been used inearlier studies on motorcycle crashes on highwaylinks (Radin 1996; Radin et al. 1995, 2000) and inour earlier analysis of motorcycle crashes at intersections(Harnen et al. 2003a, 2003b).Using the quasi-likelihood approach, the dispersionparameter was estimated from the mean deviance(scaled deviance over its degrees of freedom).This may result in a model where the scaled devianceis equal to its degrees of freedom. The finalmodel was based on the goodness-of-fit and significancetests carried out on the models such as thechange in scaled deviance from adding or removingthe terms, the ratio of scaled deviance to its degreesof freedom (mean deviance), and the 5% significancelevel of t-statistics of the parameter estimates.Both multivariate and univariate analyses wereconducted for Model 1, while only multivariateanalysis was undertaken for Model 2. We used multivariateanalysis to assess which of the variable(s)had the most effect on the probability of motorcyclecrashes. The univariate analysis was employed toobtain a complete picture of the effect of all explanatoryvariables on motorcycle crashes. It should benoted that only those variables found significant atthe 5% level in the univariate analysis were subsequentlyincluded in the multivariate analysis.RESULTSModel 1Table 2 presents the results of the univariate analysisfor Model 1. It can be seen that all terms, exceptQPED, LNn, and NL, were significant at the 5%level. The respective scaled deviance was equal to itscorresponding degrees of freedom, as the quasi-likelihoodapproach had been introduced in the fittingprocess. Because the terms QPED, LNn, and NLwere not significant at the 5% level, they were thenexcluded from any further analysis.The multivariate analysis (table 3) shows that allexplanatory variables were significant at the 5%level. The scaled deviance was equal to its degrees offreedom, changing from 15,022.0 to 39.0 with aloss of 11 degrees of freedom. The mean deviancechanged from 300.4 to 1.0.On the basis of the multivariate analysis, the finalmodel is:MCA = 0.002822 QNMm 0.3241 • QNMn 0.0835 •whereQMm 0.0683 • QMn 0.1296 • EXP z(5)z = 0.02602 SPEED – 0.0727 LWm – 0.0718 LWn– 0.01758 LNm – β 7 SHDW + β 8 LUand where MCA is motorcycle crashes per year, β 7 =0.0, 0.01755, and 0.02554 for SHDW = 1, 2, and 3,respectively, = 0.0 and 0.01591 for LU = 1 and 2,β 832 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 1 Description, Factor Levels, Coding System, and Basic Statistics of the Explanatory VariablesExplanatoryvariablesMODEL 1QNMmQNMnQMmQMnQPEDDescriptionNonmotorcycle flow on majorroad (nmpd)Nonmotorcycle flow on minorroad (nmpd)Motorcycle flow on major road(mpd)Motorcycle flow on minor road(mpd)Pedestrian flow (pedestrians/hour)FactorlevelsCoding systemin GLIM Min Max Mean MedianQNMm 14,527 50,529 31,389 32,354QNMn 2,133 20,129 11,276 11,129QMm 5,510 21,899 12,228 10,792QMn 1,752 4,771 3,183 3,142QPED 0 235 36 19SPEED Approach speed (km/hour) SPEED 53.00 68.00 59.57 59.50LWmLWnLNmLNnAverage lane width on majorroad (m)Average lane width on minorroad (m)Number of lanes on major road(lanes/traffic direction)Number of lanes on minor road(lanes/traffic direction)NL Number of legs 2 (1) 3-legged(2) 4-leggedSHDWAverage shoulder width onmajor and minor roadsLWm 3.30 4.00 3.58 3.60LWn 3.40 4.00 3.69 3.60LNm 2 5 2.6 2.0LNn 1 3 1.6 2.03 (1) SHDW = 0.00 m(2) 0.00 < SHDW< 1.00 m(3) SHDW > 1.00 mLU Land-use category 2 (1) Noncommercialarea(2) CommercialareaMODEL 2QmajorQmajorSHDTraffic flow on major road(vehicles/day)Traffic flow on minor road(vehicles/day)Average shoulder width onmajor and minor roads (m)Key: km = kilometers; m = meters; mpd = motorcycles per day; nmpd = nonmotorcycles per day.1 2 1.5 1.01 3 1.7 2.01 2 1.7 2.0Qmajor 20,043 72,428 43,617 42,258Qminor 4,504 24,900 14,459 14,293SHD 0 1.3 0.5 0.9respectively (table 1). Figure 4 shows the actual andpredicted motorcycle crashes.Model 2Table 4 presents the results of the multivariate analysisof Model 2. All terms were found to be significantat the 5% level. The scaled deviance was equalto its degrees of freedom, because the quasi-likelihoodapproach had also been introduced in the fittingprocess. The scaled deviance changed from854.8 to 47.0 with a loss of 3 degrees of freedomand the mean deviance changed from 17.1 to 1.0.HARNEN, RADIN UMAR, WONG & WAN HASHIM 33

TABLE 2 Univariate Analysis of Model 1ExplanatoryvariablesEstimatesStandarderrorsDegrees offreedomScaleddeviancet-statisticsSig. at0.05Constant –9.2260 0.3480 49 49 –26.55 YesQNMm 0.9835 0.0334 29.42 YesConstant –1.2210 0.2160 49 49 –5.64 YesQNMn 0.2490 0.0243 10.26 YesConstant –0.7520 0.2580 49 49 –2.92 YesQMm 0.1943 0.0288 6.76 YesConstant –2.0910 0.3790 49 49 –5.51 YesQMn 0.3877 0.0478 8.12 YesConstant 0.8636 0.0748 49 49 11.54 YesQPED 0.0357 0.0237 1.51 NoConstant –3.6760 0.1090 49 49 –33.63 YesSPEED 0.0771 0.0018 42.83 YesConstant 3.2900 1.1800 49 49 2.79 YesLWm –0.6510 0.3290 –1.98 YesConstant 3.0200 1.0500 49 49 2.88 YesLWn –0.5800 0.2950 –1.97 YesConstant 1.1960 0.1260 49 49 9.47 YesLNm –0.1023 0.0519 –1.97 YesConstant 1.0780 0.1200 49 49 8.97 YesLNn –0.0744 0.0697 –1.07 NoConstant 1.0020 0.1280 49 49 7.83 YesNL (2) –0.0294 0.0826 –0.36 NoConstant 1.0578 0.0524 48 48 20.17 YesSHDW (2) –0.1812 0.0856 –2.12 YesSHDW (3) –0.2750 0.1190 –2.32 YesConstant 0.8316 0.0752 49 49 11.05 YesLU (2) 0.1774 0.0885 2.01 YesNote: Estimates for factors (2) and (3) are the differences compared with the reference level (1).The final model developed in this analysis was:MCA = 0.0004693 Qmajor 0.5948 • Qminor 0.2411 •estimates for each of the corresponding variablesthat are identical.EXP– 0.0589 SHD(6)DISCUSSIONTables 2, 3, and 4 show that the variables have aconsistent effect on motorcycle crashes. This is indicatedby the sign (plus or minus) of the parameterModel 1The final Model 1 reveals that the number ofmotorcycle crashes per year is proportional to the34 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 3 Multivariate Analysis of Model 1ExplanatoryvariablesEstimatesStandarderrorsDegrees offreedomScaleddeviancet-statisticsSig. at0.05MeandevianceConstant –5.8700 0.4580 50 15,022.0 –12.81 Yes 300.4QNMm 0.3241 0.0297 49 748.6 10.91 Yes 15.3QNMn 0.0835 0.0183 48 483.6 4.57 Yes 10.1QMm 0.0683 0.0188 47 241.5 3.64 Yes 5.1QMn 0.1296 0.0230 46 142.8 5.63 Yes 3.1SPEED 0.0260 0.0033 45 75.1 7.79 Yes 1.7LWm –0.0727 0.0320 44 70.7 –2.27 Yes 1.6LWn –0.0718 0.0305 43 69.1 –2.35 Yes 1.6LNm –0.0176 0.0044 42 55.0 –3.97 Yes 1.3SHDW (2) –0.0176 0.0069 40 47.5 –2.55 Yes 1.2SHDW (3) –0.0255 0.0100 40 47.5 –2.56 Yes 1.2LU (2) 0.0159 0.0055 39 39.0 2.91 Yes 1.0Note: Estimates for factors (2) and (3) are the differences compared with the reference level (1).FIGURE 4 Actual and Modeled Motorcycle Crashes:1997–2000 (Model 1)Number of motorcycle crashes2018161412108642ActualModeled00 10 20 30 40 50 60Number of intersections(out of a total of 51)traffic flow entering the intersection. The estimatesof QNMm, QNMn, QMm, and QMn indicate thatan increase in nonmotorcycle and motorcycle flowson major and minor roads is associated with moremotorcycle crashes (figure 5). For instance, doublingnonmotorcycle flow on a major road(QNMm) is expected to cause an increase of about25% in motorcycle crashes. If all traffic entering theintersection is doubled, an increase of about 45% inmotorcycle crashes would result. We also found thatnonmotorcycle flows on major roads (QNMm) wasthe most important variable for the probability ofmotorcycle crashes. The results support the findingsof earlier studies on traffic crashes at intersections(Summersgill 1991; Mountain et al. 1998; Rodriguezand Sayed 1999; Vogt and Bared 1998; Vogt1999; Bauer and Harwood 2000).The SPEED estimate shows that an increase inapproach speed is associated with a rise in motorcyclecrashes. For instance, if the approach speedgoes up by 10 kilometers per hour, 30% moremotorcycle crashes can be expected. Our findingssupport earlier studies on the relationship of trafficspeed to crashes (Griebe and Nielsen 1996; Vogtand Bared 1998; Bauer and Harwood 2000; Lynamet al. 2001; USDOT 2002; Taylor et al. 2002).The estimates of LWm and LWn imply that awider lane is associated with a reduction in motorcyclecrashes. For instance, widening the lane onmajor and minor roads by 0.50 meters is expectedto reduce motorcycle crashes by some 3.6% and3.5%, respectively. This result is in line with thefinding reported in an earlier study on traffic crashesat intersections (Bauer and Harwood 2000).Meanwhile, the estimate of LNm indicates thatan increase in the number of lanes on a major road isassociated with a reduction in motorcycle crashes.However, the effect of this variable is marginal(1.7%). The result seems to be in line with the findingreported by Bauer and Harwood (2000). Thisreduction was probably the result of the presence ofHARNEN, RADIN UMAR, WONG & WAN HASHIM 35

TABLE 4 Multivariate Analysis of Model 2ExplanatoryvariablesEstimatesStandarderrorsDegrees offreedomScaleddeviancet-statisticsSig. at0.05MeandevianceConstant –7.6640 0.4650 50 854.8 –16.49 Yes 17.1Qmajor 0.5948 0.0707 49 65.4 8.41 Yes 1.3Qminor 0.2411 0.0640 48 52.1 3.77 Yes 1.1SHD –0.0589 0.0261 47 47.0 –2.25 Yes 1.0FIGURE 5 Effects of Traffic Flow on MotorcycleCrashes: Model 1Motorcycle crash increment (%)50403020Total flow10QMnQNMnQMm00 20 40 60 80 100 120Traffic flow increment (%)QNMman exclusive right turn lane on the major road. Ofthe 51 intersections we studied, 48 had an exclusiveright turn lane on each major road approach. Thepresence of such lanes may reduce rear-end crashesfor motorcycles. It should be mentioned that anexclusive turning lane was counted as a lane in ourmeasurements of LNm. Earlier studies confirmed thebenefit provided by such lanes for crash reduction atintersections (Kulmala 1992; Vogt 1999; Bauer andHarwood 2000) and at links (Tarko et al. 1999).However, for a better explanation, a separate modelshould be developed to explain the effects of anexclusive left, exclusive right, and short turning laneson all types of motorcycle crashes at intersections.The SHDW estimates indicate that a wider pavedshoulder is associated with fewer motorcyclecrashes. The result seems to be in line with the findingreported by Bauer and Harwood (2000). Forinstance, 25% more motorcycle crashes occur atintersections without a shoulder than at intersectionswith a shoulder wider than 1.0 meters. Whenwe compare motorcycle crashes at intersectionswithout a shoulder with crashes where the shoulderwidth is between 0.0 meters and ≤1.0 meters, thedifference is smaller, only 1.7% more crashes occurwhen there is no shoulder. This finding seems reasonablebecause motorcyclists use the availableshoulder width when approaching an intersection,and the rates of rear-end and sideswipe crash typesbetween motorcycles on the shoulder and othervehicles on the adjacent lane should be lower if theshoulder is wider. This situation is common in countrieslike Malaysia with a high population of motorcycles.However, a better explanation can beprovided, and a separate model was developed toexplain the effect of shoulder width on all types ofmotorcycle crashes at intersections.The estimate of LU shows that signalized intersectionslocated within commercial areas are associatedwith increased motorcycle crashes. The resultconfirms the findings of an earlier study on trafficcrashes at four-legged signalized intersections (Wangand Ieda 1997). However, the difference in the estimationof motorcycle crashes between commercialand noncommercial areas is marginal (1.6%). Asexplained earlier, this study includes only thoseintersections located within commercial areas havingno access road to the adjacent land use within50 meters of the intersection stop lines. As such, thenumber of conflicts between vehicles entering orleaving the intersection and vehicles turning into orout of the adjacent land use may be reduced, hencefewer crashes. The effect of access control or thenumber of accesses on traffic crashes has also beenreported in earlier studies (Vogt 1999; Bauer andHarwood 2000).Model 2Model 2 results verify the contribution of traffic flow,both on major roads (Qmajor) and minor roads(Qminor), to motorcycle crashes. The estimates ofthe variables show that an increase in traffic flow on36 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

major and minor roads is associated with a greaternumber of motorcycle crashes, and an increase inshoulder width (SHD) is associated with a reductionin these crashes. For example, widening the shoulderby 1.0 meters is expected to reduce the number ofmotorcycle crashes by about 6%. In this model, theeffect of shoulder width on motorcycle crashes can bedirectly quantified when the width is changed, andthis is one of the main differences between Model 1and Model 2.As described earlier, design curves relating majorandminor-road flows for different shoulder widthscan be developed based on Model 2 (figure 6). Asdiscussed, wider shoulders at intersections offerhigher levels of safety to motorcyclists approachingthe junction. Based on the relationships among thevariables developed based on Models 1 and 2,future work includes carrying out an indepth analysisof whether intersection treatments that have nonexclusivemotorcycle lane facilities could reducemotorcycle crashes.CONCLUSIONSFIGURE 6 Relationship of Major- and Minor-RoadFlows with Differing Shoulder Widths(based on Model 2)12,00010,0008,0006,0004,0002,000Minor road flow(vehicles/day, both directions)SHDW = 2.5 mSHDW = 2.0 mSHDW = 1.5 mSHDW = 1.0 mSHDW = 0.0 m00 5,000 10,000 15,000 20,000 25,000 30,000 35,000Major road flow(vehicles/day, both directions)This paper presents motorcycle crash predictionmodels for signalized intersections on urban roadsin Malaysia. The models reveal that traffic flow,approach speed, intersection geometry, and land useare significant factors in explaining motorcyclecrashes at signalized intersections. The number ofcrashes is proportional to the level of traffic enteringthe intersections. An increase in motorcycle crashesis associated with a larger total vehicle flow onmajor and minor roads. Nonmotorcycle flows onmajor roads had the most effect on the likelihood ofmotorcycle crashes.An increase in approach speed is associated withmore motorcycle crashes, while wider lanes, agreater number of lanes, and wider shoulders bringa reduction in these crashes. Furthermore, moremotorcycle crashes occur at signalized intersectionslocated within commercial areas than at intersectionslocated outside of commercial areas.The models developed in this study presentinformation to aid traffic engineers in deciding theappropriate level of intervention for intersectiontreatment with respect to motorcycle crashes.Using our models, design parameters for intersectionsmay be changed to achieve appropriate safetylevels. Decisions on whether special treatment tominimize motorcycle conflicts is needed at intersectionscan be objectively carried out based on themodels. However, the models might only be validfor a typical traffic environment in developingcountries like Malaysia, where the proportion ofmotorcycles is 20% to 40% of all vehicles at signalizedintersections.For design options, further investigation of therole of parameters of traffic flow by time periods(hourly, peak hour, peak periods) and categorizingthe models by time period(s) is suggested, and theneed for further categorization of model structureby different intersection geometric configurations(e.g., intersections with and without exclusivemotorcycle lanes) is also advised.ACKNOWLEDGMENTSMotorcycle crashes = 1 crash per yearThis paper reports findings of part of a study conductedfor the Intensified Research Priority Area(IRPA) project, Development of Design Criteria andStandards for Malaysian Motorcycle Lanes. Wegratefully acknowledge the financial support fromthe Ministry of Science, Technology and EnvironmentMalaysia. The authors would like to thank theRoyal Malaysian Police and the Highway PlanningUnit, Ministry of Works, Malaysia, for providingthe data.HARNEN, RADIN UMAR, WONG & WAN HASHIM 37

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Effects of Extreme Values on Price Indexes:The Case of the Air Travel Price IndexJANICE LENTBureau of Transportation StatisticsResearch and Innovative TechnologyAdministrationU.S. Department of Transportation400 7th Street, SWWashington, DC 20590Email: janice.lent@dot.govABSTRACTThis paper examines the effects of extreme pricevalues on the Fisher and Törnqvist index formulas.Using a simple model, we first consider the impactof outliers on the unweighted arithmetic, harmonic,and geometric means of a collection of values. Then,under the same model, we investigate the effect of asingle extremely high or low price on the price indexformulas (weighted means). Further investigationusing Taylor series approximations leads to somegeneral conclusions regarding the relative robustnessof the Fisher and Törnqvist indexes. These areillustrated with empirical results based on airfaredata from the U.S. Department of Transportation’sOrigin and Destination Survey.INTRODUCTIONMany economists have come to favor the “superlative”Fisher and Törnqvist price indexes over themore traditional Laspeyres formula (see, e.g., Diewert1976; Aizcorbe and Jackman 1993). The U.S. Bureauof Labor Statistics recently began publishing a newprice index series targeting the Törnqvist formula.The choice between the Fisher and Törnqvist formulasmay be based on a variety of factors, includingother price index formulas currently in use by theKEYWORDS: Price index, extreme value, Taylor series.JEL Categories: C43, C13, E31.41

organization producing the index and the relativesensitivity of the two formulas to extreme values. Inthis study, we compare the Fisher and Törnqvist formulaswith respect to the latter criterion—sensitivityto extreme values.Extreme-valued price ratios often occur as a resultof deep discounts or “free” promotional goods orservices. Such extreme-valued ratios can be eitherlarge or small, depending on whether the discountedprice appears in the numerator or denominator ofthe price ratio. Less often, extremely high pricesappear with converse effects. The Laspeyres formulais sometimes criticized as sensitive to extreme values,because it is based on an arithmetic mean of theprice ratios. We will see, however, that such sensitivitydepends on the direction of the outlying value(high or low), as well as on the weights used in theselected mean.In the next section, we consider the effect of anextreme value on the unweighted arithmetic, harmonic,and geometric means. The third sectioncontains a discussion of the corresponding effectson the Fisher and Törnqvist index formulas underdiffering assumptions regarding the correlationbetween the expenditure-share weights and theprices. This correlation is related to the elasticity ofsubstitution (i.e., the extent to which consumersshift their purchases toward lower priced itemswhen relative prices change).The fourth section presents an empirical example:the case of air travel index estimates computedusing data from the Passenger Origin andDestination Survey collected by the Bureau ofTransportation Statistics. The extreme-valued priceratios in this application resulted from a change indata-collection procedures and are in this sense artificial.They do, however, provide an opportunity tocompare the performances of the different indexformulas under the conditions represented by theapplication. We summarize our conclusions in thefinal section.EFFECTS OF EXTREME VALUES ONUNWEIGHTED MEANSThe following simple model shows the effects of anextreme value on three types of unweighted means.Let x 1 , ..., x n be a collection of nonnegative values,where x i = µ for i = 1, ..., n – 1, while x n = yµ forsome factor y > 0; that is, x n is an outlier in the collection.We define the unweighted arithmetic, harmonic,and geometric means, respectively, as follows:andAHGn1= -- x ,n ∑ ii = 1⎛ n ⎞ – 11--1= ⎜ -- ⎟ ,⎜n∑ xi⎟⎝ i = 1 ⎠=n1 ⁄ n∏ x ii = 1.For M ∈ { A, H,G}, letMf M = ---- .µThenn – 1 + ynf A = -------------------- , f , andn H = -------------------------n – 1 – y – 1f G=y 1 ⁄ n.We first consider the rate at which the variousmeans approach µ as n approaches infinity. Forfixed y, we have1 – f 1 – yA = -----------n=⎛O 1 ⎝n -- ⎞⎠as n → ∞. For the harmonic mean also,1 – f 1 – y – 1H = ----------------------------n – 1–y – 1⎝⎛ ⎠⎞=⎛O 1 ⎝n -- ⎞⎠as n → ∞, and, similarly,1 – f G = 1 – e=--1lnyn1 ⎛-- y O 1 ⎞– ln – ⎜----n ⎝n 2 ⎟⎠⎛O 1 n -- ⎞= ⎝ ⎠as n → ∞. Thus, as n becomes large, all three of themeans approach µ at approximately the same rate.Their behavior in the presence of an outlier differs,however, under various assumptions about the42 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 1 Limits and Orders of Magnitude for the Unweighted Mean Factorsy → y → 0Limit∞Order ofmagnitudeLimitOrder ofmagnitudef A ( y) ∞Ω( y) n – 1Oy ( )-----------nf G ( y) ∞Oy ( ( 1 ⁄ n)) 0 Ω( y ( 1 ⁄ n))f H ( y) 1-----------n – 1Ω( y) 0 Oy ( )outlier itself. If we suppose that n is fixed, we mayfollow derivations similar to those above to arriveat the results, which are summarized in table 1.The results shown in table 1 for f G (y) may leadus to conclude that price index formulas based onthe geometric mean are, overall, the most robustformulas available; at the very least, they representa sensible choice when both high and low outliers areexpected to occur. By contrast, while A is robust tolow outliers, it is sensitive to high outliers; similarly,H is robust to high outliers but sensitive to low ones.In most applications, however, price indexes are notcomputed as unweighted means. In the next section,we examine the effect of expenditure-share weightson the Laspeyres, Paasche, Fisher, and Törnqvistindexes, with special emphasis on the latter two.EFFECTS OF EXTREME VALUES ONPRICE INDEXESPrice Index FormulasWe begin by presenting several population index formulas.The Laspeyres index measuring price changebetween time periods 1 and 2 is defined asNNΣL j = 1qj1p j2⎛p j2 ⎞= -------------------------- = w ,Nj1 ⎜------∑⎝p ⎟j1 ⎠Σ j = 1qj1 p j1j = 1where p jt denotes the price of item j at timet ∈ {, 12}, q jt denotes the quantity of item j purchasedat time t,Nw jt = p jt q jt ⁄ Σ k = 1 p kt q kt ,and N denotes the number of items in the targetpopulation. The weight w jt is the expenditure sharefor item j in period t; the price ratios p j2 /p j1 are oftencalled price relatives. Clearly L is the arithmeticmean of the price relatives with weights representingfirst period expenditure shares. The Paasche index isa harmonic mean of the price ratios, with secondperiod expenditure-share weights:PNΣ j = 1qj2p-------------------------- j2Σ Nj = 1 q j2 p j11= = ------------------------------------------------- .NΣ j = 1wj2⎛p j2 ⁄ p j1⎞ – 1⎝ ⎠The Fisher index is simply defined as F = LP,while the Törnqvist is a geometric mean of the priceratios with weights representing the averages of theperiod 1 and period 2 expenditure shares, shown asT=N∏j = 1p------ j2p j1⎛ ⎞⎜ ⎟⎝ ⎠wj , 12 ,where w j,1,2 = (w j1 + w j2 ) /2.,Extreme Values and the Elasticityof SubstitutionTo examine the effects of an outlier on the indexesdescribed above, suppose we have a collection of nitems priced in time periods 1 and 2. Suppose furtherthat for j = 1, ..., n we have p j1 = q j1 = 1 and that forj = 1, ..., n – 1 we also have p j2 = 1, while p n2 = y(i.e., we assume for simplicity that the µ above is1.) For t ∈ {, 12}, let x jt = p jt q jt , the expenditurelevel for item j in period t. We wish to allow thequantity of an item purchased to vary in responseto price change and an assumed elasticity level.When p j2 = p j1 , we assume that q j2 = q j1 . Otherwise,let⎛p j2 ⎞ – τq j2 = q j1 ⎜------p⎟⎝ j1 ⎠where 0 ≤ τ ≤ 1 , and τ is assumed constant. Then⎛p j2 ⎞ 1 – τx j2 = x j1 ⎜------.⎝p⎟j1 ⎠LENT 43

We define the elasticity τ in this way, because τprovides a convenient means of examining the effectsof extreme values under conditions of high and lowelasticity, defined relatively. Note that higher valuesof τ indicate less impact of price change (representedby the price ratios) on second period item-levelexpenditure levels.For j = 1, ..., n – 1, we have q j2 = q j1 = 1; and= y – τ .q n2The resulting first and second period expenditureshareweights are as follows:1w j1 = --,j = 1, ..., n ;n1w j2 = ----------------------------- ,;n – 1 + y j = 1, ..., n – 11 – τandy 1 – τw n2 = ----------------------------- .n – 1 + y 1 – τThe “average weights” used in the Törnqvist indexare1w j, 1, 2-- 1 ,2 n -- 1⎜⎛ + -----------------------------n – 1 y 1 – τ ⎟⎞ =, j = 1, ..., n – 1⎝ + ⎠and1⎛w n, 12 ,-- 1 .2 n -- y 1 – τ+ ----------------------------- ⎞= ⎜n – 1 y 1 – τ ⎟⎝ + ⎠Note that when τ is small (low or zero elasticity)and y is large,w n1 < w n, 12 ,, (1)so the Laspeyres index gives less weight to high outliersthan does the Törnqvist index. Similarly, whenτ and y are both small,w n2 < w n, 12 ,, (2)indicating that the Paasche index gives less weightto low outliers than the Törnqvist. Under conditionsof low elasticity, we therefore observe the followingphenomena: although the Laspeyres index, based onthe arithmetic mean, is sensitive to high outliers, itassigns them weights that are low relative to theTörnqvist weights, while the Paasche index, a harmonicmean, assigns lower weights to low outliers.The weights in the Laspeyres and Paasche indexescan therefore be expected to compensate, at leastpartially, for the sensitivity of the arithmetic and harmonicmeans to high and low outliers, respectively.Under this simple model, the values of theLaspeyres, Paasche, Fisher, and Törnqvist indexesare as follows:n – 1 + yLny ( , ) = -------------------- ;nPnyτ ( , , )n – 1 y 1 – τ= -----------------------------+;n– 1 + y – τand⎛--------------------n – 1 + y⎞ ⎛n – 1 + y 1 – τ ⎞ 1 ⁄ 2;⎝ n ⎠⎜-----------------------------⎝ n – 1 + y – τ ⎟⎠1⎛Tnyτ ( , , ) -- 1 .2 n -- + ----------------------------- ⎞= exp ⎜n – 1 y 1 – τ ⎟lny⎝ + ⎠Both the Fisher and Törnqvist indexes are knownas superlative indexes, because economic theorysuggests that they approximate a true cost of livingindex under relatively weak assumptions regardingeconomic conditions (Diewert 1987). (In the applicationconsidered in the next section, these indexesshould be viewed as cost of flying indexes ratherthan as cost of living indexes.) We, therefore, focuson the relative robustness of F (n, y, τ ) and T (n, y,τ) under the assumptions τ = 1 and τ = 0 . Thevalue τ = 1 indicates that consumers shift theirpurchases toward items (or item categories) whoserelative prices have decreased between periods 1 and2, while τ close to zero represents the case of little orno change in buying behavior in response to pricechange.First consider the case τ = 1 , where a value of τrepresents the assumption that consumers alter thequantities of the items they purchase so as to maintainthe same level of expenditure on each item—asituation corresponding to a fairly high level of elasticity.In this case, we have, for fixed n and large y,whileFnyτ ( , , )Fny1 ( , , )==y 1 – τ⎛n – 1 + y⎞⎛ n⎜--------------------⎟ -------------------------⎞ 1 ⁄ 2⎜⎝ n ⎠⎝n – 1 + y – 1 ⎟⎠⎛ n – 1 + y ⎞ 1 ⁄ 2= ⎜-------------------------⎝n – 1 + y – 1 ⎟⎠⎛ y⎞≈ ⎜1+ --⎟⎝ n⎠1 ⁄ 2, (3)Tny1 ( , , ) = y 1 ⁄ n . (4)So, for reasonably large n, T is more robust than Fin the presence of high outliers. For the case of lowoutliers, we have44 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

andFny1 ( , , )(5)(6)for fixed n as y approaches 0. Under the simplemodel, we may therefore conclude that, with regardto robustness, conditions of high elasticity favor theTörnqvist index over the Fisher.With⎛ n – 1 + y ⎞ 1⁄2= ⎜-------------------------⎝n – 1 + y – 1 ⎟⎠= O⎛y 1⁄2 ⎞⎝ ⎠Tny1 ( , , ) = Ω⎛y 1 ⁄ n ⎞⎝ ⎠τ = 0, we have⎛n – 1 + y⎞Fny0 ( , , ) = ⎜--------------------⎟ ,⎝ n ⎠and1Tny0 ( , , ).2-- 1 n -- y= exp ⎛ + n -------------------- ⎞lny⎝ – 1 + y⎠Note that F (n, y, 0) = L (n, y, 0) = P (n, y, 0). Forfixed n and large y,Fny0 ( , , ) ≈y1 + -- , (7)nwhileTny0 ( , , ) ≈ y n + 1( ) ⁄ 2n. (8)As a rough rule of thumb, the above approximationssuggest that T is likely to outperform Fwhenever outliers are as large as n 2 . The relativerobustness of T and F thus depends on the relativevalues of y and n, which may, in turn, depend on theaggregation level being considered. Equations (4)and (8) also indicate that, for large values of n, T ismuch more robust to high outliers under high elasticitythan it is under low elasticity. For low outliers,however, the elasticity assumption has less impacton T. With n fixed and y small, we have⎛Fny0 ( , , ) Ω n – 1 ⎞= ⎜-----------⎟, (9)⎝ n ⎠andTny0 ( , , ) = O⎛y 1 ⁄ 2n ⎞ , (10)⎝ ⎠revealing that, under conditions of low elasticity, Tis more sensitive to low outliers than F. Equations(5), (6), (9), and (10) suggest that T is somewhatmore robust to low outliers for τ = 0 than forτ = 1, while F is much more robust.The above results lead us to conclude that, underconditions of low elasticity, the Fisher index mayoften be more robust to outliers than the Törnqvist:the Fisher is more robust to low outliers and, whenn is sufficiently large relative to any price ratios inthe dataset, the Fisher is also more robust to highoutliers. Conditions of higher elasticity ( τ close to1) render both indexes more robust to extremelyhigh values. Under conditions of high elasticity, theTörnqvist is preferable to the Fisher, because it isless sensitive to both high and low outliers.The numerical examples shown in appendix Aillustrate these conclusions. Tables A1 and A2 givevalues of the Fisher and Törnqvist indexes underthe single outlier scenario described above. (Notethat these are not random values produced by aMonte Carlo simulation but simply the values ofthe functions F (n, y, τ ) and T (n, y, τ ) for the givenparameters.) Table A1 gives index values under theassumption that τ = 1 (high elasticity). Under thisassumption, the Törnqvist is clearly more robustthan the Fisher to both high and low outliers.Table A2 shows values of the indexes under theassumption that τ = 0 . The bold numbers in thistable highlight points at which y becomes largeenough, relative to n, to render the Törnqvist indexbetter than the Fisher for approximating the meanµ = 1 in the presence of a high outlier. As expected,the turning points occur as y approaches n 2 . Theexamples in table A2 also illustrate that, under lowelasticity, both indexes are more sensitive to highoutliers and less sensitive to low outliers than theyare under high elasticity.Taylor Series ResultsThe single outlier model employed in the previoussubsections does not, of course, account for the datacomplexity often encountered in practical applications.Here, we look beyond the single outlier modelto examine Taylor series expansions that shed furtherlight on the relative robustness of the Fisher andTörnqvist indexes under the general assumption oflow elasticity. Following and expanding on thedevelopment of Lent and Dorfman (2004a), weassume that the price indexes are computed from aLENT 45

collection of expenditure share weights and subindexesI g , which here take the place of the priceratios p j2 /p j1 in the previous subsection. Each Î g isan aggregate of the ratios p j2 /p j1 for all items j in aparticular item category g. In practice, the standardformulas are often applied in this two-step fashion.The categories into which we divide the items maybe defined according to item characteristics, geographicarea of purchase, or both.We begin by defining some notation. Fort ∈ {, 12}and for each item category g, letw gtΣ j ∈gp jt q= ------------------------------- jt,Σ g Σ j ∈gp jt q jtand letµ t = ∑w gt I g,2σ t = ∑w gt ( I g – µ ) 2 ,ggandγ t = w gt ( I g – µ ) 3 .∑gNext, with w g defined as the Törnqvist weights,w gw g1 + w= ----------------------- g2,2letµ = ∑w g I g, σ 2 = ∑w g ( I g – µ ) 2 ,ggandγ = w gt ( I g – µ ) 3 .∑gWe expand each of the superlative indexes aboutthe point at which all of the sub-indexes I g equal themean µ . The relevant partial derivatives are given inappendix B. From the general form of the thirdorderapproximation given by Lent and Dorfman(2004a) for a geometric mean, we have the followingapproximation of the Törnqvist index:T I=∏gI gw g≈ µ – -----σ2 + --------γ. (11)2µ 3µ 2The second-order approximation for the Fisherindex is⎛ Σ g w g1 I g ⎞ 1 ⁄ 2F I = ⎜-----------------------⎟⎝Σ g w g2 ⁄ I g ⎠µ σ 22≈ – -----(12)2µThus, to the second order, we haveσ 2 2– σT I – F 2I ≈ ----------------2µ1= ----- . (13)4µ ∑⎛w g2 – w g1⎞⎛ Ig – µ ⎞ 2⎝ ⎠⎝⎠gConsider the relative values of w g1 and w g2 in thepresence of high outliers among the I g and high correlationbetween the I g and the w g2 (the case of lowelasticity). Under these conditions, we are likely tohave w g2 > w g1 for large values of ( I g – µ)2and thusT I – F I > 0. Similarly, in the presence of low outliers,we are likely to have w g2 < w g1 for large values of( I g µ) , resulting in negative values of T I – F I .Thus, the approximation shown in equation (13)indicates that, under conditions of low elasticity, theFisher index may be more robust than the Törnqvistto both high and low outliers.– 2 τAN EMPIRICAL EXAMPLEFor the air travel price index series, the apparentelasticity of substitution is low—in some cases, evennegative. The series, therefore, exemplify only thebehavior of the different indexes under conditions oflow elasticity ( close to 0). Note that the elasticityreflected in the data, rather than the actual elasticity,is the quantity that affects the performance of theindexes; Dorfman et al. (1999) showed that the elasticityreflected in sample survey data need not alwaysequal the true population elasticity.The air travel price index series shown in figures1 through 6 are based on data from the Bureau ofTransportation Statistics’ quarterly Origin and Destination(O&D) Survey. The sample for the O&DSurvey comprises about 10% of all passenger itinerarieshaving some U.S. component (i.e., itinerariesthat include at least one flight arriving at or departingfrom a U.S. airport) and includes about 6 to 7million itineraries per quarter. Data items collectedinclude trip route, class of service (e.g., coach, firstclass), and transaction fare including taxes. Notethat the scales differ across figures, so comparisonsacross figures are distorted in some cases.When goods and services are sampled for thepurpose of estimating a price index, the sampleitems generally remain in the sample over anextended time period (e.g., two years) unless theyare taken off the market by the retailer. The stable46 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

sample allows comparison of prices across time foridentical items. Ratios of prices in different timeperiods for individual items are the building blocksof the traditional price index estimators.In the O&D Survey, however, the sampling isperformed independently for each reference quarter.Since the itineraries selected in a given quarter maynot match those selected for a previous or subsequentquarter, we developed and tested a two-stage processfor matching categories of itineraries across quartersand comparing average prices within categoriesacross time. The ratio of average prices for differenttime periods is called a unit value index. These subindexesare then aggregated by the Fisher, Törnqvist,and other index formulas. The index series arebased only on data from sample itineraries flown ondomestic carriers and are chained quarterly. 1 Lentand Dorfman (2004b) provide a more detaileddescription of the index estimation methodology.The figures show the Laspeyres, Paasche, Fisher,and Törnqvist index series for various classes ofservice and for all classes combined. Note that, in allof the figures, the Paasche series runs either slightlybelow the Laspeyres series or even (for businessclass service) above the Laspeyres, indicating low ornegative elasticity of substitution. Lent and Dorfman(2004a) describe a method of estimating the elasticityof substitution; elasticity estimates computed by theirmethod, measuring elasticity of substitution betweenunit value categories as described above, run closeto 0 for these data. Although air travel passengersreadily substitute one carrier for another in responseto fare changes, little substitution across trip origin/destination pairs occurs. Since origin/destinationpairs far outnumber carriers, this substitution behaviorleads to low overall estimates of elasticity ofsubstitution between the unit value categories.In examining figures 1 through 6, it is importantto note that the Class of Service variable in theO&D Survey was redefined and standardized in1997–98. (Formerly, air carriers had used a variety1 Price index chaining is done by estimating long-termprice changes as products of shorter term changes (links).Quarterly chaining can lead to “chain drift,” as seen inthe Laspeyres and Paasche series in the figures in this section.For more information on chain drift in the airfareindexes, see Lent 2003.of service classifications in reporting this information,so the variable values had to be recoded bythe Bureau of Transportation Statistics.) We thereforeexpect some unusual data values to affect theindex series during this period; indeed, many of theseries display a visible break between the fourthquarter of 1997 and the first quarter of 1998.These breaks may be exacerbated, because a lowerpercentage of the O&D Survey observations were“matched” across time during 1997–98 (see Lentand Dorfman 2004b for a description of the acrosstimematching method), resulting in lower thanusual effective sample sizes.Figures 1 and 2 show the series for all classes ofservice combined and for restricted coach class (byfar the largest class), respectively. The series in figure2 behave in typical fashion: the Laspeyres series runsjust above the others, displaying a slight upwarddrift, while the Paasche shows a similar downwarddrift, and the two superlative series run betweenthem, closely tracking each other. This type ofbehavior results from the large number of observationsand because the 1997–98 break has relativelylittle impact on these series. Figure 1 is similar tofigure 2, except for the noticeably larger effect of the1997–98 change, which lifts the Törnqvist seriesslightly above the others. Recall that, under conditionsof low elasticity, the Törnqvist index is oftenmore sensitive to outliers than the Fisher.Index series for other classes of service (categoriescomprising fewer observations) are shown in figures3 through 6. For the unrestricted first andrestricted first class indexes (figures 3 and 4), theLaspeyres series runs very slightly above thePaasche, indicating low but positive elasticity. Forthe unrestricted first class series, the 1997–98 breaksends the Törnqvist above the other series, whilethe Törnqvist for restricted first class is “bumpeddown” and runs well below the others for 1998and subsequent years. In both cases, the Törnqvistcontinues to roughly parallel the Fisher after thebreak, indicating that unusual data values generatedthe level shifts. Note also that the Törnqvist’supward shift for unrestricted first class is noticeablyless severe than its downward shift for restrictedfirst class, perhaps due to its greater robustness tohigh outliers than to low ones.LENT 47

FIGURE 1 All Classes of Service: Quarterly Chained Preliminary Series1201101009080FIGURE 2 Restricted Coach Class: Quarterly Chained Preliminary Series1201101009080Index: 95Q1 = 100Index: 95Q1 = 10095Q195Q295Q395Q496Q196Q295Q195Q295Q395Q4The business class index series (figures 5 and 6)display the relatively rare phenomenon of negativeelasticity. The Paasche series runs above theLaspeyres, indicating that consumers are shiftingtheir purchases toward higher priced services asrelative prices change. It is important to note thatsample survey data may not always reflect true populationelasticity; in this case, the class-of-servicecategories are coarsely defined, and many differenttypes of restrictions may apply to tickets in therestricted categories. (Restrictions may include, forexample, a requirement of advance ticket purchaseor, in the case of roundtrip itineraries, a Friday or96Q396Q497Q197Q297Q397Q498Q198Q298Q398Q499Q199Q299Q399Q400Q100Q200Q300Q4Laspeyres Paasche Fisher Törnqvist96Q196Q296Q396Q497Q197Q297Q397Q498Q198Q298Q3Laspeyres Paasche Fisher Törnqvist98Q499Q199Q299Q399Q400Q100Q200Q300Q4Saturday night stay at the destination.) Elasticityestimates based on these data reflect substitutionbetween these categories but not within them (forthe same route and carrier) and may therefore suffera downward bias.On the other hand, since business class service istypically paid for by a third party (i.e., the passenger’semployer), very low elasticity is expected. Somebusiness class passengers may even choose higherpriced tickets assuming that “you get what you payfor,” and such behavior could also explain the negativeelasticity indicated. Under negative elasticity,quantities purchased are positively correlated with48 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

FIGURE 3 Unrestricted First Class: Quarterly Chained Preliminary Series1501401301201101009080Index: 95Q1 = 10095Q195Q295Q395Q496Q1FIGURE 4 Restricted First Class: Quarterly Chained Preliminary SeriesIndex: 95Q1 = 10013012011010090807060504095Q195Q295Q395Q4price change, and this correlation may cause expenditureshares to increase dramatically when pricesincrease. The Törnqvist index, whose weights areaverage expenditure shares, therefore assigns largeweights to some high price ratios. Apart from thenegative elasticity, the movements of the businessclass series appear similar to that of the first classseries, that is, the Törnqvist index is shifted up ordown during the 1997–98 period, while the otherseries are less affected by the unusual values. TableA3 in appendix A shows unweighted percentiles ofthe distributions of the unit value indexes for thefirst class and business class categories over the96Q296Q396Q497Q197Q297Q397Q498Q198Q298Q398Q499Q199Q299Q399Q400Q100Q200Q300Q4Laspeyres Paasche Fisher Törnqvist96Q196Q296Q396Q497Q197Q297Q397Q498Q198Q298Q3Laspeyres Paasche Fisher Törnqvistcrucial period. The outliers are clearly sufficient innumber and severity to impact the tails of the sampledistributions.CONCLUSIONS98Q499Q199Q299Q399Q400Q100Q200Q300Q4Practitioners may often consider robustness to outliersan important criterion in selecting a priceindex formula, especially for item categories suchas airfares, in which extreme prices may regularlyresult from frequent flyer awards and other pricediscriminatory discounts. Although price index formulasbased on different types of means inherit theLENT 49

FIGURE 5 Unrestricted Business Class: Quarterly Chained Preliminary Series1801701601501401301201101009080Index: 95Q1 = 100FIGURE 6 Restricted Business Class: Quarterly Chained Preliminary SeriesIndex: 95Q1 = 10012011010090807060504095Q195Q295Q395Q195Q295Q395Q496Q196Q2relative robustness of these means, the weightsapplied in price index calculation also play a crucialrole. This paper shows that, under conditions oflow elasticity of substitution, the high correlationbetween the weights and the price ratios may offsetthe sensitivity of the Laspeyres and Paasche indexes,making the Fisher a more attractive option than theTörnqvist. The choice between index formulas istherefore more complex than the mere selection ofan arithmetic, harmonic, or geometric mean. Itrequires information on the elasticity of substitutionreflected in the data as well as an estimate of themagnitude of outliers (high or low) that can beexpected.ACKNOWLEDGMENTComments from Alan Dorfman of the Bureau ofLabor Statistics resulted in significant improvementsto this paper.REFERENCES96Q396Q497Q197Q297Q397Q498Q198Q298Q398Q499Q199Q299Q399Q400Q100Q200Q300Q4Laspeyres Paasche Fisher Törnqvist95Q496Q196Q296Q396Q497Q197Q297Q397Q498Q198Q298Q3Laspeyres Paasche Fisher Törnqvist98Q499Q199Q299Q399Q400Q100Q200Q300Q4Aizcorbe, A. and P. Jackman. 1993. The Commodity SubstitutionEffect in CPI Data, 1982–1991: Anatomy of a PriceChange. Monthly Labor Review, December 1993:25–33.Washington, DC: U.S. Government Printing Office.Diewert, W.E. 1976. Exact and Superlative Index Numbers.Journal of Econometrics 4:115–145.50 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

_____. W.E. 1987. Index Numbers. The New Palgrave: A Dictionaryof Economics. Edited by J. Eatwell, M. Milate, and P.Newman. London, England: MacMillan.Diewert, W.E., R. Feenstra, and W. Alterman. 1999. InternationalTrade Price Indexes and Seasonal Commodities.Washington, DC: U.S. Department of Labor, Bureau ofLabor Statistics.Dorfman, A., S. Leaver, and J. Lent. 1999. Some Observationson Price Index Estimators. Proceedings of the Federal Conferenceon Survey Methodology, Arlington, VA, November1999.Lent, J. 2003. Chain Drift in Experimental Price Index Series forAir Travel, Proceedings of the 2003 Joint Statistical Meetings,Section on Survey Research Methods, CD. Alexandria, VA:American Statistical Association.Lent, J. and A. Dorfman. 2004a. Using a Weighted Average ofthe Jevons and Laspeyres Indexes to Approximate a SuperlativeIndex. Working paper._____. 2004b. A Transaction Price Index for Air Travel. Workingpaper.APPENDIX ATABLE A1 Values of Fisher and Törnqvist Indexes with a Single Outlierτ = 1, µ = 1Outlieryn = 5 n = 10 n = 15F T F T F T0.010 0.18 0.40 0.27 0.63 0.34 0.740.025 0.27 0.48 0.41 0.69 0.49 0.780.050 0.37 0.55 0.53 0.74 0.62 0.820.100 0.48 0.63 0.66 0.79 0.74 0.8610 1.65 1.58 1.37 1.26 1.26 1.2620 2.18 1.82 1.70 1.35 1.50 1.3550 3.28 2.19 2.43 1.48 2.06 1.4880 4.09 2.40 2.98 1.55 2.50 1.5590 4.33 2.46 3.14 1.57 2.63 1.57100 4.56 2.51 3.30 1.58 2.76 1.58TABLE A2 Values of Fisher and Törnqvist Indexes with a Single Outlierτ = 0, µ = 1n = 5 n = 10 n = 15Outliery F0T F T F T0.010 0.80 0.63 0.90 0.79 0.93 0.860.025 0.81 0.68 0.90 0.83 0.94 0.880.050 0.81 0.73 0.91 0.85 0.94 0.900.100 0.82 0.77 0.91 0.88 0.94 0.9210 2.80 2.87 1.90 2.06 1.60 1.7420 4.80 4.70 2.90 3.26 2.27 2.6750 10.80 9.05 5.90 6.38 4.27 5.2580 16.80 12.49 8.90 8.92 6.27 7.4790 18.80 13.52 9.90 9.68 6.93 8.14100 20.80 14.51 10.90 10.41 7.60 8.79Note: Bold indicates the points at which y becomes large enough, relative to n, to render the Törnqvist indexbetter than the Fisher for approximating the mean µ = 1 in the presence of a high outlier.LENT 51

TABLE A3 Unweighted Percentiles of the Unit Value Sub-index Distributionsfor the Airfare Index Application1997 1998Quarter 3 Quarter 4 Quarter 1 Quarter 2Unrestricted business classNo. of sub-indexes 2,944 1,739 1,800 2,98550 th percentile 0.999 1.004 1.000 1.00890 th percentile 1.286 1.292 1.376 1.56595 th percentile 1.636 1.765 1.970 3.67199 th percentile 50.464 189.000 76,283.000 10,421.000Unrestricted first classNo. of sub-indexes 8,552 7,313 2,888 5,63650 th percentile 0.992 1.051 1.000 1.00090 th percentile 1.231 1.462 3.947 1.38795 th percentile 1.696 2.222 7,500.000 2.64499 th percentile 14,850.000 9,700.000 148,400.000 52,700.000Restricted business classNo. of sub-indexes 2,617 2,600 2,011 1,89850 th percentile 1.000 1.005 0.982 1.00010 th percentile 0.588 0.589 0.102 0.3835 th percentile 0.245 0.297 0.009 0.1001 st percentile 0.000 0.003 0.000 0.000Restricted first classNo. of sub-indexes 9,524 9,428 3,655 3,75650 th percentile 1.000 1.015 1.000 1.00010 th percentile 0.840 0.860 0.270 0.6465 th percentile 0.639 0.671 0.000 0.3391 st percentile 0.000 0.000 0.000 0.000APPENDIX BPartial DerivativesTo derive equations (11) and (12), the function F Iis expanded around the point I =µ = ( µ , ..., µ ).The general formula for the third-order Taylorexpansion isf() I = f ( µ ) + ∑f ' g ( µ)( I g – µ )1+ --∑∑f ''g1, g2( µ )( I2g1 – µ )( I g2 – µ )1+ --∑∑∑f '''g1, g2,g3 ( µ ) •6( I g1 – µ ) • ( I g2 – µ )•( I g3 – µ ).For a derivation of the third-order expansion of T I(equation (11)), see Lent and Dorfman (2004a). Thefirst- and second-order partial derivatives of F Ievaluated at I = µ (used in the derivation of equation(12)) are as follows:∂F------- I∂I g2∂ FI---------2∂I g2∂ FI------------------∂ ∂I g1I g2I =µI =µI =µ===w g⎛µ – 1 ⎜ w 2gµ 1⎝2 2w– w g2 – w ⎞ g1g2 + ------------------------ ⎟2 ⎠– ⎛w w g12 g22 + w w g11 g21⎞⎜------------------------------------------------- + w g1w g2 ⎟⎝ 2⎠52 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

Estimating Link Travel Time Correlation: An Application ofBayesian Smoothing SplinesBYRON J. GAJEWSKI 1,*LAURENCE R. RILETT 21 Assistant ProfessorSchools of Allied Health and NursingBiostatisticianCenter for Biostatistics and BioinformaticsMail Stop 4043The University of Kansas Medical Center3901 Rainbow Blvd.Kansas City, KS 661602 Keith W. Klaasmeyer Chair inEngineering and Technology,and DirectorMid-America Transportation CenterUniversity of Nebraska-LincolnW339 Nebraska HallP.O. Box 880531Lincoln, NE 68588-0531ABSTRACTThe estimation and forecasting of travel times hasbecome an increasingly important topic as AdvancedTraveler Information Systems (ATIS) have movedfrom conceptualization to deployment. This paperfocuses on an important, but often neglected, componentof ATIS—the estimation of link travel timecorrelation. Natural cubic splines are used to modelthe mean link travel time. Subsequently, a Bayesianbasedmethodology is developed for estimating theposterior distribution of the correlation of traveltimes between links along a corridor. The approachis illustrated on a corridor in Houston, Texas, thatis instrumented with an Automatic Vehicle Identificationsystem.INTRODUCTIONEstimating and forecasting link travel times hasbecome an increasingly important topic as AdvancedTraveler Information Systems have moved fromconceptualization to deployment. Sen et al. (1999)proposed estimating the correlation of travel timesbetween various links of a corridor as an open problemfor future research. In this paper, we assumethat instrumented vehicles are detected at discreteE-mail addresses:* Corresponding author—bgajewski@kumc.eduL.R. Rilett—lrilett@unl.eduKEYWORDS: Automatic vehicle identification, GibbsSampler, intelligent transportation systems, Markov ChainMonte Carlo.53

points in the traffic network, and links are defined asthe length of roadway between adjacent detectionpoints. The set of contiguous links forms a corridor.The link travel time for a given instrumented vehicleis calculated based on the times at which each ofthese vehicles passes a detection point.Using these observations, link summary statistics,such as travel time mean and variance as afunction of time of day, can be obtained. The traveltime statistics for the corridor may be obtaineddirectly or be based on the sum of the individuallink travel times. In the latter case, a covariancematrix often is required, because link travel timesare rarely independent.This paper focuses on estimating the correlationof link travel times using Bayesian statistical inference.While the problem is motivated and demonstratedusing vehicles instrumented with AutomaticVehicle Identification (AVI) tags, the methodologiesdeveloped can be generalized to any probe vehicletechnology. In addition, while the AVI links havefixed lengths, the procedure can be applied to linksof any length.The mean link travel time is a key input forestimating the link-to-link travel time correlationcoefficient. A continuous estimate of the mean linktravel time as a function of the time of day is animportant input to this process. In this paper, we usea natural cubic splines (NCS) approach to estimatethe mean travel time as a function of time. The differencebetween each individual vehicle travel timeand the corresponding estimated mean travel time isused, along with standard correlation equations, toobtain a point estimate of the correlation coefficient.A technique for calculating the variability ofthe estimate is also developed in order to makeinferences about the statistical significance of thiscorrelation coefficient.Traditionally, variability is estimated usingasymptotic theory. However, for the travel timeestimation problem, this approach is complicatedbecause of the nonparametric nature of the estimatorand the covariance between links. Consequently,we adopted a Bayesian approach, which had anumber of benefits in terms of interpretation andease of use. An added benefit to this approach isthat the actual distribution of the parameter isprovided, which allows a much broader range ofstatistical information, and consequently betterresults, to be obtained. Further, we hypothesizedthat the distribution of the correlation coefficientcould be used by traffic operations staff to helpcharacterize the corridor in terms of the consistencyof individual vehicle travel times relative tothe mean travel time. As such, it may be considereda performance metric for traffic operations.In this paper, an 11.1 kilometer (km) (7.0 mile) testbed located on U.S. 290 in Houston, Texas, was usedto demonstrate the procedure. AVI data were obtainedfrom the morning peak traffic period. We chose thistime period because U.S. 290 experiences the highestlevels of congestion in the morning than at any othertime, and because estimating and forecasting traveltimes during congested periods are considerably morecomplex than during noncongested periods.This paper is divided into four sections. First wepresent a traditional approach to correlation coefficientestimation with a special focus on the inherentcomplexities and difficulties. Next we providedetailed discussion of the proposed Bayesianapproach. The third section demonstrates the methodologyusing AVI data observed from the test bedand compares the Bayes approach to a more traditionalapproach for estimating correlation. Wefound that the estimates and their intervals can becalculated using the proposed approach. Then, theestimated correlation coefficients are examinedfrom the viewpoint of traffic flow theory. The lastsection gives concluding remarks.We hypothesized that the positive correlationindicates the links can be categorized as consistentin that drivers who wish to drive faster (or slower)than the mean travel time can do so. Conversely, ifthe correlation between links is negative, then thelinks can be categorized as inconsistent. In this situation,drivers who are slower (or faster) than averageon one link are more likely to be faster (orslower) than average on the other link. Finally,when the correlation coefficient is at or near zero,then the system is operating between the twoextremes. Here, the drivers are unable to maintainconsistently lower or higher travel times betweenlinks, again in relation to mean travel time, and thelink travel times may be considered independent.54 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

This latter case is often assumed in corridor traveltime forecasting and estimation, although theassumption is rarely tested.TRADITIONAL APPROACH ANDSMOOTHING SPLINESOver the last 10 years, most urban areas of NorthAmerica have seen extensive deployment of intelligenttransportation system (ITS) technologies.ITS traffic monitoring capabilites can be categorizedbased on whether they provide point orspace information. For instance, inductance loopdetectors provide point estimates of speed andvolume. Conversely, AVI systems provide spacemean speed estimates of instrumented vehicles. Thefocus of this paper is on AVI-equipped systems.Note that even though the focus is on AVI systems,the procedure can be readily generalized to othersystems that provide space information such asthose that utilize global positioning satellites or cellphone location technology.Because of the nature of AVI systems, the speedof any one vehicle at a given time is unknown;instead, the travel time (or space mean speed) ofeach vehicle on each link is calculated based onthe time stamps recorded at each AVI reader. Thetravel time of vehicle i along link l on any givenday is defined as Y il . Because the relationshipbetween travel time variability and time of day maybe considered unstable, a natural log transformationz il = ln(Y il ) is used to stabilize the relationship.Assume that g il is the expected value of z il . It isassumed that the distribution of this transformationhas a multivariate normal (MVN) distribution asshown below:whereg il[ ] iid ∼ MVN([g il ],Σ) ∀ i,lz il(1)is a smooth function representingthe mean log traveltime for link l; andcolumn vector of g il ’s from i = 1,…,n, will be estimatedusing NCS, an approach that is discussed indetail elsewhere (Green and Silverman 1995;Eubank 1999). The fundamental calculation of anNCS is linear in nature. For example, ĝ l = A( α)z l ,where z l is the column vector of z il ’s, gives the meanlog travel time profile for a particular day on link l,where α is the tuning parameter. The tuning parameteris discussed later, and details for the calculationof A( α)are shown in the appendix under “NCSAlgorithm.”The test bed for this study is a three-lane sectionof U.S. 290 located in Houston. It has a barrierseparatedhigh-occupancy vehicle (HOV) lane thatruns along the centerline of the freeway, but the datautilized are from the non-HOV section of the freeway.Eastbound (inbound) travel time data werecollected over a 11.1 kilometer (7.0 mile) stretch ofU.S. 290 from 4 AVI reader stations (yielding 3links). The lengths of links were 2.5 (1.6), 4.6 (2.9),and 4.0 (2.5) kilometers (miles), respectively. Thedata were collected over 20 weekdays in May 1996from 6:00 a.m. to 8:00 a.m.Figure 1 and table 1 outline an example of theabove calculation for a subset of the data (i.e., 18observations on day 1 that begin at 7 seconds andrun to 6,822 seconds). In general, a tuning parameter,α , that is too large produces a mean estimateFIGURE 1 Logarithm of Travel Time forLink 1 v. Time of DaySeconds from 6 a.m. on day 1ln (travel time)7.06.56.05.55.0HighMediumΣ = [ σ ll ′], where is the variance-covariance2 matrix of the log travel time[σ ll ′] = σ l when l = l′between links l and l ′ .The normality assumption can be checked globallyby inspecting the residuals. In this paper g l , the4.54.071,8883,148Low4,2824,952Time, t (seconds)6,2226,822GAJEWSKI & RILETT 55

TABLE 1 Logarithm of Travel Time for Link 1 v. Time of DaySeconds from 6 a.m. on day 1Estimated mean ln (travel time)Time of entry Observed α = 8x10 3 α = 3x10 5 α = 1x10 11t (seconds) ln (travel time) (Low) (Medium) (High)7 6.15 6.15 6.12 6.03816 5.80 5.80 5.90 5.851,415 5.85 5.84 5.77 5.721,888 5.58 5.58 5.64 5.622,252 5.58 5.58 5.51 5.542,607 5.43 5.43 5.32 5.463,148 4.93 4.93 4.99 5.343,691 4.74 4.73 4.85 5.224,086 4.69 4.73 4.97 5.144,282 5.27 5.22 5.09 5.094,569 5.23 5.27 5.24 5.034,731 5.47 5.44 5.30 5.004,952 5.43 5.43 5.31 4.955,356 5.05 5.06 5.17 4.865,811 5.02 5.00 4.90 4.766,222 4.38 4.41 4.61 4.676,531 4.52 4.48 4.43 4.606,822 4.29 4.31 4.28 4.54that is too smooth and does not follow the patternlaid out by the data. For example, it can be seenthat when α = 1x10 11 , the NCS is basically adecreasing straight line and captures none of thetraffic dynamics. Conversely, a tuning parameterthat is too small yields a rough NCS. It may be seenthat when α = 8x10 3 , the function essentially runsthrough each observation point and does not provideadequate smoothing. However, for the intermediatevalue α = 3x10 5 , there is an adequate tradeoffbetween the travel time dynamics and the smoothing.Therefore, it is important to identify a tuningparameter that is smooth but appropriately followsthe dynamic trend. This can be accomplished usingvisual inspection or by automatic techniques.A popular choice for automating the selection ofthe tuning parameter is using the Generalized CrossValidation (GCV) method (Green and Silverman1995, p. 35). In this method, the smoothing curvefor one choice of tuning parameter is calculatedwithout the first value. Subsequently, the averagesquare error is calculated using the remaining values.This is repeated for all times during the day. A modifiedversion of the process eliminates the need toremove each value by using the following formula:n 1n∑i = 1– {z il g il } 2GCV( α)= ------------------------------------------------- .n⎧1 n – 1 ⎫⎨ – ∑ A ii( α ) ⎬⎩i = 1⎭In essence, a convex function of the tuning parameteris drawn and the choice of tuning is the minimum ofthis function. The advantage of using this procedureis that the process is automated.Figure 2 shows the log travel time as a function oftime of day (6 a.m.–8 a.m.) for test bed links 1 and2. For illustration purposes, a subset of the vehicleshas its log travel times highlighted with a circlearound the observation. In this particular example,the log travel time experiences only slight changesduring this period of time: it begins relatively flat,–56 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

FIGURE 2 Log Travel Time v. Time of Day for Links 1 and 2Seconds from 6 a.m. on day 19ln (travel time)4.82A: LINK 14.74.64.54.44.3●●●α●= 10 5 ●●● ●●●●● ●●●● ●●●●●●● ●●●● ●●●●●●●● ●● ●●● ● ●●● ●●●●●●● ●●●● ● ● ●●●●●● ● ●●●●●●●●●●●● ●● ● ●● ● ● ● ● ● ● ●● ●● ●● ● ●●● ● ●● ● ● ●● ● ●● ● ● ●● ●●● ●●● ●● ● ● ●● ● ●● ●● ● ● ● ●● ●●●●● ● ● ● ●● ●●● ●●● ● ● ●● ● ● ● ●● ●●●●● ●●●●● ●●● ● ●● ●●● ●●●●●●●● ●●●● ● ●●●● ● ● ●● ●● ● ●● ●● ● ●● ●α = 10 7●●●●●●●● ●●●● ●●117071,3472,468α = 103,1813,822Time, t (seconds)4,5555,1726,1736,944ln (travel time)5.42B: LINK 25.35.25.15.04.9●α = 10●●●●●●α = 10 5 ●●●●●●●●●●● ●●●● ●●● ●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●● ● ●●●●●●●●●●●●● ● ●● ● ●●●● ●●● ●● ● ● ●●●● ● ●●●● ●●● ●●●●●● ● ●●●●●●●●●●● ●●● ● ●●●●●●●● ● ●●●●●●●●● ●● ● ● ●●●● ●●● ●●● ● ●●● ● ●● ● ●● ●● ● ● ●●●●●● ●●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●● ●●●●α = 10 7 ●● ●●●117071,3472,4683,1813,822Time, t (seconds)4,5555,1726,1736,944experiences an increase at around 2,000 seconds anddecreases starting at 4,000 seconds. Figure 2 alsoshows three NCS where each one has a different tuningparameter. For this example, a tuning parameterof α = 1x10 5 was chosen based on visual inspectionand α = 0.065x10 5 was identified based on theGCV process. For a particular day, the same tuningparameter is applied to all links along the corridor.To illustrate the correlation between travel timeson the two links, consider the first highlightedvehicle that begins to traverse link 1 at approximatelyt 1 = 11 seconds. Note that this vehicle has aGAJEWSKI & RILETT 57

lower than average travel time on both links 1 and2. The second highlighted vehicle begins totraverse link 1 at approximately t = 548 seconds,and it can be seen that its observed link traveltimes are above the mean travel time on links 1and 2. Eight of the 12 highlighted vehicles in figure2 show evidence of positive correlation. Noticethat this method requires that the vehicles traverseboth links, and vehicles entering after the beginningof the first link or exiting before the end ofthe second link are not included in the correlationcalculation. Later we employ these calculations forthree links where we include only vehicles thattraverse the entire three-link corridor.To quantify the above relationship, we calculatedthe cross product of the residuals. More specificallythe covariance,[ ] = E ε i1 ε i2σ 12 = E ( z i1 – g i1 )( z i2 – g i2 )ρ 12σ 12= ----------σ 1 σ 2[ ] andwhere σ 1 as defined earlier, was obtained using thefollowing procedure. The mathematical details ofthe procedure are in the appendix under “ClassicEstimation of Correlation.”Step 1: Transform the data to logarithms.Step 2: Estimate the mean function by usingNCS.Step 3: Calculate residuals by subtracting themean function from the logarithm traveltimes.Step 4: Estimate the variance and covarianceusing the procedure outlined in theappendix.Step 5: Calculate confidence intervals of thecorrelation using standard asymptoticdistribution theory (Wilks 1962).For the example data shown in figure 2, theabove procedure resulted in an estimated correlationof ρˆ 12 = 0.6918. This relatively high valuereflects the positive correlation for the log traveltime of vehicles between the links.Note that the above approach is problematicbecause it results only in a partial solution. First, itwould be desirable to have the correlation coefficientfor the untransformed data Y il and not thenatural log of the data, ln(Y il ). Because the abovecorrelation coefficient reflects the transformed,rather than untransformed, scale, interpretation isdifficult. Secondly, and more importantly, in order tomake statistical inferences regarding the correlationcoefficient, the distribution of the untransformedcorrelation coefficient is required. Identifying thedistribution of the untransformed correlation coefficientis equivalent to finding the standard error ofthis estimate in the normal distribution case usinglarge sample theory. Additionally, because the correlationcalculation requires an estimate of themean function using NCS, this stage of uncertaintyshould be incorporated into the estimate of thestandard error. The entire process is very difficult toaccomplish, because the NCS and the sum ofsquares residuals need to be calculated simultaneously.The traditional or classic approach outlinedin the appendix yields an approximation that doesnot account for the uncertainty in the NCS. To overcomethese difficulties, an approach for obtainingthe distribution of the correlation coefficient on theuntransformed scale using Bayesian methodology isdeveloped.BAYESIAN APPROACHTo address the covariance problems identified inthe preceding section a Bayesian methodology isemployed.General BackgroundIn Bayesian inference, the unknown parameters ofthe probability distributions are modeled as havingdistributions of their own (Gelman et al. 2000).Generally, the identification of the distribution ofthe parameters, or prior distribution, is done beforethe data are collected. Suppose that is a vectorθ˜containing unknown parameters with a prior distributionπ( ). The observed data are used to updateθ˜this prior distribution. The data are stored in thevector ỹ and its distribution, conditional on theparameter vector , is the likelihood f( ỹ ). Theθ˜θ˜parameters’ distribution is updated using the Bayestheorem as shown below:f( ỹ )π( )h( ỹ ) = ----------------------------------------θ˜θ˜ . (2)θ˜f( ỹ )πθ˜∫θ˜()dθ˜58 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

Once the posterior distribution, h(.,.), is identified,it can be used to make inferences about themodel parameters and to identify the percentiles,the mean, and/or the standard deviation of the distributionof the parameter.Because the distribution shown in equation 2 isvery difficult to solve, a simulation method knownas Gibbs Sampler or Markov Chain Monte Carlo(MCMC) is used to approximate the distribution.This approach has become increasingly popular overthe last 10 years for Bayesian inference (Gelfand2002). The Gibbs Sampler is generally constructedof univariate pieces of the posterior distribution. (Formore on the this topic, see the appendix under“Gibbs Sampler.”) Note that the Gibbs Samplerrequires a number of simulation replications that wedenote as nreps.The procedure is best illustrated by a simpleexample. Consider a travel time/time-of-day relationshipwhere the mean travel time does not fluctuateand there is no need for an NCS (shown infigure 3). In this situation, it is reasonable to treatthis distribution as being normally distributed,⎛y . In this case, the parameter vector isi N ⎝µ , σ 2 ⎞∼ ⎠= ( µ σ θ˜2 ) ′where µ is the mean travel time and σ 2 is the varianceof travel time. When choosing the prior distributions,it is convenient to choose a distribution ofa conjugate form (Gelman et al. 2000). Because theposterior distribution is of the same family as theprior distribution, it leads to a straightforwardcomplete conditional distribution. The prior distributionsof conjugate form that we adopted in thispaper are µ ∼ Nap ( ,2 ) and σ 2 ∼ IG( c,d), whereIG is the inverse gamma distribution. (A moredetailed discussion of the technical reasons forchoosing conjugate prior distributions can be foundin Gelman et al. (2000). The details of the GibbsSampler algorithm for the example are in theappendix under “Example Gibbs Sampler.”)For the simple example, nreps was set to 2,000and the distributions are summarized with histogramsas shown in figure 3. In figure 3B, the meanparameter is summarized. The 5th and 95th percentilesof µ ỹ are 5.068 and 5.086 seconds, respectively.Because of the simple nature of the example,it is possible to use standard methods to calculatethe 90% credible intervals. Note that the classicalt-distribution-based 90% confidence interval, whichwould be 5.069 to 5.085 seconds, is comparable tothe percentiles of the Bayes approach because ofthe diffuse priors. An added advantage of the simulationis that any function of the distribution canbe summarized. For example, figure 3C displaysthe distribution of ln σ 2 in the form of a histogram.It shows that, like the mean, the log variance⎝⎛ ⎠⎞tends to have a normal distribution. This is similarto the normal distribution properties associatedwith maximum likelihood estimators.Natural Cubic SplineBayesian MethodWhile the idea of Bayesian NCS has been used inother applications (Berry et al. 2002), here weexpand the concept in order to calculate the covariancefunction for the travel time for vehiclesbetween links. The travel time along link l whenstarting at time t i is defined as Y il . Because traveltime variability is unstable as a function of time, thevariance is stabilized using a natural log transformationz il = ln( Y il ). It is assumed that this distributionwill have a multivariate normal (MVN)distribution with a smooth mean function and afixed covariance matrix as shown in equation 3where:g l∑ =[ ] iid ∼ MVN([g il ],Σ) ∀il,⎛⎜⎜⎜⎜⎝z il(3)is a smooth function representing themean log travel time for link l; andσ 12σ 12 σ 13σ 22σ122 ⎟ ⎟⎟⎟⎞sym σ 3 ⎠is the variance-covariancematrix of the log travel time.This assumption can be checked globally in themodel using Bayes p-values (Gelman et al. 2000).As discussed earlier, the area of focus is on theuntransformed space and, therefore, standardtechniques are used to calculate expectations ofexponential space random variables (Graybill1976). The moment generating function (MGF)for the multivariate normal distribution ismt () = exp ( t ′µ + t′Σt ⁄ 2 ) . Using this MGF, it canGAJEWSKI & RILETT 59

FIGURE 3 Example Problem with Static Travel Timeln (travel time)5.43A600Count, B = 2,0003B: Mean Parameter5.3●●●●●●5005.25.15.04.9●●●●●●●● ●●●●● ●●●● ●●● ●●●●●●●●●● ●●●●●●●●● ●●●●●●●● ●●●●● ● ●●●● ●●●●●● ●● ●● ●●● ●●● ●● ● ●●●● ● ●●●● ●●● ●●●●●● ●●●● ●●● ●●●● ●● ● ●●● ● ●● ● ● ● ●● ● ● ●● ● ● ●●●● ●●●● ●● ●●● ● ●● ● ● ●●●● ●● ● ●●● ●●●● ●●●●●● ● ●●●●●●●●●●●●●● ●●●●●● ●● ●11Time, t (seconds)7071,3472,4683,1813,8224,5555,1726,1736,94440030020010005.06 5.07 5.08 5.09 5.1µ |y (seconds)~Count, B = 2,0001,0003C: Log Variance8006004002000–5.5 –5.4 –5.3 –5.2 –5.1 –5.0 –4.9ln( σ2 )| y, 2ln (seconds)be shown that the covariance for an individual specifically obtained for any given day. This isvehicle between two links as a function of time is shown below:cov y il y il ′ ) exp g il g il ′ σ 2 2( exp(σ( , = ( + + l ⁄ 2 + σ l ′ ⁄ 2)• (4) corr ( y il , y )ll ′ ) – 1)= ----------------------------------------------------------------------------- .(5)il′2( exp( σ ll ′) – 1).⎛exp( σ l ) – 1⎞⎛2exp( σ⎝⎠ l ′)–1⎞⎝⎠The correlation coefficient of the untransformeddata is shown in equation (5), and it is important tonote that it is not a function of time. This allows apoint estimate of the covariance between links to beThe prior distribution of the smooth mean for link lis–1g l ∼ MVN⎛0, ( α K ⁄ σ l2 ) ⎞⎝⎠60 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

where K = αnQB – 1 Q′ and α are used in calculatingNCS and Σ ∼ Inv-Wishart( Λ 0 ). The matrices– 1Q and B are defined in the appendix. “Inv-Wishart”denotes the inverse Wishart distribution. If a noninformativeversion of the inverse Wishart distributionis used, the following posterior distribution isobtained:2 2g l z l ,σ l α ∼ MVN(A( α)z l , σ l A αwhereΣ ε ∼ Inv-Wishart n ( S)ε l = z l – g l , andS = ε i ε i ′(6)The above posterior distributions are extensionsof the multivariate calculations found in manyBayesian texts (e.g., see Gelman et al. 2000). Thedistributions can readily be calculated with a twostepGibbs Sampler. The approach is summarized inthe appendix under “NCS Bayesian Algorithm.”The steps can be calculated easily in any matrixbasedprograms that can simulate a multivariatenormal distribution and an inverse Wishart. Forexample, S-plus, MATLAB, R, or SAS-IML wouldbe appropriate.DATA ANALYSISThe methodology is illustrated on the test bed usingthree days’ data representing three different trafficconditions: moderate, heavy, and light congestion.In all cases sampled, nreps is set to 500.An assessment of this log fit can be found usingBayes factors or Bayes p-values (Gilks et al. 1996).In this paper, Bayes p-values were used to verifymodel goodness-of-fit and to check the validity ofthe underlying assumption. Two steps are involvedin this calculation. First, the predictive values of theMCMC output are calculated using the MCMCmodel parameters. In this paper, the predictive distributionfor all links at t i is=ipz˜wheren∑i = 1( b)( b), ig˜( b)ẽ i+∼ MVN( 0,I )( b) ( b)1⁄2e i Σand( ))p stands for predictive distribution with the“b”th iteration of the MCMC.From the output, a χ 2 discrepancy function iscalculated between the observed data and theparameters, as well as between the predicted dataand the parameters from the MCMC. The discrepancyfunctions are calculated for each of the iterationsof the MCMC. In addition, the proportion ofiterations from the MCMC for which the data discrepancyis larger than the predicted discrepancy isenumerated.The flexibility in the choice of discrepancyfunction allows the user to test many alternatives.The average within-link auto-correlation per day isa relevant and interesting criterion to test. This discrepancyfunction uses the standardized dataset’sone lag auto-correlation. The standardized data are( b)ũ iand the one lag correlation for the data and the predictivedata isn – 1( b) ( b)∑ ũ i ũi + 1T ( b)i=1( b)T p=----------------------------n – 1n – 1( b) ( b)∑ ẽ i ẽi + 1i=1= --------------------------- .n – 1From this, theb= ( i– iz˜g˜ ( ) )Σ band( ) – 1p-value ⎨⎧ #( T ( b)– T ( b))> 0 p ⎬⎫ =⁄ nreps > 0.⎩⎭A test using this choice of discrepancy function,on all days, found that 75% of the days have a p-value within an acceptable range of 0.01 to 0.99.The other 25% of the days report a p-value lessthan 0.01. These latter p-values come from daysthat have predominately free-flow traffic conditions.These results indicate that while the model fits fortraffic that is dynamic, it needs additional work forfree-flowing traffic days. It is hypothesized that anextra parameter that accounts for auto-correlationmay reduce this discrepancy. Because free-flowtraffic conditions are not as interesting from atraffic monitoring center (TMC) point of view,this extension is not performed here.Figure 4 presents data for a day with moderatetraffic congestion. Figures 4A and 4B show theGAJEWSKI & RILETT 61

FIGURE 4 Illustration of Covariance Function and Correlationfor a Moderately Congested Two-Link Example: Day 104A: LINK 14B: LINK 25.2 ln (travel time) ● ●●●●● ● ●5.0●●●● ●● ●●●●●● ● ●●●●● ● ●●●●●●● ●● ● ● ●●●●●● ● ● ● ● ●● ● ●4.8● ● ●● ● ●● ● ●● ●●●●●●●●●●●●● ●●● ● ●● ● ●●● ● ●● ●● ● ●●●●● ●4.6● ●●● ●● ●●●● ● ●● ● ● ● ● ●●● ●●●●● ●●●● ●●●●●●● ● ● ●●● ●● ● ●●●●●●● ●● ●●●●●●●●● ●● ●●●●● ● ● ●●●●●● ●●● ●●●● ●●● ●● ● ●●●●● ● ●●●●●●● ● ●● ●●● ● ●●●● ● ● ● ●●●●● ●● ● ●●●●●● ●●●●●●●●● ●●● ●4.4● ●●●●●●● ●●●4.2Time, t (seconds)104789051,4861,8322,5682,9743,3573,8564,3354,8135,3025,8876,5586,9726.0 ln (travel time) ● ●●●● ● ● ●●● ● ●●● ●● ●● ●●●● ●●● ●●●●●● ● ● ● ● ●●●● ● ●●● ● ● ●● ●● ●●●●●● ●●● ● ●●●● ● ●5.5●●● ●● ●●● ●● ●●● ● ● ● ● ●●●●● ●●● ●●● ●●● ● ● ●●●● ●● ● ●●● ●●●● ● ●●●●●● ●●●●●●●●●●●●●● ● ●●●●● ● ●●● ● ●● ●●●● ● ●● ●● ●● ● ●●● ●● ●● ●● ●● ●● ●●●● ●● ● ●●●● ●● ● ●●●●● ●●● ● ●● ●● ●● ● ●●●●●● ●5.0●●●● ●●Time, t (seconds)4.5104789051,4861,8322,5682,9743,3573,8564,3354,8135,3025,8876,5586,972100Covariance,untransformed scale4C: LINKS 1 and 24D: CORRELATION, LINKS 1 and 280604095 th percentile180 Count160140120100806012200105 th percentile4789051,4861,8322,5682,974Time, t (seconds)3,3573,8564,3354,8135,3025,8876,5586,972relationship between the log travel time and time ofday for links 1 and 2, respectively. In both instances,the natural log travel time fluctuates between six(high) and four (low). Using the Gibbs Sampler, the5th and 95th percentile values of the covariance ofthe travel times were calculated and are shown infigure 4C. Note that the covariance is positive and40200–1.0 –0.5 0 0.5 1.0fluctuates proportionally to the mean travel times ofthe links. Because the correlation result in equation5 is time-independent, figure 4D can be used toshow the distribution of the correlation coefficient.The distributions for the Bayes approach are summarizedwith the 5th and 95th percentile values. Werefer here to this region as the 90% Bayes credibleρ62 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

egion (BCR). The classic approach utilizes a 90%confidence interval (CI) based on normal asymptotictheory. This corresponds to a 90% BCR of (0.26,0.42). The classic 90% CI was slightly narrower(0.30, 0.45).Figure 5 shows an analysis similar to that in figure4 but for a day in which the congestion is muchgreater. It can be seen that the correlation is negativebetween adjacent links. The 90% BCR of the correlationcoefficient is (–0.39, –0.12). The classic 90%CI was narrower and had a shift of (–0.42, –0.17).FIGURE 5 Illustration of Covariance Function and Correlationfor a Very Congested Two-Link Example: Day 75A: LINK 15B: LINK 2765● ●● ● ●●●● ●● ●● ●● ●●●● ●●● ●●●●●●● ●● ● ● ●●●●●●●● ● ●●●●● ●● ● ● ●● ●● ●●●●●● ●●●● ● ●●● ●●●● ●● ● ●● ● ●● ●● ● ●● ●● ●●●●●● ● ●●●●●●●●●● ●●●● ●●● ● ● ●● ●● ●●●● ●●● ●●●●● ● ● ●●●● ● ●●●●●● ●41026341,1853,9598 ln (travel time) Time, t (seconds)4,9326,2446,9207.5 ln (travel time) Time, t (seconds)7.06.56.05.5●●●● ● ● ● ●● ●● ● ●●●● ●● ●●●●● ● ●● ● ● ●●●● ●●● ● ●●● ● ●● ●● ● ●●●●●●●● ●●●● ● ●●●●● ● ●● ●●●● ●●● ●●● ●● ●●● ● ●● ●●●● ●●●●● ● ● ●●●●●● ●●● ●●●●●●●● ●●● ●●●●●● ● ●● ●● ● ●●● ●● ●●●● ●●5.01026341,1853,9594,9326,2446,9200Covariance,untransformed scale5C: LINKS 1 and 25D: CORRELATION, LINKS 1 and 2140 Count 12–1,00095 th percentile120100–2,000–3,000–4,0005 th percentile80604020–5,0001026341,1853,9594,932Time, t (seconds)6,2446,9200–1.0 –0.5 0 0.5 1.0ρGAJEWSKI & RILETT 63

FIGURE 6 Illustration of Covariance Function and Correlationfor a Less-Congested Two-Link Example: Day 206A: LINK 15.0 ln (travel time) Time, t (seconds)5.4ln (travel time)6B: LINK 24.84.64.44.2●● ●● ● ●●● ● ●●●●●●● ●●●● ● ●●●●● ● ● ● ●●●●●●● ● ● ●●●●●● ● ●●● ● ● ● ●●●● ● ● ●● ●● ●●● ●● ● ●● ●● ●●●●●● ● ● ●●●●● ● ●● ● ●●●●● ●● ● ● ●●●● ● ● ●●● ●●● ●● ●●●● ● ● ●●●● ● ● ● ● ●● ● ● ●●●●● ● ●●●● ●●● ● ●● ● ● ● ●●● ● ● ●●● ● ●●● ●● ●●●● ●● ●● ● ● ● ●● ● ● ●● ● ●●● ●●●●● ● ● ●●● ●●●●● ●● ● ●●●● ● ●●●● ● ● ●●●●●●204689141,4691,9642,7573,0993,6024,0764,5505,0695,6256,4216,8915.35.25.15.04.920●●● ●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ●●● ●●● ● ●● ● ●●● ●● ●● ●●● ● ●●● ●●● ●●●●●●● ●● ●●●●●●●● ●● ●● ● ●●● ●●● ●●●●● ●●●●●● ● ● ●●●●● ● ●●●● ●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●● ●●●● ●● ● ●●●● ●● ●● ● ●●●●●● ●●●●● ● ●● ●●●●●● ●● ●●● ●●● ● ●● ● ● ●●● ●●●●●● ● ●●●●●●●●●●●●● ● ●● ●● ● ● ●● ●● ● ● ●●● ● ● ● ●●●●● ● ●●●● ●●● ●●● ●●●●●●●● ●●●●●●● ● ●●● ● ● ● ●●●●4689141,4691,9642,7573,0993,6024,0764,550Time, t (seconds)●5,0695,6256,4216,89160Covariance,untransformed scale6C: LINKS 1 and 26D: CORRELATION, LINKS 1 and 2300 Count 125095 th percentile250200401503020204689141,4691,9642,7575 th percentileTime, t (seconds)3,0993,6024,0764,5505,0695,6256,4216,891Lastly, figure 6 shows a situation where thetravel times are less dynamic with high levels offree-flowing traffic, reflected by the positive correlation.The 90% BCR of the correlation coefficientis (0.59, 0.69). The classic 90% CI is (0.61, 0.71).In summation, this correlation coefficient reflects100500–1.0 –0.5 0 0.50 1.0ρthe amount of freedom an individual vehicle has intraveling at a consistent speed relative to the overallaverage travel time. Figure 6 shows a high positivecorrelation, while figures 4 and 5 show correlationnearer zero or negative correlation, respectively.64 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

The “C” component of all three figures reportsthe covariance as a continuous function of the timeof day (equation (4)). It is the pretransformed spacethat allows for interpretations on the original scale.Notice that the fluctuations are proportional to themean travel time from the NCS. This result illustratesthe distinct advantage of the Bayes approachover the frequentist approach. In order to derive aconfidence interval when using a frequentistapproach, a large sample size is required to be ableto apply the linear assumption used in asymptotictheory. In addition, there is a propagation of theuncertainty in the covariance because there are severalsteps in its estimation. For example, there is alogarithm transformation and an adjustment for thesmooth mean via NCS.In contrast, the Bayesian approach does not relyon the large sample size assumption. In addition,the nature of the MCMC iterations implicitlyaccounts for the transformed space. Specifically, thecovariance function’s actual distribution is calculatedwhile ensuring that all forms of error arepropagated. However, given the large number ofprobe vehicles observed, it seems conceivable thatthe large sample properties hold. Therefore, wecompare the frequentist-based approach (or classicapproach) to the Bayesian method.One interesting result is that for any given dayequation (5) summarizes the correlation betweenpairs of links. Because the equation relies on thevariance and covariance between links, whichrequires the estimate of the mean travel times, thedistribution still needs a method that includes theerror propagation mentioned above.Figure 7 summarizes the correlation coefficientsfor all 20 days. The correlation BCR percentiles forall three links and their pairs are shown along withthe classic 90% CI that appears next to the BCRfor comparison purposes. For the adjacent links 1and 2, there are four days that either have a negativecorrelation or a correlation where the 90% BCRcovers zero. There are six such days between theadjacent links 2 and 3. The results are consistentfor the non-adjacent links (i.e., links 1 and 3).This illustrates that the nonpositive correlationremains constant from link to link and seems to bea within-day characteristic.Also, in terms of space mean speed, this correlationmeasure can be compared with traditionaltraffic congestion measures. Suppose a link is consideredcongested when the speed falls below 56km/hr (35 mph). For the first 12 days, all links hada minimum space mean speed that ranged from 8km/hr (5 mph) to 32 km/hr (20 mph). This lattercase corresponds to days when the 90% BCR wasbelow 0.5. In contrast, the last eight days have aminimum space mean speed ranging from 73 km/hr (45 mph) to 105 km/hr (65 mph) and at leastone link pair with a 90% BCR covering 0.5. Thisdemonstrates that the single correlation measurematches traditional measures of congestion thatare based on speed. In general, the heavier the congestion,the lower the correlation of travel timebetween links.Indeed, an obvious question raised by figure 7 isto ask whether the MCMC method is necessary orif the classic approach is an adequate approximation.The lengths of their respective 90% intervalsindicate that on average the Bayes intervals are10.5% longer than the classic intervals. TheMCMC approach has longer intervals because itaccounts for the uncertainty in the estimate of thesmoothing spline. The user of our algorithm maywant to balance the gain in the MCMC approachwith the loss in time it takes to implement the algorithm.For the data from day 1, the 500 MCMCiterations take 36 seconds to implement usingMATLAB on a 2.00 GHz processor with 1.00 GBRAM, whereas the classic approach takes less than1 second to implement. This difference in implementationtime might be different but is well worththe effort for those users who wish to account forall of the uncertainty generated in the estimate ofinterlink correlation. Given the rapid increase incomputational abilities, it is our belief that computationconcerns will not be a deciding factor.The NCS smoothing technique is commonly usedin statistics but not extensively in transportationengineering. There is an explicit tradeoff betweenthe tuning parameter and the fitted curve, and it isimportant that the tuning parameter be selected inan appropriate and consistent manner. Figure 8AGAJEWSKI & RILETT 65

FIGURE 7 90% Bayes Credible Regions for the Correlation for 20 DaysCorrelation1.07A: 90% BCR, LINKS 1 and 27B: 90% BCR, LINKS 1 and 30.50–0.5–1.05 10 15 20Day–1.01.0 Correlation Day0.50–0.55 10 15 207C: 90% BCR, LINKS 2 and 31.0 Correlation Day0.50–0.5–1.05 10 15 20Note: Dashed lines indicate the classic interval approach and solid lines denote the Bayes interval approach.shows the correlation between links 1 and 2 (i.e.,ρˆ 12 ( α)) as a function of the logarithm of the tuningparameter value for day 7. The arrow represents the“optimal” tuning parameter based on the GeneralizedCross Validation method. However, note thatin that figure (8A), the sign of the correlation is positivefor tuning values but negative for others.Therefore, it is important that the tuning parameterbe identified correctly, otherwise the correlationvalue might be not only erroneous but of a differentsign.Figures 8B and 8C show the correlation betweenlinks 1 and 2 (i.e., ρˆ 12 ( α)) as a function of the logarithmof the tuning parameter value for days 10 and20, respectively. The link-to-link correlation seen inthese figures is relatively high and stable. For these66 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

FIGURE 8 Correlation Coefficient for Links 1 and 2 as a Function of the Tuning Parameter1.0Correlation8A: DAY 7, LINKS 1 and 2Correlation1.08B: DAY 10, LINKS 1 and 20.50.500–0.5–0.5–1.0–1 0 1 2 5 8 10 15log10( α )–1.0–1 01 2 5 8 10 15log10( α )Correlation1.08C: DAY 20, LINKS 1 and 20.50–0.5–1.0–1 0 1 2 5 8 10 15log10( α )examples, the travel time fluctuation is relativelysmooth, and the large values of the tuning parametersafeguard the dynamic trend.CONCLUDING REMARKSThis paper demonstrates that for the travel timeestimation problem the traditional approach iscomplicated because of the nonparametric nature ofthe estimator and the covariance between links. Weadopted a Bayesian approach that had a number ofbenefits in terms of interpretation and ease of use.As an added benefit, this approach provided theactual distribution of the parameter, which alloweda much broader range of statistical information, andconsequently better results, to be obtained. Wefound that, contrary to a common assumption usedin many transportation engineering applications,the link covariance is non-zero. Furthermore, theGAJEWSKI & RILETT 67

distribution of the correlation coefficient has thepotential to be used as a performance metric fortraffic operations.From a transportation engineering perspective,this work is important for two reasons. First, thispaper shows that the common assumption thatlink travel time covariance is zero is erroneous.More importantly, we developed a method for calculatingthe covariance with appropriate intervals.This technique can be readily incorporated for calculatingtravel time variance and the associatedinterval. This will have relevance in a wide range ofapplications including route guidance and trafficsystem performance measurement. Secondly, correlationcoefficients have the potential for categorizingthe performance of the traffic system, because theyare a direct measure of how constrained drivers arewith respect to traveling at their desired speed. Theuse of the proposed technique in the above transportationapplications will be the focus of the nextstep in the research.Two caveats to our study are as follows. First, theresults depend on the length of the links in which thevehicles traverse. Suppose that the travel times forthe three links have a positive correlation. However,if for that distance the links were shorter (i.e., sixlinks over the same length) there is no guarantee therelationship will remain the same (e.g., all six linksare positively correlated). No study finds the extentto which this occurs. This issue could be addressedin future research by utilizing a vehicle simulationprogram such as TRANSIMS (2003). With this simulationprogram, the researcher can examine thesetypes of issues by playing “what if” scenarios withvariations of the length of the links under assorteddynamic and complex traffic conditions.The second caveat to our study is that duringseverely congested traffic, link travel times are essentiallyconstant. In this case, researchers will find itdifficult to utilize link travel time correlation as acongestion measure. This case is similar to the freeflowcase where drivers can go at the speed they wish.In both situations, sophisticated congestion measuresare not needed. However, when things are rapidlychanging, this approach would be very useful. Weshow that the method is appropriate under severaldynamic conditions, where the speed ranged from8 km/hr (5 mph) to 105 km/hr (65 mph). Thesewould be the most interesting traffic conditions(e.g., where the travel time fluctuates in and out offree-flow and congested traffic conditions) from atraffic management perspective.ACKNOWLEDGMENTSThis research was funded in part by the TexasDepartment of Transportation through the Trans-Link ® Research Center. The TransLink ® partnershipconsists of the Texas Transportation Institute, RockwellInternational, the U.S. Department of Transportation,the Texas Department of Transportation, theMetropolitan Transit Authority of Harris County,and SBC Technology Resources. The support ofthese organizations, as well as other members andcontributors, is gratefully acknowledged. We thanktwo referees and the editor for helpful and insightfulreviews that greatly improved this article. Theauthors would also like to thank Mary BensonGajewski and Beverley Rilett for editorial assistancein the preparation of this article.REFERENCESBerry, S.M., R.J. Carrol, and D. Ruppert. 2002. BayesianSmoothing and Regression Splines for Measurement ErrorProblems. Journal of the American Statistical Association97(45):160–169.Eubank, R.L. 1999. Nonparametric Regression and SplineSmoothing, 2nd edition. New York, NY: Marcel Dekker.Gelfand, A.E. 2002. Gibbs Sampling. Statistics in the 21st Century,Edited by A.E. Raftery, M.A. Tanner, and M.T. Wells.Chapman & Hall and American Statistical Association,Washington, DC.Gelman, A., J.B. Carlin, H.S. Stern, and D.B. Rubin. 2000.Bayesian Data Analysis. London, England: Chapman &Hall.Gilks, W.R., S. Richardson, and D.J. Spiegelhalter. 1996.Markov Chain Monte Carlo in Practice. London, England:Chapman & Hall.Graybill, F.A. 1976. Theory and Application of the LinearModel. Belmont, CA: Wadsworth.Green, P.J. and B.W. Silverman. 1995. Nonparametric Regressionand Generalized Linear Models. London, England: Chapman& Hall.Ruppert, D., M.P. Wand, and R.J. Carroll. 2003. SemiparametricRegression. Cambridge, England: Cambridge UniversityPress.68 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

Sen, A., P. Thakuriah, X.Q. Zhu, and A. Karr. 1999. Variancesof Link Travel Time Estimates: Implications for OptimalRoutes. International Transactions in Operational Research,(6)75–87.TRANSIMS. 2003. Available at http://transims.tsasa.lanl.gov/,as of May 2004.Wilks, S.S. 1962. Mathematical Statistics. New York, NY: Wiley.APPENDIXNCS AlgorithmThe travel times for each of the individual vehiclesare y l1 , y l2 , y l3 ,…, y ln and are recorded at timest 1 ,…, t n . The steps for calculating A( α)are asfollows:1. Let Q be a matrix of zeros of dimension n–2by n. For i from 1 to n–2 let1Q ii = -----------------------,( – )2. Let B be a matrix of zeros of dimension n–2by n–2.For i from 2 to n–3 let B ii–1 = t i+1 –t i ,B ii = 2(t i+2 –t i ) and B ii+1 = t i+2 –t i+1 .Let B 11 = t 2 –t 1 , B 12 = t 3 –t 2 ,B n–2n–3 = t n–1 –t n–2 and B n–2n–2 = t n –t n–1 .3. SetB = B/6.t i + 1 t i1Q ii + 1 = – ⎛⎝ t i + 2 – t i + 1– 1Q ii + 2 =t i + 2 – t i + 11-----------------------------( )+----------------------- ⎞ , and( – ) ⎠-----------------------------( )t i + 1 t i4. A( α) = I n – nαQ′ ( nαQQ′ + B) – 1 Q .5. The quantity being analyzed isA( α)zzl , where z il = log( y il ).Classic Estimation of CorrelationThe correlation between two links,σˆ 12 = E[ ( z i1 – g i1 )( z i2 – g i2 )] = E[ ε i1 ε i2 ],is estimated using the following procedure:1. Given the tuning parameter α , use an NCS toderive a continuous estimate of the meantravel time over the analysis period for the twolinks and call them ĝ 1 and ĝ 2 .2. For each link estimate the residuals for eachvehicle: εˆ 1 = z 1 – ĝ 1 and εˆ 2 = z 2 – ĝ 2 .3. Calculate the equivalent degrees of freedom(EDF):nEDF = n – A( α).∑i = 14. Estimate the covariance between links 1 and 2:n∑εˆ i1 εˆ i2i = 1εˆ′σˆ 12 --------------------- 1 εˆ= = ------------ 2.EDF EDFEstimate the variance for links 1 and 2 respectively:εˆ i1 εˆ i1εˆi = 1εˆ′--------------------- 1 εˆ i2 εˆ i21 2------------i = 1εˆ′and σˆ 2 --------------------- 2 εˆ2= == = ------------ .EDF EDFEDF EDF5. Calculate the estimated correlation:2σˆ 1n∑σˆ 12ρˆ 12 = ----------σˆ 1σˆ 2Note that the basic concept of EDF is to penalizethe estimation of the covariance matrix using theproper equivalent of degrees of freedom. The EDFfor splines is discussed in Green and Silverman(1995) and Ruppert et al. (2003).Inferences utilizing the above approach can beaccomplished with large sample distribution theorybased on Wilks (1962, p. 594). The result indicatesthat as the number of vehicles approaches infinitythe statistic 1/2 log[ ( 1 + ρˆ 12 )/( 1 – ρˆ 12 )] has anapproximate normal distribution with mean1/2 log[ ( 1 + ρˆ 12 )/( 1 – ρˆ 12 )] and variance 1/n. Thus,this distribution is used to calculate a 90% confidenceinterval with the formulawhere1 ⎛1 + ρˆ 12 ⎞ ⎛r L-- ⎜----------------⎟ 1.64 1 ⎞= log – ⎜------⎟ and2 ⎝1 – ρˆ 12 ⎠ ⎝ n⎠1 ⎛1 + ρˆ 12 ⎞ ⎛r U-- ⎜----------------⎟ 1.645 1 ⎞= log + ⎜------⎟ .2 ⎝1 – ρˆ 12 ⎠ ⎝ n⎠The 1.645 corresponds to the 90th percentile of thestandard normal.n∑( exp( 2r L ) – 1)( exp( 2r------------------------------------U ) – 1), -------------------------------------( 1 + exp( 2r L ))( 1 + exp( 2r U ))GAJEWSKI & RILETT 69

Gibbs SamplerThe approach is simulation-based where nreps isthe number of simulations performed on theparameter vector and b = 1,2,3,…,nreps. The GibbsSampler begins with a reasonable starting value( 1)(i.e., estimates of the parameters from a traditionalapproach). From this starting value, the k thθ˜component of is updated conditional on the dataθ˜and all the other components of the parametervector, h⎛( b + 1θ ) ( bk θ ) ( b)1 ,...,θ k – 1 , ỹ⎞. The next step is⎝⎠to simulate the subsequent component of the( b + 1 ) ( b) ( b)( b + 1 )parameter vector h⎛θ k – 1 θ1 ,...,θ k – 1 , θk, ỹ⎞.⎝⎠This is repeated for all unknown parameters untilnreps simulations for each component of theparameter vector have been completed.Example Gibbs SamplerThe following steps are used to perform the GibbsSampler simulation:1. Set the prior distribution parameters to bediffusea = 0, p 2 = ∞, c = 0 and d = 0.2. Set the starting values for the unknownparametersµ ( 1)= y and σ 21 ( ) = s 2 .3. Generate the mean portion⎛µ ( b)N y σ2 ( b – 1)⎞∼ ⎜ , ----------------⎟,⎝ n ⎠which is of conjugate form (see Gelman etal. 2000 for the derivation).4. Generate the variance portion⎛⎛σ 2( b)IG n y i – µ ( b)⎞ 2 ⎞n⎜⎟⎜ ∑ ⎝ ⎠ ⎟-- i = 1∼ ⎜, ----------------------------------- ⎟⎜, which is of2 2 ⎟⎝⎠conjugate form.5. Repeat steps 3 and 4 nreps times.NCS Bayesian AlgorithmFor convenience, the approach is summarized inalgorithmic form:1. Calculate = ln( ).z ilY il( 1)g lΣ ( 1)2. Obtain the starting values and usingtechniques previously discussed in the section,“Traditional Approach and SmoothingSplines.”3. Simulate( b) 2( b – 1)2( b – 1) g l zl , σ l , α ∼ MVN⎛A( α)z l,σ l A( α)⎞ .⎝⎠Note that the same tuning parameter will beused throughout the algorithm. The g’s and Σare defined in equation (3).4. CalculateΣ ( b + 1)ε ( b)⎛Inv-Wishart ,nS ( b)⎞∼⎝ ⎠where( b)( b)ε l = – g l and S ( b)n( b) ( b)′= ε i εi .z li = 15. Summarize the function(s) of the unknownparameters.∑70 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2003

Speeds on Rural Interstate Highways Relative to Postingthe 40 mph Minimum Speed LimitVICTOR MUCHURUZA*RENATUS N. MUSSADepartment of Civil Engineeringand Environmental EngineeringFlorida A&M University—Florida State University2525 Pottsdamer StreetTallahassee, FL 32310ABSTRACTThe relevance of posting the 40 mile per hour (mph)minimum speed limit on the Interstate Highway Systemhas been increasingly called into question sincethe National Highway System Designation Act of1995 repealed the federally sanctioned maximumspeed limit. In this study, data were collected onmajor interstate highways in Florida to evaluatespeed distribution relative to the 40 mph posted minimumspeed limit. The data revealed that the 15thpercentile speed at all sites was 60 mph or above onboth four-lane and six-lane highway sections. Theanalysis showed that the average speed at all siteswas approximately 5 standard deviations above the40 mph minimum. The coefficient of variationranged from 7% to 11%, while the trimmed varianceanalysis showed that vehicles traveling below55 mph contributed insignificantly to the variationin traffic speeds. A comparison of data collectedbefore the speed limit rose from 65 mph to 70 mphshowed that the average speed increased by 5 mph,while the variances did not change significantly. Thecoefficients of variation, however, increased significantly.The results reported here suggest that speedvariability at the lower end of the distribution is notEmail addresses:*Corresponding author V. Muchuruza—vmuchuruza@eng.fsu.eduR. Mussa—mussa@eng.fsu.eduKEYWORDS: Speed limit, speed variation, highways.71

a significant factor in traffic operating characteristicson Florida rural interstate highways.BACKGROUNDThe decades-old practice of posting minimum speedlimits on rural interstates and other limited accesshighways is predicated on the desire to reduce vehicleconflicts caused by speed variability in a trafficstream. The relevance of the 40 mile per hour (mph)posted minimum speed limit found on the InterstateHighway System is increasingly being called intoquestion in light of the National Highway SystemDesignation Act of 1995, which repealed the federallysanctioned maximum speed limit of 65 mph onrural highways. Most states, including Florida, thenraised the maximum speed limits, and by the end of1997, most parts of Interstates 4, 10, 75, and 95 inFlorida posted 70 mph, which is the maximum speedallowed by the Florida state statutes. While the maximumspeed limit fluctuated over time, the minimumdid not and, in Florida, the 40 mph limit was in effectand posted across many sections of rural interstatehighways, even when the U.S. Congress requiredstates to lower the speed limit to 55 mph in 1974.With such a wide (30 mph) gap between maximumand minimum speed limits, it is logical toquestion the relevance of the 40 mph posted minimum.If the review of the current speed distributionshows that the 15th percentile speed is much higherthan the 40 mph posted minimum, perhaps the minimumspeed needs to be increased or rescinded.Also, it is important to know if the continued postingof the 40 mph minimum speed limit results inthe increase in speed variability on rural interstatehighways. A review of traffic operations on sectionsof Florida highways may provide answers to thesequestions.HISTORICAL PERSPECTIVEThe promise behind the posting of minimum speedlimits on interstate highways was to reduce interactionsbetween fast and slow moving vehicles. Manystates based their minimum speed limits on the UniformVehicle Code (UVC) published by theNational Committee of Uniform Traffic Laws andOrdinances (National Committee 1954). The UVCstipulated that minimum speed limits be establishedon highways whenever traffic and engineeringinvestigations concluded that slow-moving vehiclesconsistently impeded the normal flow of traffic onthe highways.Studies showed that, by 1962, many states hadadopted slow speed laws in their statutes in compliancewith the UVC (National Committee 1964).Florida was among the states adopting slow-speedprovision, making 40 mph the minimum on thefour-lane interstate system, the Turnpike, anddefense highways. Basically, the Florida statutesmade it illegal to drive at a slow speed that impedesthe normal and reasonable flow of traffic on ruralhighways.The literature reveals that, in the early 1960s, 41states and the District of Columbia instituted slowspeedlaws in verbatim or significantly conformingwith the UVC, while the remaining 9 states did notadd minimum speed regulations in their codes. LikeFlorida, Georgia and South Dakota statutes explicitlystated that the minimum speed limit was 40mph, while Michigan and North Carolina maintaineda 45 mph minimum speed rule on their interstatehighways.A 2003 survey of minimum speed practices in differentstates conducted for the Florida Department ofTransportation showed that, following the 1995National Highway System Designation Act, 43 statesraised the maximum speed limit on their InterstateHighway System roads (Mussa 2003). However, theposted minimum speed on these systems did notchange. In fact, the survey showed that 14 states stilluse 40 mph minimum speed limit signs, 10 states use45 mph, and 1 state uses 55 mph. Furthermore, thesurvey showed that 25 states do not post minimumspeed limit signs. Some respondents in states that donot post minimum speed limit signs indicated thatslow driving is not a big problem on their highwaysand if a need arose for enforcement, various rules intheir state statutes, such as “impeding traffic flow,”can be used to warn or cite slow drivers.UNDESIRABLE EFFECTS OF SPEEDVARIABILITYPosting a minimum speed limit was and still is motivatedby the desire to reduce speed variability in atraffic stream and its attendant consequences in effi-72 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

ciency and safety of traffic operations. Numerousstudies have documented the negative effects ofspeed variability.In determining the extent to which the 55 mphfederally sanctioned maximum speed limit affectedsafety, a Transportation Research Board (TRB)study found that the probability of crashes occurringincreases as the speed variance rises. The studyshowed that speed variation causes significant lanechanging and passing maneuvers, which are knownto be potential sources of conflicts and crashes (TRB1984). The significance of speed variance wasobserved by developing a fatality model thatincluded highway safety characteristics such as trafficdensity, percentage of vehicles exceeding 65 mph,percentage of teenagers, and enforcement activity, aswell as speed variance and average speeds. The TRBmodel revealed that speed variance had a statisticallysignificant effect on fatality rates—states withwider variances in vehicle speed on the highwaytended to have higher fatality rates. The study furtherfound that the mean speed only affected theseverity of crashes. Holding the effect of speed varianceconstant in the model presented no statisticallysignificant relationship between the fatality rate andany other speed variables. The study concluded thatcontrolling speed variance could be an effective toolin improving highway safety.Another study of 36 crashes that occurred onIndiana highway 37 indicated that the crashinvolvement rates per million vehicle-miles of travelwere higher for vehicles whose speeds were belowand above the mean speed (West and Dunn 1971).After removing data on all crashes related to turningmaneuvers, the authors found that the crash riskassociated with vehicles traveling faster or slowerwas more than six times the involvement rates at themean speed. The West and Dunn findings were supportedby Hauer (1971) who developed a mathematicalmodel to correlate accident involvementrates and vehicle travel speeds. Hauer found that theimposition of a minimum speed limit on highwayswas two to three times as effective as an equivalentmaximum speed limit in reducing the frequency ofovertaking and thereby crash involvement rates.Hauer suggested that the relationship between vehiclespeed deviations and crashes might be due to ahigher incidence of passing maneuvers from whichthe vehicle passes or is passed by another vehicle—asituation caused by the presence of slower vehiclesimpeding fast vehicles in the traffic stream.Lave (1985) found that the major highway safetybenefits obtained after the enactment of the 1974National Maximum Speed Limit Act—whichreduced the maximum speed limit on interstatehighways to 55 mph—were due to the reduction ofspeed variance rather than average speed. Theauthor argued that a reduction in speed variancewas realized because speed differences between slowand fast moving vehicles were reduced enough tocause a uniform flow of traffic on interstate highways.Thus, with small speed variances there arefewer passing and overtaking maneuvers, eventuallyleading to the reduction in the potential for conflictsand crashes. Lave concluded that slow drivers arejust as dangerous as fast drivers and thus postingminimum speed limits is desirable so as to reducespeed variance in a traffic stream.RESEARCH AGENDA FOR A MINIMUMSPEED LIMITThe posting of higher maximum speed limits onrural interstate highways necessitates an evaluationof the relevance of posted minimum speed limitsigns that existed prior to raising the maximumspeed. Some studies (e.g., West and Dunn 1971;Hauer 1971; and Lave 1985) documented that postingthe minimum speed limit has the beneficial effectof smoothening traffic flow by removing perturbationscaused by speed differences.While evidence obtained from past researchshows that vehicle speed variability contributes tocrashes, it is a big and unsubstantiated leap to saythat posting 40 mph minimum speed limit signs ona highway with a 70 mph maximum speed limit, asis the case on the Florida rural Interstate HighwaySystem, contributes to large differences in vehiclespeeds. The effect of the 40(min)/70(max) seemingmismatch can be evaluated through a carefullydesigned field study in which driver characteristicsand the resulting operating speeds are observed overa long period of time on highway sections with similargeometrics and traffic characteristics but withsome having the 40 mph minimum posted and oth-MUCHURUZA & MUSSA 73

ers not having the minimum posted. Furthermore,knowing whether the minimum speed limit shouldbe increased above 40 mph and by how much, giventhat the maximum speed limit has been raised from65 mph to 70 mph, would also be useful. To obtainthis information, a study would require experimentalhighway sections with the desired minimumspeed limit signs posted.This study aimed at evaluating operating speedcharacteristics on the Florida Interstate HighwaySystem where 40 mph minimum speed limit signsare posted. It would have been desirable to conducta study designed as described above but control siteswith no minimum speed limit signs were not available.An experimental site with 40 mph minimumspeed signs removed or covered can be created forconducting a longitudinal study where both operationaland traffic crash data are collected and latercompared with the current conditions. However,creating such sites has legal implications that are difficultto resolve at this time. Thus, this study waslimited to the following: determining how speedcharacteristics deviate from the 40 mph limit, anddetermining the speed variability that resultedbefore and after the limit was raised.Note that the relevance of the 40 mph minimumspeed limit is analyzed in this paper from the operationalstandpoint only. Certainly, law enforcementpersonnel would prefer to have these signs erectedto provide support for warning or citing slow movingdrivers; the “impeding traffic flow” criterionmay be less useful for enforcement purposes.STUDY SITESThere are four interstate highways in Florida, Interstates4 and 10, oriented in the east-west direction,and Interstates 75 and 95, which go in a northsouthdirection. In addition, the Florida Turnpike isa tollway from central to south Florida oriented inthe north-south direction.Site selection targeted rural sections of theseroads where minimum speed limit signs are posted.The established site selection criteria requiredchoosing sites where the geometric characteristicsproduced the highest free-flow speed possible, thatis, sites devoid of horizontal and vertical curves, sustainedgrades, or other geometric constraints.Another criterion used was to select sections withtelemetered traffic monitoring stations that collecttraffic flow data on volume, occupancy, and individualvehicle speeds on a 24-hour basis throughoutthe year. The Florida Department of Transportationoperates and maintains these sites. We could notselect a site on Interstate 4 because the Tampa-Orlando-Daytona Beach corridor, through whichthis highway runs, is heavily congested throughoutthe week with few periods in which free-flow speedsare attainable. Table 1 shows the study sites selectedbased on the established criteria discussed above.DATA COLLECTIONAs part of the data collection strategy, the projectteam drove through the entire Interstate HighwaySystem to observe geometrics and traffic operatingconditions. In addition, the project team evaluatedover 320 telemetered traffic monitoring sites todetermine their locational suitability in relation tothe research objective of evaluating speed characteristics.The field review resulted in choosing sitesdescribed in table 1. The elements of the datacollectionplan including the data quality checks areexplained below.Individual Vehicle RecordsTelemetered traffic monitoring stations use loopdetectors that provide individual vehicle records composedof the exact time of passage of a vehicle, itsspeed, the lane of passage, the number of axles andaxle spacing, vehicle length, and, in some stations, anindividual vehicle’s axle weight. A cursory review ofthe speed characteristics at most sites indicated thatthere were minor differences between weekend andweekday traffic speed distribution. Thus, data fromall sites were collected on weekdays in good weatherconditions and dry pavement. The integrity of thedata was verified by checking for errors.Data Error ChecksLogic checks on the recorded data elements wereapplied to the raw data files downloaded from thecount stations. Typical data errors includedimproper recording of speeds and loop failures onsome lanes. We set an initial criterion that if more74 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 1 Description of the Study SitesSite codeHighwayNumber oflanesFDOTdistrict County MilepostGeographicallocation320 I-75 6 2 Columbia 22.4 Between I-10 andUS 909904 I-75 6 2 Alachua 3.0 3 miles north of theMarion County line9905 I-95 6 2 Duval 4.4 2 miles south of theI-295 interchange9901 I-10 4 3 Jefferson 18.2 1 mile east of CountyRoad 2579928 I-10 4 3 Walton 10.3 1.3 miles west of BoyScout Road351 I-75 4 1 Collier 41.5 At EvergladesBoulevard overpass9919 I-95 4 7 Brevard 9.9 3.5 miles south ofState Route 5149932 FloridaTurnpikeKey: FDOT = Florida Department of Transportation.4 8 Osceola 30.2 North of the CountyRoad 525 underpassthan 5% of the data were bad, the dataset for thewhole day was discarded and another day’s datawere downloaded. The accuracy of individual vehiclespeeds was checked by relating vehicle lengthand its corresponding recorded speed. When thelength of the vehicle was missing, it was assumedthat the vehicle did not cross both loops in the speedtrap thus suggesting that the recorded individualvehicle speed was erroneous. The number of recordswith missing speed or length was used to check thepercentage of usable counts with respect to raw dataelements and finally to decide whether the data forthat particular day was within acceptable limitsneeded for further analysis.Next, outliers were removed prior to performingthe statistical analyses. All outliers, defined as datapoints that were inconsistent with the general trendof the data elements, were eliminated by a computerprogram developed for this purpose. The computerprogram discarded data points showing zero speedor speed greater than 120 mph (the maximum speedvalue the equipment can record). The time slots inwhich data were discarded were coded as missingdata. In all datasets used for further analysis, thepercentages of data coded as missing were less thanone. All individual vehicle records were then summarizedper hour and per lane and the required volume,speed, and headway statistics were calculated.Analyses proceeded only after assuring the dataquality through these error checks.VOLUME ANALYSISUnder low to moderate traffic congestion, asdemand on travel lanes increases so does the need offast moving vehicles to pass slow moving vehicles.The combination of passive and active passingmaneuvers creates the potential for conflicts in thetraffic stream. Higher operating speeds are generallyattainable at level of service (LOS) A 1 and continuallydecrease as the speed-volume relationshipmoves toward congested flow conditions. An hourby-hourvolume analysis of the 24-hour dataset wasconducted at all eight sites to determine the volumedistribution across the travel lanes, the percentageof trucks on each lane, and the minimum and maximumvolumes and their hour of occurrence. Thetraffic volumes were expressed on a per-lane basis,because, in general, volume varies by lane. The averageannual daily traffic, which is the gross indicatorof traffic activity, usage, and need, was estimated bymultiplying the 24-hour recorded volume with the1 LOS classifies the quality of operation provided by theroadway from A through F, with “A” representing themost favorable driving conditions and “F” the worst,measured at the peak hour period of the day (USDOT2000).MUCHURUZA & MUSSA 75

adjustment factors developed by the Florida Departmentof Transportation Statistics Office. Table 2shows the results of the volume analysis.Table 2 presents the results categorized by thenumber of lanes on the highway (i.e., four or sixlanes) and by direction of travel. Examination of thehourly variation at each site showed that thedemand volumes were at their lowest from midnightto dawn hours, while the peak-hour demandoccurred in the afternoon, typically from 3 p.m. to 5p.m. with a few exceptions. The lane distributionanalysis for the six-lane highway sections showedthat flow rates in the middle lane were typicallyhigher than on shoulder and median lanes. On fourlanesections, the flow rates on the shoulder laneswere higher than on the median lanes.We also analyzed the distribution of trucks ineach lane. Vehicles traveling at the low end of thespeed distribution tended to be trucks, recreationalvehicles, and vehicles towing trailers. Table 2 showsthat truck percentages are higher on the shoulderlanes in both four-lane and six-lane sections. Notethat on Sites 320 and 9904 on Interstate 75 in northFlorida trucks are not allowed to travel on themedian lane of three-lane (in one-direction) sections(i.e., they can only use the two outermost lanes).A comparison of the peak-hour and 24-hourtruck percentages suggests that more trucks travelduring the offpeak hours. The need to change lanesand to pass some slow moving vehicles—typicallytrucks and RVs—is high during offpeak hours. TheLOS in most of the sections was B or better duringthese time, thus operating speeds tend to be highdue to fewer traffic interactions. With trucks andRVs typically among the slower moving vehicles,changing lanes and passing, resulting from the speedvariances, might be a concern.SPEED ANALYSISThe analysis of speed is presented in two parts. Thefirst part of the analysis details the central tendencyof the speed data while the second part looks at thespeed variability in the traffic stream. The analysisof both measures of center and dispersion takes intoaccount the demand volume, lane of travel, and thetype of vehicles—passenger cars or trucks—in thetraffic stream.Central Tendency AnalysisFigure 1 shows the 24-hour mean speed of all vehiclescategorized by facility type (i.e., four-lane or sixlanehighway). Examination of the graphs in figure1 reveals that average speeds of vehicles vary fromshoulder to median lanes with median lanes experiencinghigher average speeds. At four-lane sites, theaverage speeds ranged from 66 mph to 74 mph inshoulder lanes and 67 mph to 85 mph in medianlanes. At six-lane sites, the average speeds of thevehicles on the shoulder, middle, and median lanesranged from 67 mph to 70 mph, 72 mph to 75mph, and 75 mph to 81 mph, respectively.Pairwise comparisons of the average speedsusing a t-test showed that, on four-lane sections,average speeds differed significantly betweenshoulder and median lanes (p = 0.0002). Furtheranalysis showed that the average speeds were significantlydifferent between shoulder-middlelanes and middle-median lanes on six-lane sections(p 0.0001 and p 0.0001). These resultsconfirm that slow-moving vehicles generally usethe shoulder lanes while fast moving vehicles usethe median lanes. At the prevailing LOS, it seemsthat the influence of traffic intensity was not a significantfactor, because at six-lane sites the middlelanes carried higher volumes than shoulder lanesyet they had higher average speeds. To furtherunderstand the profile of speeds at these highwaysections, table 3 presents the overall 24-hourmean speeds by lane and vehicle type. Table 3 alsoshows the harmonic mean speeds weighted bylane volumes and by vehicle type. The harmonicmean speeds were calculated as follows:u 1∑v liv li∑ -----u li∑v tj= ------------ and u 2 = ------------(1)whereu = the harmonic mean speed weighted by the124-hour lane volume in lane i,u 2 = the harmonic mean speed weighted by 24-hour vehicle type j volume,u li = the 24-hour mean speed of all vehicles inlane i,v li = the total 24-hour volume in lane i,u tj = the 24-hour mean speed of all vehicles oftype j, and= the total 24-hour volume of vehicle type j.v tjv tj∑ -----u tj76 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 2 Results of the Volume AnalysisSite320(I-75)9904(I-75)9905(I-95)9901(I-10)9928(I-10)351(I-75)9919(I-95)9932(TNPK)Directionof travelNBSBNBSBNBSBWBEBWBEBWBEBNBSBNBSBLaneof travelMin. hourlyvolume and timeof occurrenceMax. hourlyvolume and timeof occurrence%trucks(peak hr)%trucks(24-hr)Six-lane sitesShoulder 19545841 51Middle 144 4 – 5 a.m. 761 3 – 4 p.m. 17 21Median 17 389 3 3Shoulder 11949641 47Middle 99 3 – 4 a.m. 851 2 – 3 p.m. 17 19Median 12 459 2 3Shoulder 12868238 51Middle 109 2 – 3 a.m. 1,015 3 – 4 p.m. 11 18Median 13 576 1 2Shoulder 14549052 54Middle 74 1 – 2 a.m. 73311 a.m. –12 p.m.23 20Median 9 376 2 2Shoulder 16264021 38Middle 102 2 – 3 a.m. 1,198 7 – 8 a.m. 12 21Median 12 601 2 6Shoulder 17774418 36Middle 95 3 – 4 a.m. 1,179 5 – 6 p.m. 7 18Median 5 630 3 6Four-lane sitesShoulder 12075630 372 – 3 a.m.5 – 6 p.m.Median 12 224 15 15Shoulder 10923428 393 – 4 a.m.3 – 4 p.m.Median 16 523 9 16Shoulder 7347925 342 – 3 a.m.3 – 4 p.m.Median 9 224 15 16Shoulder 8246828 322 – 3 a.m.1 – 2 p.m.Median 8 184 10 19Shoulder 36424 11 a.m. – 15 201 – 2 a.m.Median 3 165 12 p.m. 2 5Shoulder 38439 11 a.m. – 14 203 – 4 a.m.Median 2 149 12 p.m. 4 5Shoulder 6556445 271 – 2 a.m.4 – 5 p.m.Median 11 451 7 12Shoulder 7962830 321 – 2 a.m.3 – 4 p.m.Median 7 457 11 14Shoulder 61486 11 a.m. – 15 164 – 5 a.m.Median 11 262 12 p.m. 5 9Shoulder 11961612 213 – 5 a.m.1 – 2 p.m.Median 20 399 6 10PeakhourLOSAABBBBAAAAAAAAAAAADT51,06564,17264,28425,62718,72819,04733,91727,163Key: AADT = average annual daily traffic; EB = eastbound; LOS = level of service; NB = northbound; SB = southbound; WB = westbound.MUCHURUZA & MUSSA 77

FIGURE 1 Hourly Variations of Average Lane Mean SpeedsShoulder lane: 4-lane highway sectionMean speed (mph)75Shoulder lane: 6-lane highway sectionMean speed (mph)757070659901351993299289919600 3 6 9 12 15 18 21Time of day65600 3 6 9 12 15 18 21Time of day32099049905Middle lane: 4-lane highway sectionMiddle lane: 6-lane highway sectionMean speed (mph)8075No middle lane706532099049905600 3 6 9 12 15 18 21Time of dayMedian lane: 4-lane highway sectionMean speed (mph)85Median lane: 6-lane highway sectionMean speed (mph)85807575659901 9928351 99199932550 3 6 9 12 15 18 21Time of day706532099049905600 3 6 9 12 15 18 21Time of day78 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 3 Mean Speed CharacteristicsSite code(highway)320(I-75)9904(I-75)9905(I-95)9901(I-10)9928(I-10)351(I-75)9919(I-95)9932(Turnpike)Directionof travelNBSBNBSBNBSBWBEBWBEBWBEBNBSBNBSBTravellane24-hourpassenger carlane volumeVehicletype-basedmean speedLane-based24-hourtruck volumemean speedu 1 u 2Six-lane highway sectionsTrimmedmeanspeedAveragemeanspeedShoulder 4,003 4,07470 70Middle 8,173 2,202 73 74Median 3,609 99 73 7379 79Shoulder 4,350 3,85069 70Middle 9,109 2,175 75 75Median 4,976 140 74 7480 81Shoulder 4,614 4,43669 67Middle 10,606 2,227 73 72Median 6,512 107 71 7376 76Shoulder 3,814 4,18469 68Middle 8,899 2,058 75 74Median 4,658 78 73 7277 78Shoulder 5,685 3,20968 68Middle 12,288 3,072 73 73Median 5,711 316 72 7278 77Shoulder 7,004 3,76668 68Middle 10,992 2,279 73 73Median 4,471 280 72 7277 77Four-lane highway sectionsShoulder 4,959 2,72973 73Median 2,356 367 74 7478 78Shoulder 4,843 2,84473 73Median 2,254 407 74 7478 78Shoulder 4,551 2,12268 68Median 2,081 266 69 6973 73Shoulder 4,894 2,10770 70Median 1,867 257 70 7172 72Shoulder 4,522 1,09772 72Median 2,087 122 73 7377 77Shoulder 4,399 13274 74Median 2,297 1,103 78 7985 85Shoulder 3,649 3,57470 68Median 5,118 2,640 69 6975 70Shoulder 5,427 3,73068 66Median 3,642 1,440 66 6769 67Shoulder 6,479 1,14270 70Median 3,074 192 72 7275 77Shoulder 7,733 1,40070 71Median 4,415 361 73 7376 78Key: EB = eastbound; NB = northbound; SB = southbound; WB = westbound.MUCHURUZA & MUSSA 79

Table 3 also includes the straightforward averagespeeds of all vehicles and the trimmed mean speeds.The trimmed mean speeds were calculated by discardingthe lowest 15% and the highest 15% ofvehicle speeds. We statistically analyzed the significanceof the difference between the speed types displayedin this table. Pairwise comparisons of u and 1u 2 showed no and slightly significant differences(p = 0.7 and p = 0.08) between lane-based and vehicletype-based mean speeds on both six-lane andfour-lane highway sections, respectively. Statisticalcomparisons between trimmed mean speed andaverage speeds in each lane indicated lack of a discernibledifference in both six- and four-lane sections(p = 0.57 and p = 0.40). The non-existence ofthe difference between trimmed speed and meanspeed shows that the presence of fast and slow movingvehicles in the speed distribution has no significanteffect on the average speeds on these facilities.The average speed of the bottom 15th percentile ofthe vehicles was 62 mph on both facility types,while in the upper 15th percentile, the average speedof vehicles was 81 mph and 83 mph on six-lane andfour-lane sections, respectively.Speed Dispersion AnalysisThe dispersion of speeds was analyzed by lane andvehicle type using the standard deviation, coefficientof variation, and 10-mph pace, which is the 10 mphspeed range with the highest number of observationsof vehicles in the speed distribution. In addition,as is the case in most traffic engineering designand operational analyses, the 85th and 15th percentilespeeds were also calculated. The results follow.Facility Type Speed DistributionWe computed the standard deviations of vehiclespeeds and the corresponding coefficient of variation.The results showed that their values varieddepending on facility type. On six-lane sections, thestandard deviation of speeds ranged between 4 mphand 6 mph, while on four-lane sections the standarddeviations were as high as 10 mph. Specifically, Sites351 and 9919 showed high values of standard deviations—9mph and 10 mph on the median lanes,respectively. The field review revealed that these twosites are on highway stretches that are longitudinallystraight for at least 10 miles.The coefficient of variation, which measures relativedispersions of vehicle speeds from the averagespeed, was also calculated by lane for each site. Thisstatistic was necessary to compare speed variationsby examining the magnitudes of deviation relativeto the magnitude of the mean given that there weredifferent mean speeds grouped by lane. The analysisof the coefficients of variation in each lane showedthat they ranged from 5% to 14%. When coefficientsof variation for adjacent lanes on each sitewere compared, the results showed that the differenceswere less than 2%. These results suggest thatthe scatters of the vehicle speeds from the averagespeed are small. Therefore, the traffic speeds arevery closely clustered about the mean speeds in allsections analyzed.Speed Distribution by Vehicle TypeOn average, the results of the speed distributionanalysis by vehicle type showed that passengercar speeds were higher than truck speeds by atleast 1 mph on both six-lane and four-lane sections.The results further showed that the coefficientsof variation did not differ significantlybetween passenger cars and truck speeds for sixlanehighway sections but were significant onfour-lane sections (p = 0.027). Figure 2 displaysthe results of the speed distribution analysis at thelower end of distribution.With respect to the vehicles traveling at the lowerend of speed distribution (i.e., less than 60 mph), wefound that more trucks on four-lane sections traveledbelow 60 mph than passenger vehicles at Sites9901, 9919, and 9928, while more passenger carstraveled below 60 mph at Sites 351 and 9932. Theresults were also mixed on six-lane highway sections.At Sites 320 and 9904, which are on the samestretch and approximately 70 miles apart, differentpatterns of vehicles traveling below 60 mph wereobserved. While at Site 9904 more passenger carstraveled at speeds below 60 mph, at Site 320 moretrucks traveled below 60 mph. At Site 9905, morepassenger cars than trucks had speeds below 60mph in all lanes.The results further showed that on both four-laneand six-lane sections the percentage of vehicles at80 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

FIGURE 2 Analysis of Vehicles Traveling at Speeds Below 60 mph9876543210% with speedbelow 60 mphShoulder% trucks% carsTruck speedCar speedMiddleMedianSix-lane sectionsShoulderMiddleMedianAverage speed (mph)70ShoulderMiddleMedian320 9904 99059901 9928 351 9919 9932Note: In the median lane on highway 320, no vehicles traveled below 60 mph.60504030201001412108MedianShoulderShoulderMedianShoulderMedianShoulderMedianShoulderMedian6420% with speedbelow 60 mph% trucks% carsTruck speedCar speedFour-lane sectionsAverage speed (mph)706050403020100each site traveling below 40 mph (the posted minimumspeed limit) was approximately zero. In fact,the results showed that at all sites only 1% of thevehicles traveled below 55 mph. Both passenger carsand trucks averaged speeds below 60 mph butabove 55 mph on six-lane sections. On four-lanesections, the speed of vehicles traveling below 60mph averaged above 54 mph.Percentile and Pace CharacteristicsTable 4 displays the 15th and 85th percentilespeeds in each lane, 10 mph pace speeds, and thepercentages of vehicles within the pace. Analysisof percentile speeds showed that, in four-lane andsix-lane sections, the 85th percentile speedsranged from 71 mph to 94 mph and 73 mph to86 mph, respectively, while the 15th percentilespeeds ranged from 60 mph to 77 mph and 62mph to 76 mph, respectively, depending on thelane of travel (i.e., median lanes had higher percentilespeeds than shoulder lanes). Of significantinterest was the 15th to 85th percentile range,because it represents the proportion of vehiclestraveling close to the mean speed. At the six-lanesites, the percentile speeds ranged from 7 mph to10 mph, 8 mph to 10 mph, and 10 mph to 12mph on the median, middle, and shoulder lanes,respectively. The ranges for four-lane sites were 7speed was about 65 mph, 25 mph above the postedmph to 11 mph and 11 mph to 12 mph on themedian and shoulder lanes, respectively.Note that the results from Sites 351 and 9919do not particularly follow the trend of other sitesbecause of the somewhat large differencesbetween percentile speeds at the two sites—14mph and 19 mph, respectively. These differencescould result from the straightness of the segmentsas well as a low volume of traffic that induceshigh-speed travel by some drivers. Furthermore,these two sites also showed the highest values ofstandard deviations. Table 4 further details thatthe paces ranged from the mid-60s to the mid-80son both facility types with shoulder lanes experiencinglower pace speeds. The results in table 4show that there is no direct relationship betweenthe number of lanes on a highway and pacespeeds.Trimmed Variance AnalysisA trimmed variance analysis determined the contributionof slow- and fast-moving vehicles on overallspeed variation. Using five different scenarios, vehiclestraveling slower than 40 mph, 45 mph, 50 mph,55 mph, and 60 mph were removed from thedataset when calculating the variance. The resultingspeed variances from these trimming processes werethen compared. At all sites, the 15th percentileminimum speed of 40 mph.MUCHURUZA & MUSSA 81

TABLE 4 Percentile and Pace Speed CharacteristicsSite(highway) Direction Lane320(I-75)9904(I-75)9905(I-95)9901(I-10)9928(I-10)351(I-75)9919(I-95)9932TurnpikeNBSBNBSBNBSBWBEBWBEBWBEBNBSBNBSB15thpercentilespeed (u 15 )85thpercentilespeed (u 85 )Six-lane freeway sections10 mphpaceVehiclesin pace(%)Shoulder 64 76 66 – 76 66Middle 69 79 70 – 80 76Median 75 83 74 – 84 76Shoulder 63 75 66 – 76 66Middle 70 80 71 – 81 78Median 76 86 76 – 86 70Shoulder 62 73 65 – 75 68Middle 69 77 67 – 77 77Median 73 80 71 – 81 76Shoulder 64 74 65 – 75 77Middle 71 79 68 – 78 75Median 74 82 70 – 80 70Shoulder 63 74 63 – 73 68Middle 68 78 68 – 78 72Median 72 83 74 – 84 60Shoulder 63 73 63 – 73 70Middle 69 78 70 – 80 78Median 73 81 74 – 84 72Four-lane freeway sectionsShoulder 67 78 69 – 79 69Median 73 82 73 – 83 75Shoulder 67 78 69 – 79 70Median 73 82 73 – 83 72Shoulder 62 73 64 – 74 66Median 69 78 68 – 78 77Shoulder 65 75 66 – 76 69Median 68 77 67 – 77 77Shoulder 65 79 69 – 79 62Median 70 84 72 – 82 59Shoulder 67 81 70 – 80 57Median 77 94 82 – 92 52Shoulder 63 74 67 – 77 60Median 60 79 72 – 82 49Shoulder 62 72 64 – 74 63Median 64 71 64 – 74 63Shoulder 64 76 65 – 75 66Median 72 81 72 – 82 70Shoulder 65 77 68 – 78 60Median 73 84 73 – 83 70Key: EB = eastbound; NB = northbound; SB = southbound; WB = westbound.82 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

The results showed no discernable contributionto speed variance for vehicles with a speed of lessthan 55 mph, primarily because very few vehicles ateach site traveled at speeds less than 55 mph. In fact,at each site vehicles with speeds under 55 mph madeup 1% of those recorded, while the percentage ofvehicles with speeds of less than 40 mph was negligible(i.e., 0.15%). Although the contribution to thestandard deviation of vehicles with speeds less than55 mph is very minor, the safety implications of thepresence of vehicles with very low speeds cannot beignored. Even though only a few vehicles causespeed differential conflicts, these vehicles could be acontributory factor in crashes.PLATOON ANALYSISHighway travel is generally composed of freeflowingand platooned vehicles. In free-flowing traffic,drivers can choose their speeds as they desire aslong as conditions are such that slow-moving vehiclesdo not impede their ability to change lanes atwill. Platooned vehicles travel close to each othermostly because of lack of passing opportunities,thus causing other vehicles to be trapped behind thelead vehicle. No definition exists in the literature ofa headway value below which vehicles are consideredto be moving in a platoon. Thus, in this study,four definitions were considered—less or equal to 1,2, 3, and 4 seconds.The analysis showed that six-lane highway sectionscarried larger proportions of platooned vehiclesthan four-lane sections. Further, the middlelanes of six-lane sections carried more platoons thanthe shoulder and median lanes. To study the effectof platooned vehicles on the distribution of speed,the mean speeds of platooned vehicles were comparedwith the mean speeds of nonplatooned (orfree-flowing) vehicles. The statistical analysis hereuses a t-test in which platooned and nonplatoonedvehicles were paired by site and by lane of travel.The results showed that the difference between thespeeds of platooned and nonplatooned vehicleswere insignificant for both four- and six-lane highwaysections regardless of whether the cut-off pointwas 1, 2, 3, or 4 seconds of time headway. Theseresults indicate that platooned vehicles are not slowmoving and thus do not create a need for freeflowingvehicles catching up behind them to pass.However, it should again be noted that the highwaysections analyzed were relatively uncongested, operatingat levels of service B or better for a majority ofthe hours in a year.BEFORE AND AFTER COMPARISONTo understand the change in speed characteristicsfollowing the increase in the speed limit, table 5 presentsa comparison of before-and-after data. In1996, the speed limit was 65 mph at all the sitesindicated in the table. Data-collection sites for both1996 and 2002 were physically very close, and thefield review of the sites indicated that for all practicalpurposes the geometric characteristics prevailingat these sites would produce similar driver behavior.The results in table 5 show that the averagespeeds across all sites increased by 5 mph to 72mph. The 15th percentile speed also showed a significantincrease of 3 mph when averaged across allsites (p 0.0001). A statistical F-test comparison ofthe variances indicated no significant differencebetween the 1996 and 2002 data (p = 0.50). However,significant differences were found in the varianceson four-lane sections (p = 0.0003). Furtheranalysis indicated that in 1996, the average speedon six-lane sections was 4.75 standard deviationsabove the 40 mph minimum posted speed limit. In2002, it was 5 standard deviations above the 40mph minimum. In four-lane sections, the resultsshow that the average speeds were 6 and 5 standarddeviations above 40 mph in 1996 and 2002, respectively.Examination of the coefficients of variationbetween the two datasets indicated that 2002 datashow significant large variations compared with1996. However, the coefficients of variation are stillbelow 10%, indicating a reasonable equity in travelspeeds.DISCUSSION OF RESULTSThis paper presents a review of traffic operatingcharacteristics on rural interstate highways in Florida.Using various analytical techniques, we determinedspeed characteristics in relation to the postedminimum speed limit of 40 mph. Our intent was toexamine the relevance of the 40 mph minimumMUCHURUZA & MUSSA 83

TABLE 5 Comparison of Before-and-After Speed DataHighwayI-75I-95I-75I-10MeanspeedLocation, direction, and year(mph)Six-lane highway sectionsStandarddeviation(mph)Coefficientof variation(%)15 th percentilespeed (mph)Between I-10 & CR136, NB, 1996 66 4 6 63Site 320, NB, 2002 73 6 8 67Between I-10 & CR136, SB, 1996 66 5 8 61Site 320, SB, 2002 74 7 9 66Between CR234 & SR21, NB, 1996 68 5 7 63Site 9904, NB, 2002 71 6 8 64Between CR234 & SR21, SB, 1996 67 5 7 63Site 9904, SB, 2002 71 6 8 64Between CR210 and I-295, NB, 1996 67 4 6 64Site 9905, NB, 2002 72 7 10 65Midpoint CR210 and I-295, SB, 1996 63 6 10 60Site 9905, SB, 2002 72 6 8 65Near Flagler CL, NB, 1996 69 4 6 65Site 9905, NB, 2002 72 7 10 65Near Flagler CL, SB, 1996 64 5 8 63Site 9905, SB, 2002 72 6 10 65Four-lane highway sectionsAt mile marker 89, WB, 1996 66 4 6 61Site 351,WB, 2002 74 7 9 66At mile marker 89, EB, 1996 68 6 9 63Site 351, EB, 2002 78 9 11 68Overpass E. of SR 85, WB, 1996 67 4 6 64Site 9901, WB, 2002 74 6 8 68Overpass E. of SR 85, EB, 1996 69 4 6 64Site 9901, EB, 2002 74 6 8 68C-280 overpass, WB, 1996 68 5 7 65Site 9901, WB, 2002 74 6 8 68C-280 overpass, EB, 1996 67 4 6 64Site 9901, EB, 2002 74 6 8 68Between SR257 & US221, WB, 1996 67 5 7 62Site 9928, WB, 2002 70 6 9 66Between SR257 & US221, EB, 1996 69 5 7 65Site 9928, EB, 2002 71 5 7 68East end of Aucilla River, WB, 1996 67 5 7 62Site 9928, WB, 2002 70 6 8 66East end of Aucilla River, EB, 1996 69 4 6 64Site 9928, EB, 2002 71 5 7 68Key: EB = eastbound; NB = northbound; SB = southbound; WB = westbound.84 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

speed limit in light of the increase in the maximumspeed from 65 mph to 70 mph.It is clear from the analysis that raising the speedlimit increased average speeds on rural interstatehighways. The comparison of 1996 data with 2002data showed that average speeds rose by 5 mph,which is the same amount of the speed limitincrease. The comparison further showed a slightincrease in the coefficient of variation after the maximumspeed went up; however, the increase is statisticallyinsignificant and under 10%, a threshold thatcan be considered to indicate uniform operations. Inaddition, the 15th percentile speed showed anincrease of 3 mph when averaged across all sites. Inrelation to the 40 mph posted minimum speed, the2002 average speed on all sections was 5 standarddeviations above this minimum speed, comparedwith 5.4 standard deviations for the 1996 data.In light of the above data and analyses, from atraffic operations standpoint, several questionsarise: Is the practice of posting the 40 mph minimumspeed irrelevant or is it successful in ensuringthat vehicles do not travel below 40 mph? Shouldthe 40 mph posted minimum speed limit bescrapped or should it be raised to a higher value?What should that value be? These are importantquestions that could not be adequately answeredthrough the research paradigm reported here. However,the data reveal a few pointers.First, the 40 mph posted minimum speed limitprobably does not have a significant influence ondriver behavior given that the number of vehiclestraveling below 55 mph at all sites was negligible(i.e., 1%). If these signs influenced drivers, wewould expect a higher percentage of vehicles totravel at speeds in the 40 mph to 50 mph range, as isthe case on the higher side of the speed distributionwhere a large percentage of drivers maintain speedsbetween 70 mph and 80 mph.It has been suggested in the past (e.g., McShaneet al. 1998) that the 15th percentile speed may beused as a measure of the minimum reasonable speedfor the traffic stream. (This suggestion mirrors theattempt to use the 85th percentile speed as a measurefor setting the maximum speed limit). The datareported here indicate that, in all sections studied,the 15th percentile speeds on the aggregate rangedfrom 60 mph to 70 mph, which is 20 mph to 30mph above the posted minimum speed limit value.Does this mean that the minimum speed limitshould be set at 60 mph? There are number of concernsthat would need to be addressed before achange like this could be made. First, Florida statutes(Florida Statutes 2002) state that “no schoolbus shall exceed the posted speed limit or 55 mph.”Second, as a tourist state, some Florida visitors driverecreational vehicles (sometimes towing a trailer) ormotor homes, and field review indicated that theseare the vehicles that tend to make up the lowest15% of the speed distribution at all sites. Third, asafety analysis would be needed to fully justify anychange in the minimum highway speed.Instead of increasing the minimum speed, shouldit be eliminated? After all, the results of a surveyconducted as part of this research showed that 25states do not post minimum speeds on interstatehighways. Currently, Florida statutes state that:“The minimum speed limit on interstate andDefense Highways, with at least 4 lanes, is 40mph.” The Florida Highway Patrol in the context ofthis research study indicated that such a statute isrequired to enable law officers to issue citations. Aquestion was raised that in the absence of the minimumspeed rule, can the law officers use anotherFlorida statute that states “No person shall drive amotor vehicle at such a slow speed as to impede orblock the normal and reasonable movement of traffic”to warn or issue citations to slow moving vehicles?One police officer pointed out that if a vehicleis alone on the highway traveling at, say 25 mph,what traffic is the driver impeding?RECOMMENDATIONSFurther research is needed to ascertain the effect ofthe current posted minimum speed limit on driverbehavior. While the data seem to indicate that the40 mph minimum speed might not be that relevantbased on prevailing operating speed distributions, itis not clear what the effect would be if the signswere removed from rural interstate highways. Theanswer to most of the questions raised aboverequires field evaluation, as simulation analysiswould not appropriately depict driver behavior onMUCHURUZA & MUSSA 85

oadways with and without posted minimum speedlimit signs.Additional research that is planned includes collectingdata on interstate highway sections in statesthat do not have minimum speed limits posted buthave similar geometric and driver characteristics. Acomparison of multistate data might shed somelight on the relevance of posting minimum speedlimit signs. Multistate data would also be of interestto traffic engineers who want to compare safetycharacteristics on sites with and without postedminimum speed limits.REFERENCES2002 Florida Statutes. Title XXIII, Chapter 316, section 183(3).Hauer, E. 1971. Accidents, Overtaking, and Speed Control.Accident Analysis and Prevention 3:1–13.Lave, C. 1985. Speeding, Coordination, and the 55 mph Limit.American Economic Review 75:1159–1164.McShane, W.R., R.P. Roess, and E.S. Prassas. 1998. TrafficEngineering. Upper Saddle River, NJ: Prentice-Hall.Mussa, R. 2003. Nationwide Survey of the Practice of PostingMinimum Speed Limit Signs on Interstate Highways,manuscript.National Committee on Uniform Traffic Laws and Ordinances.1954. Uniform Vehicle Code. Washington, DC.______. 1964. A Comparative Survey Based on the UniformVehicle Code. Traffic Laws Annual 1.Transportation Research Board (TRB). 1984. Special Report204: 55: A Decade of Experience. Washington, DC:National Research Council.U.S. Department of Transportation (USDOT), Federal HighwayAdministration. 2000. Highway Capacity Manual. Washington,DC.West, L.B. and J.W. Dunn. 1971. Accidents, Speed Deviationand Speed Limits. Traffic Engineering 41:52–55.86 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

Book ReviewsReviews that appear in JTS describe and assessbooks about new developments in statistics,economics, the environment, or engineeringresearch that focus on transportation issues.The topics can be theoretical, empirical applications,or methodological innovations.Suggestions of books for review are welcomed,as are requests to become a bookreviewer. Please contact the Book Review Editor:Prof. Vincent W. Yao2801 S. University AvenueInstitute for Economic AdvancementUniversity of Arkansas at Little RockLittle Rock, AR 7204-1099USA(501) 569-8453; wxyao@ualr.eduTransportation Labor Issues andRegulatory ReformJames Peoples and Wayne Talley, editorsElsevierNovember 2004, 179 pagesISBN 0-7623-0891-5www.elseviersocialsciences.com$95 € 86 £57.50Transportation Labor Issues and RegulatoryReform, volume 8 in Elsevier’s Research in TransportationEconomics series, is a pleasant surprise ina couple of ways. First, edited volumes often end upbeing mélanges of contributions united by littlemore than their bindings. Articles in this volume,however, cover the topic well and in a logical order.In a refreshingly concise introduction, the editorslay out the rationale for the book, its organization,and the main points of the chapters and how theyrelate to one another. Second, the book is worthwhile.As one of the semi-unemployed “soldiers” inthe struggle that led to transport deregulation, Iappreciate efforts to breathe life into the subject,although I begin to question the relevance of anevent older than most of my students and some ofmy clothes. But the volume analyzes a range ofissues that, in many cases, have continuing importancewithin transportation. Of perhaps even moresignificance, the volume is a rich case study of theimpacts of regulatory changes on labor and managementcompensation and employment levels, aswell as working conditions. As such, it would be ofvalue to researchers and practitioners in transportation,human resource management, safety, andindustrial organization.The book may be thought of as being in threesections: 1) safety; 2) employment, productivity, andworking conditions; and 3) compensation. All threesections have merit. If they had to be ranked, safetyis the strongest and compensation the weakest.Safety: Chapters 2 and 3In a 23-page tour de force, Ian Savage examinestrends in injuries since deregulation in trucking, railroads,and airlines. His command of the issues anddata is impressive. Of particular importance, hemakes the almost always neglected point that crashdata alone provide an incomplete picture of workersafety. In trucking and railroads, transportationaccidents account for only 12% of lost workdaysand a mere 4% of those in the airline industry.Safety trends are examined using a variety of measures,such as lost workdays per full-time employeeand per unit of output. Comparisons are providedwith either all private industry or manufacturing.In the next chapter, Daniel Rodriguez et al.explore relationships between, on the one hand,motor carrier financial performance, firm size andtype of operations, and driver payment methodwith, on the other hand, driver safety. Not surprisingly,the sometimes confounding effects of theserelationships, as well as data limitations, sap thestrength of the results. But the authors do reach87

some important conclusions and the study is ofvalue as an example of a very competent empiricalinvestigation of complex issues.Employment, Productivity, and WorkingConditions: Chapters 4, 5, and 6Kristen Monaco and Dale Belman examine theimpacts of technology on working conditions fortruck drivers. The study presents considerable informationabout the types of technologies in use, whouses them, and to what extent. Most interesting aretheir findings with regard to the ability of satellitebasedsystems to substitute for driver experienceand to increase revenue-miles.Next, Nancy Johnson and Jonathan Andersonexplore employment, productivity, and workingconditions in airlines following deregulation. Thepiece is a rich source for data about airline profitability,bankruptcies, and output. Some of this couldhave been relegated to appendices, though all of usenjoy checking out when our favorite or most hatedcarrier went belly up. The authors clearly lay outhow swings in employment and productivity relateto technological innovations, such as the hub-andspokesystem, and the health of the overall economy.The analysis of changes in working conditions,though, seems to be trying a bit too hard to make acase for deterioration. For example, Johnson andAnderson show that between 1970 and 2001,average weekly hours worked by pilots increasedfrom 29 to 42. While acknowledging that airlineaccident rates steadily declined over this period andthat 42 hours may not seem a very high number,they point out that pilots may suffer from jet lagand that even slight fatigue may result in catastrophicmiscalculations. They do not mention,however, that aircraft in 2001 automatically tookcare of many more pilot tasks than was the case in1970. Suggestive of increasing strain on airlineattendants, the authors point out that between 1995and 2000 incidences of crews having to enforce disciplineon unruly passengers rose from 140 to 266.They fail, however, to put these statistics into anycontext, that in 1995 there were 0.00002 such incidentsper plane departure, versus 0.00003 in 2000.Daniel Rich’s investigation of productivity, technologychanges, and labor relations is the best of thissection. Incorporating rail, air, truck, and watertransport, Rich explores the interplay of labor-savingand labor-using technologies on the sources of productivitychanges and employment and compensationlevels. The discussions of the setting, relevanttheories, data, empirical approaches, and results areall first rate. It is simply an excellent treatment of anextremely worthwhile, but complex, topic. I recommendit to you and will insist on it for my students.Compensation: Chapters 7, 8, and 9Though not without considerable merit, for tworeasons the weakest contribution in the volume isStephen Burks et al.’s study of executive earnings intrucking in the era of post-deregulation. The first isnot the authors’ fault. Data problems limited theinvestigation to 1977 to 1986, too short a period tosee the full effects, if any, of deregulation.The second reason is another matter. After abrief dip past 1980, the authors find that executivecompensation recovered and, thereafter, increasedcontinually, despite much less rosy compensationtrends for their subordinates. In their conclusionsthey state: “From 1985 onwards they [i.e., truckingexecutives] received pay increases in line with thewider boom in executive pay.” I believe there is asimple explanation that did not enter into theempirical investigation nor discussion given by theauthors. Joe, out on the loading dock or driving atruck, may have few alternatives outside the industryand his salary is pegged to variations in the fortunesof his company. But many of the skills neededfor managing a firm are not industry-specific and, assuch, executive compensation in trucking wouldbe influenced by compensation levels for executivesthroughout the economy. The immediate postderegulationdip in compensation might havereflected a transition from executives with skills ofspecific value under regulation to those better suitedto lead firms in unregulated environments.John Bitzan presents an analysis of compensationlevels for low- and mid-level managers in airlines,trucking, and rail. Similar to Burks et al., Bitzan doesnot account for earning levels outside of a manager’sindustry when examining the impacts of deregulationon their earnings and, like Burks et al., he findslittle or no effect. As if to atone for these “sins,” thebulk of the chapter addresses whether the qualificationsof managers in transport industries changed88 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

This is a statistical book. This is a statistical book.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical b ok. This is astatistical book. This is a statistical book. Alpha is awesome.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is a statisticalbook. This is a statistical book. This is a statistical book.This is a statistical book.This is a statistical book. This is a statistical book.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical b ok. This is astatistical book. This is a statistical book. Alpha is awesome.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is a statisticalbook. This is a statistical book. This is a statistical book.This is a statistical book.This is a statistical book. This is a statistical book.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical b ok. This is astatistical book. This is a statistical book. Alpha is awesome.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is a statisticalbook. This is a statistical book. This is a statistical book.This is a statistical book.after deregulation, how these changes comparedwith those in nontransportation industries, and howmanagers were compensated for their qualifications.These are important questions. Bitzan deals withthem well and gets some intriguing results.The editors, John Peoples and Wayne Talley, finishoff the batting order with an examination ofmotor carrier owner-operators serving port cities.It is a perfect finale for the volume. They examinehow changes in regulations affecting maritimeshipping and advances in containerization impactedone segment of the motor carrier industry. Thestudy is competently done and its results of interest.Of perhaps more significance, their work challengesreaders to consider the broader impacts of policychanges in one industry throughout the economy.This may be a self-serving attempt to set the stagefor their next volume. At least that is something tobe wished for.Reviewer address: Richard Beilock, University of Florida,PO Box 110240, Gainesville, FL 32611-0240, USA.Email: rpbeilock@ifas.ufl.edu.Statistical and Econometric Methods forTransportation Data AnalysisSimon P. Washington, Matthew G. Karlaftis, andFred L. ManneringChapman & Hall/CRC PressApril 2003, 425 pagesISBN 1584880309www.crcpress.com$89.95Transportation analysis has changed dramatically inthe last 50 years. For example, the sequential fourstepmodel of travel forecasting is slowly beingreplaced by activity-based analysis in addition tochanges in the types of techniques and methodsused. We have seen cross-classification analysisreplaced by linear regression, and statistical andeconometric methods such as hazard-based durationmodels and structural equations have become anintegral part of the methodological framework oftravel forecasting. With this evolution in methodscomes a tremendous need for books that synthesizeand extend the usual statistical theory presentationto one suitable for application-oriented audiences.Washington et al.’s book provides an excellent andneeded addition to this genre of texts.The book catalogs many of the major modelingtechniques used in practice, most of which are also animportant springboard for more advanced theoreticaland largely academic transportation modeling. Inthis sense, the book is an excellent addition to apracticing transportation analyst’s library as well asa perfect companion to a first year graduate modelingor methods course including, for example, travelforecasting, safety, and traffic engineering. One ofthe book’s most useful features is its singular focuson transportation. All of the examples relate to, andare focused on, transportation problems. As anadded benefit, the datasets used to develop theexamples can be accessed via the publisher’s website(http://www.crcpress.com/e_products/downloads/).Washington et al.’s book is organized into threemajor sections: the fundamentals, continuous dependentvariable models, and count and discretedependent variable models. The fundamentals sectionpresents basic statistical theory and includes topicssuch as central tendency, variability, hypotheses testing,and nonparametric tests. Part 2 includes discussionof regression, simultaneous equations, latentvariables, and duration models, as well as panel dataand time series analysis. In Part 3, count data modelsand the now familiar discrete choice and discrete/continuous models are presented.Deciding what to include in and what to leaveout of a methods book aimed at a transportationaudience is often very difficult. Practitioners maynot have the appropriate statistical background toimmediately grasp the main concepts without somereview of basic theory, yet too much foundationmaterial overlaps with many statistical texts alreadyavailable to graduate students. In Part 1, much ofthe material covered is readily available in mostintroductory statistical texts. It is useful materialeven for some first year graduate students; however,I would have liked to have seen this material dividedinto two sections, with most of the basic materialgoing into an appendix. Part 1 could then beexpanded to include many of the new modernBOOK REVIEWS 89

This is a statistical book. This is a statistical book.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is astatistical book. This is a statistical book. Alpha is awesome.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is a statisticalbook. This is a statistical book. This is a statistical book.This is a statistical book.This is a statistical book. This is a statistical book.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is astatistical book. This is a statistical book. Alpha is awesome.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is a statisticalbook. This is a statistical book. This is a statistical book.This is a statistical book.This is a statistical book. This is a statistical book.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is astatistical book. This is a statistical book. Alpha is awesome.This is a statistical book. This is a statistical book. This is astatistical book. This is a statistical book. This is a statisticalbook. This is a statistical book. This is a statistical book.This is a statistical book.graphical display techniques (e.g., Wilkenson 1999;Heiberger and Holland 2004) and perhaps somediscussion about software and statistical computing(e.g., Gentle 2002). Despite this relatively minorcaveat, the material presented in Part 1 is well done,with examples that clearly link theory to practice.Part 2 is where the book really begins to distinguishitself. The first third deals with the basic linearregression model and includes a fairly comprehensivepresentation of regression theory, assumptions,departures from assumptions, and the practicalaspects of manipulating variables and estimatingelasticities. The remaining two-thirds of Part 2 isdevoted to fairly contemporary modeling techniques,at least in terms of transportation practice.Of the various chapters, those dealing with timeseries and panel data analysis are perhaps theweakest. The remaining chapters in Part 2 are wellwritten and the authors have done an excellent jobsummarizing the major modeling approaches andthe main assumptions for each of the modelingtechniques. For example, in their chapter on durationmodels, they begin with a brief discussion of theKaplan-Meier method (the predominant nonparametricmodel used in survival analysis); the authorsthen turn to a longer exposition on semi-parametricand fully parametric models. Each section beginswith a presentation of the basic model, followed byan example, which helps to motivate the method’sapplication. In the chapter on duration models, allof the modeling approaches are tied together with abrief discussion comparing the different techniques.Finally, the chapter ends with a discussion of themodeling assumptions, which very cleverly motivatesseveral more complicated modeling approachesaddressing, in part, violations of the basic modelingassumptions.In Part 3, the authors limit their coverage to threevery important modeling approaches. The firstapproach is used for response variables that areconsidered count data and include the family ofPoisson and negative binomial models. The secondapproach focuses on discrete choice models and thefinal section on discrete/continuous models. All ofthese modeling approaches are accessible to practitionersand increasingly form the foundation forhandling many types of transportation problems. Aswith Part 2, each chapter begins with a presentationof the model structure and concludes with an examplehighly relevant to transportation planning andengineering practice.Overall, this text adroitly fills a very importantniche between practice and theory. Although I wouldliked to have seen a few additional topics—for example,more on contemporary graphical techniques andsome elaboration on simulation methods, which areplaying an increasingly important role in travel forecasting—ingeneral, the book is very well written. Irecommend it for most transportation analysts andbelieve it to be a good, solid addition to the librariesof transportation graduate students.ReferencesGentle, J. 2002. Elements of Computational Statistics: SpringerTexts in Statistics and Computing. New York, NY: Springer.Heiberger, R.M. and B. Holland. 2004. Statistical Analysis andData Display, An Intermediate Course with Examples in S-PLUS, R, and SAS: Springer Texts in Statistics. New York,NY: Springer.Wilkenson, L. 1999. Statistics and Computing, The Grammar ofGraphics: Springer Texts in Statistics. New York, NY:Springer.Reviewer address: Debbie A. Niemeier, Professor, Departmentof Civil and Environmental Engineering, One ShieldsAvenue, Davis, CA 95616. Email: dniemeier@ucdavis.edu.Transportation After DeregulationB. Starr McMullen, editorElsevier Science2001, 140 pagesISBN: 0-7623-0780-3$97.95 € 97.95 £64.95The U.S. Railroad Revitalization and RegulatoryAct of 1976 funded the reorganized bankrupt northeastand midwest railroads that formed Conrail.After the act went into effect, subsequent legislationinitiated deregulation across the transportationindustry and lifted most of the remaining motor carrierrestrictions, including those imposed by thestates. The six papers in this volume in the series,Research in Transportation Economics, all deal90 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

with the theme of transportation deregulation andregulatory reform and can be classified under one ofthe following topics: timing and impacts of deregulation,technological change issues, safety, and railroadmergers.In the paper by Wesley Wilson and WilliamWilson, the authors discuss the impact on the marketingpatterns of railroads of the Staggers Rail Act,legislation that deregulated the railroad industry.Implementation of the act resulted in lower ratesfor shipment of goods, especially grain products.The authors present an econometric analysis of railrates between 1972 and 1995 that focuses on fivegrain commodities. Their empirical model includesdemand, cost, and price relationships based on theNew Empirical Industrial Organization (NEIO)models and a specification for regulatory regimesthat uses a dummy intercept and a time trend forregulatory reform. Their results indicate the pricesof the five commodities decreased over time, but themagnitude across these commodities differed, rangingfrom 40% to 71%.Lawrence Wong’s study assesses the effects ofderegulation in the motor carrier sector by sortingout the independent impact of deregulation fromthose changes caused by the interaction of deregulationand new technologies. To do this, Wong used atranslog cost function model with a time trendincorporated as a third-order truncated Taylor seriesexpansion for data from 1976 through 1987. Additionally,the modeling efforts allowed for thedecomposition of the technological change intothree components: input bias, output bias, and characteristicbias. The author attempts to test theSchumpeter hypothesis for the less-than-truckload(LTL) sector, but the evidence to support thishypothesis in the LTL sector was not present. Theempirical results show that although implementationof new technologies provided labor-savingadvantages, they required greater expenditures andinduced input biases because of shifts in the outputlevel. The results also showed that the LTLsector of the motor carrier industry is capitalintensive,which creates higher barriers for entry.The paper by Kristen Monaco and TaggertBrooks presents a unique analysis of how deregulationaffected wages in the motor carrier sector. Theauthors used a time series approach because priortransportation wage studies that employed crosssectionalmethods could not control for macroeconomiceffects. Motor carrier wages are modeledas a function of manufacturing wages, and the relationshipsbetween the two series are then assessed.Their results show that the effects of deregulationon wages in this sector was felt most stronglybetween 1980 and 1984 and that wages in this sectordeclined from 1972 to 1996.Atreya Chakraborty and Mark Kazarosian do notexplicitly address the productivity in the motor carrierindustry in their paper, but instead, their analysisassesses the relationship between marketing strategyand information technology (IT). Some firms arelikely to use IT to increase the timely delivery ofgoods, while those that may produce the same levelof output (measured in ton-miles) as their IT-usingcounterparts may be less concerned with timelinessthan they are with lower rates. Given these twoapproaches, which are aggregated into one dataset,analysis of motor carrier productivity is inconclusivewithout controlling for marketing strategies.Although the financial condition of railroads hasimproved significantly since 1980, policymakers arerightly concerned about the future vitality of the sectorif costs are not contained. C. Gregory Bereskin’spaper focuses on the potential for transcontinentalrailroad mergers, a controversial issue given thesmall number of railroads in service and the concernthat mergers could result in a monopoly. Mergerswould likely increase efficiency and provide a higherquality of service, thus attracting more shippers andraising rail revenues. More specifically, the empiricalanalysis reveals that unexploited economies of scalecould result in monopolistic pricing, a standard theoryin market structure research.In the final paper in this volume, Frank Ruscoand W. David Walls examine the effects of deregulationon safety operations. Opponents of freightderegulation often assert that it would createincreased competition but at the cost of makingfreight transportation less safe. The authors applythis hypothesis to the minibus market in HongKong, which is both regulated and unregulated.The authors’ model shows that minibus drivers inthe unregulated market tend to drive faster andBOOK REVIEWS 91

experience higher accident rates than their regulatedcounterparts.Deregulation in the transportation sector of theUnited States has resulted in great savings andincreased flexibility. For example, freight transportationcosts dropped sharply. Railroad rates fellfrom 4.2¢ per ton-mile in the 1970s to 2.6¢ per tonmilein 1998. In addition, the railroad industrybecame more profitable. The cost of shipping bytruck fell by $40 billion from 1980 to 1988, andderegulation has improved flexibility and enabledbusinesses to provide timelier deliveries, which contributesto a reduction in inventory costs. Despitethe multitude of positive benefits from deregulation,there are some interesting policy issues that resulted,and these papers provide further empirical assessment.While readers may choose to peruse papers oftopical interest, it would be valuable to read all ofthe papers because they provide current research onthe effects of deregulation on the industry.Reviewer address: Brian Sloboda, Bureau of TransportationStatistics, U.S. Department of Transportation, 400 SeventhSt, SW, Room 3430, Washington, DC 20590.Email: brian.sloboda@dot.gov.92 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

Data ReviewAmerica’s Freight Transportation GatewaysOVERVIEWThe Bureau of Transportation Statistics (BTS) newreport, America’s Freight Transportation Gateways,and the accompanying Gateways Resource CDinclude detailed data on the movement of freightinto and out of the United States. Over 400 seaports,airports, and land border crossings currentlymove international freight in the United States. Thisnew report profiles the top 25 gateways, includingthe nation’s largest gateway by value, the Port ofLos Angeles.Folded into the back cover of the report is theGateways Resource CD, which provides detaileddata on over 200 gateways. The CD also containsextensive information on countries of origin anddestination for goods passing through U.S. gatewaysand the firms moving goods across internationalborders.The major sources of data for the Gatewaysreport are: Transborder Surface Freight Data from theBureau of Transportation Statistics, air cargo data from the Bureau of TransportationStatistics, U.S. Merchandise Trade Data from the U.S.Census Bureau, border-crossing data from U.S. Customs andBorder Protection, water data on seaports from the U.S. ArmyCorps of Engineers, and port calls and capacity data from the MaritimeAdministrationReview by: Jennifer Brady, Analyst, Bureau of TransportationStatistics, Research and Innovative TechnologyAdministration, U.S. Department of Transportation, 400Seventh St. SW, Room 3430, Washington, DC 20590.Email address: jennifer.brady@dot.gov.THE REPORTU.S. international trade increased at an average rateof 6% per year between 1990 and 2003, increasingfrom $899 billion to about $2 trillion. Water transportationcarried the most trade, in terms of bothtonnage and value. In 2003, vessels carried 78% ofthe total weight of trade and 41% of the total value(figure 1). Truck, train, and other land modes (e.g.,pipelines) carried 22% of the total weight and 28%of the value. Although air transportation accountedfor less than 1% of the total weight of trade, itstransport of high-value goods made it responsiblefor 26% of the value.FIGURE 1 Land, Water, and Air Gateways’ Shareof U.S. Merchandise Trade by Value:1990 and 20036050403020100PercentU.S. totalland trade 1U.S. totalwater trade1990 2003U.S. totalair tradeOther andunknown 21 Includes truck, rail, pipeline, and miscellaneous surface modes.2 Includes purchased vehicles such as aircraft or boats movingfrom manufacturer to customer where the vehicle itself is theshipment, pedestrians carrying freight, and miscellaneous.Source: U.S. Department of Transportation, Bureau ofTransportation Statistics, based on total trade, from U.S.International Trade Commission, USITC Interactive Tariff andTrade Dataweb, available at http://dataweb.usitc.gov/ as of Sept.15, 2004.93

The majority of trade is concentrated in a handfulof gateways. The top 5 gateways handled morethan one-quarter of the nation’s trade by value,while the top 15 handled more than 50% and thetop 50 handled 80% (table 1).The top three gateways, by value, represent thethree transportation modes (i.e., water, air, andland). The Port of Los Angeles was the leading gateway,with $122 billion in international shipments in2003. This port grew tremendously from 1999 to2003, reflecting increased trade with Asia and thePacific Rim. During this period, imports increased52% and exports increased 20%. Other gatewaysexperienced an average import/export growth rateof 14%.Import trade heavily outweighs export tradethrough the Port of Los Angeles. In 2003, importgoods comprised 86% of the value of freight movingthrough this port. The national average for importsis approximately 66%.The second largest gateway by value, John F.Kennedy (JFK) International Airport in New York,TABLE 1 Top 50 U.S. Freight Gateways, Ranked by Value of Shipments: 2003 (Current $, billions)Rank Port name ModeTotalU.S. trade Exports ImportsExports as% of total1 Port of Los Angeles, CA Water 122 17 105 13.82 JFK International Airport, NY Air 112 47 65 41.73 Port of Detroit, MI Land 102 55 47 53.54 Port of New York and New Jersey Water 101 24 77 24.05 Port of Long Beach, CA Water 96 17 79 17.96 Port of Laredo, TX Land 79 32 46 41.17 Los Angeles International Airport, CA Air 64 33 31 51.18 Port Huron, MI Land 62 23 40 36.49 Port of Buffalo-Niagara Falls, NY Land 59 27 32 46.110 Chicago, IL Air 54 21 34 37.911 Port of Houston, TX Water 50 21 28 43.012 San Francisco International Airport, CA Air 47 21 26 44.113 Port of Charleston, SC Water 39 13 26 34.014 Port of El Paso, TX Land 39 17 22 42.615 Port of Norfolk Harbor, VA Water 29 11 18 37.416 New Orleans, LA Air 27 14 14 50.017 Port of Tacoma, WA Water 26 5 21 19.818 Port of Baltimore, MD Water 26 6 20 21.919 Port of Oakland, CA Water 25 8 17 30.920 Dallas-Fort Worth, TX Air 24 11 12 48.321 Port of Seattle, WA Water 23 6 17 24.622 Miami International Airport, FL Air 23 14 9 61.523 Anchorage, AK Air 22 6 16 25.524 Port of Savannah, GA Water 21 7 14 34.725 Port of Otay Mesa Station, CA Land 20 8 11 42.026 Port of New Orleans, LA Water 19 11 8 57.927 Cleveland, OH Air 19 10 9 51.328 Atlanta, GA Air 18 8 10 45.629 Port of Miami, FL Water 17 7 10 41.130 Port of Champlain-Rouses Point, NY Land 14 5 9 36.294 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

TABLE 1 Top 50 U.S. Freight Gateways, Ranked by Value of Shipments: 2003 (Current $, billions) (continued)Rank Port name ModeTotalU.S. trade Exports ImportsExports as% of total31 Port of Hidalgo, TX Land 14 6 8 43.632 Newark, NJ Air 13 3 10 20.133 San Juan International Airport, PR Air 12 5 7 42.434 Port of Blaine, WA Land 12 5 7 43.635 Port of Portland, OR Water 12 3 9 25.136 Port of Jacksonville, FL Water 11 2 9 20.837 Port Everglades, FL Water 10 4 6 41.438 Port of Nogales, AZ Land 10 4 7 34.239 Port of Philadelphia, PA Water 10 1 10 6.140 Port of Morgan City, LA Water 10 0 10 1.841 Port of Brownsville, TX Land 10 5 5 51.542 Port of Alexandria Bay, NY Land 10 4 6 38.243 Port of Corpus Christie, TX Water 10 2 8 19.844 Port of Beaumont, TX Water 10 1 9 9.945 Port of Pembina, ND Land 9 5 4 53.146 Boston Logan Airport, MA Air 9 6 3 62.047 Port of Calexico-East, CA Land 9 4 5 42.448 Philadelphia International Airport, PA Air 9 5 4 53.849 Port of Sweetgrass, MT Land 7 4 4 48.150 Seattle-Tacoma International Airport, WA Air 7 4 3 56.8Total, top 50 gateways 1,587 576 1,011 36.3Total, U.S. merchandise tradeby all modes 1,983 724 1,259 36.5Top 50 gateways as share ofU.S. total (percent) 80.0 79.6 80.3Notes: All data—Trade levels reflect the mode of transportation as a shipment enters or exits a U.S. Customs port. Flows through individualports are based on reported data collected from U.S. trade documents. Low-value shipments (imports less than $1,250 and exports less than$2,500) and intransit shipments are not included in trade data. Air—Data for all airports are based on U.S. port classifications and include alow level (generally less than 2% to 3% of the total value) of small user-fee airports located in the same region. Air gateways not identified byairport name include major airports in that geographic area in addition to small regional airports. Also due to U.S. Census Bureauconfidentiality regulations, data for some of the air gateways include courier operations. For example, data for New Orleans InternationalAirport, include FedEx air cargo activity in Memphis, TN.Sources: Air—U.S. Department of Commerce, U.S. Census Bureau, Foreign Trade Division, special tabulation, August 2004. Water—U.S.Department of Transportation, Maritime Administration, Office of Statistical and Economic Analysis, special tabulations from WaterborneDatabank, August 2004. Land—U.S. Department of Transportation, Bureau of Transportation Statistics, Transborder Surface Freight Data asof August 2004.handled $112 billion in international trade. The topthree origins of goods delivered through JFK areLondon, Brussels, and Frankfurt. However, furtherreview of the data shows that most merchandiseoriginates in Asia, and Europe is the last link in thatsupply chain. The top two carriers operating out ofJFK, American Airlines and Lufthansa, togethertransported 21% of the imports and 17% of theexports passing through this gateway.Detroit, the third largest gateway in the UnitedStates and the largest land gateway, moved $102billion in trade. Of the top 25 gateways, Detroit isone of only three that handled more exports thanimports. Trucks carried the majority of merchandisetraveling by land through Detroit—83% by value.The gateway ranking would change if listed bytonnage. Tonnage data are incomplete, however,because for land exports these data are not availableDATA REVIEW 95

in official records. For example, among seaports,the Port of Los Angeles, which ranks first by value,ranks ninth insofar as waterborne tonnage is concerned.The need for more complete tonnage data isone of the findings of this report.THE GATEWAYS RESOURCE CDAmerica’s Freight Transportation Gateways profilesthe busiest gateways in America. Researchers interestedin performing analysis for other gateways mayuse the detailed data available on the CD, includedat the back of the report. The CD contains 11Microsoft Access files.Smaller GatewaysAll of the data breakouts presented in the Gatewaysreport can also be performed for smaller gatewaysnot included in the report. Some of the relevantdatabases on the CD include: Export Air Cargo,Freight Border Crossing and Entry Data, ImportAir Cargo, Maritime Tons by Foreign Ports, MaritimeValue by U.S. Ports, TEUs by U.S. Ports,Transborder Surface Freight, U.S. Freight Gatewaysby Value, and U.S. Seaports by Calls-Capacity.The CD includes data on trade levels at bordercrossings, ports, and airports. For example, usingthe Maritime Tons by Foreign Ports database, thetrade volume between specific U.S. ports and Europeancities can be determined. Depending on thedata needed, either the port names or the port numberscan be used, as designated by the U.S. CensusBureau. The database can also provide an historicaloverview of how port traffic changed from 1993through 2003. Other uses include: comparing theweight of trade moving through ports, airports,and land borders using short tons; and comparingthe value of imports for each mode.Trading PartnersResearchers interested in analyzing trade betweenthe United States and partner countries can turn to anumber of relevant databases on the CD: InternationalFreight by Country and Mode, MaritimeTons by Foreign Ports, Import Air Cargo, andExport Air Cargo.The International Freight by Country and Modedatabase contains data on 232 countries, from1997 through 2003. This database can be used tocompute the total value of imports or exports bymode and the total weight of imported andexported goods by vessel and air.In 2003, the five countries with the greatest valueof imports to the United States (by all modes) wereCanada, China (mainland), Mexico, Japan, andGermany. The same five countries supplied thegreatest value of imports to the United States in1997; however, at that time, Japan’s trade level wasgreater than both China and Mexico.The top five countries for exports by value in2003 were Canada, Mexico, Japan, the UnitedKingdom and Germany. Mainland China was thesixth largest recipient of U.S. goods by value. In1997, Germany was not in the top five and Koreawas.Analysis of the cargo weight (in short tons) bycarrier, origin airport, and destination airport can becomputed using the Export Air Cargo dataset. Forexample, the weight of trade (in short tons) betweenAuckland International Airport in New Zealandand all airports in the United States grew 12% from1999 to 2003. Trade from Auckland to Los AngelesInternational Airport increased 10%.Import air cargo is similar to export air cargo,where the country of destination is the UnitedStates. Analysis of the cargo weight (in short tons)by carrier, origin airport, and destination airportcan be computed using the Import Air Cargodataset.Industry ProfilesThe Import Air Cargo and Export Air Cargo databasesprovide information on trade volume by air.The database provides air carrier codes and names.Use of common air carrier and airport codes allowsthe user to link the Gateways database with otherBTS air data products. These datasets can be particularlyuseful for providing information on where96 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

air carrier firms operate and the level of businessconducted by each firm.Vessel TypesU.S. Seaports by Calls-Capacity includes the numberof port calls and capacity for 10 vessel typesand 133 ports. Not all ports received all types ofvessels. For example, Albany, New York, onlyreceived calls from dry bulk vessels and tankers. Incontrast, Boston received at least one call fromeach type of vessel. The largest percentage of portcalls in Boston were by tankers and product tankers,and the smallest number of port calls were bycrude tankers, general cargo vessels, and combinationvessels. The largest number of ports, 97,received dry bulk vessels; the smallest number ofports, 33, received vehicle-carrying vessels.COPIES AND QUESTIONSBTS provides this report, with the CD included, freeof charge, from:Customer ServiceBureau of Transportation StatisticsResearch and Innovative TechnologyAdministrationU.S. Department of Transportation400 Seventh St SW, Room 4117Washington, DC 20590Email: orders@bts.govOnline: www.bts.dot.gov, click on Products, thentype the name of the report in the Search Productsbox.Questions about this report: send an email toanswers@bts.gov or call 800-853-1351.DATA REVIEW 97

Journal of Transportationand Statisticscall for papersJTSis broadening its scope and will nowinclude original research using planning,engineering, statistical, and economic analysis toimprove public and private mobility and safetyin transportation. We are soliciting contributionsthat broadly support this objective.Examples of the type of material sought include❍ Analyses of transportation planning andoperational activities and the performance oftransportation systems❍ Advancement of the sciences of acquiring,validating, managing, and disseminatingtransportation information❍ Analyses of the interaction of transportationand the economy❍ Analyses of the environmental impacts oftransportationSee our website at www.bts.dot.gov/jts forfurther details and Guidelines for Submission.If you would like to receive a free subscriptionsend an email to the Managing Editor:marsha.fenn@dot.gov98 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

Journal of Transportation and StatisticsCall For PapersSpecial Issue on Transportation InvestmentIn conjunction with the national conference onTransportation and Economic Development(TED 2006) to be held in Little Rock, Arkansas, onMarch 29 and 30, 2006, the editors of the Journalof Transportation and Statistics are planning aspecial issue on transportation investment. Weinvite submissions relating to any mode oftransportation, with an emphasis on economicand statistical models generating policy-relevantresults. We are interested in papers dealingwith longer term planning, particularly thosefocusing on safety, infrastructure,environmental, or economic issues, but we willconsider other areas.All papers will be peer reviewed. Please refer to thejournal or visit the website (www.bts.gov/jts, andscroll to Guidelines for Manuscript Submission) foreditorial requirements. Authors are encouraged tosubmit an abstract via the registration website forTED 2006 and present your work at the conference.Information on TED 2006 is available athttp://www.ted2006-littlerock.org. The cut-offdate for submitting completed manuscripts forpublication consideration is Apr. 7, 2006. Pleasesend one hardcopy or electronic copy of yourpaper to Professor Yao, to whom allcorrespondence should be directed as well(see contact information below). The tentativepublication date of this special issue is early 2007.Editors of the Special IssueCletus C. CoughlinVice President and Deputy Director of ResearchFederal Reserve Bank of St. LouisSt. Louis, MOPhone: (314) 444-8585Email: coughlin@stls.frb.orgRandall W. EbertsExecutive DirectorW.E. Upjohn Institute for Employment ResearchKalamazoo, MIPhone: (269) 343-5541Email: eberts@upjohninstitute.orgVincent W. Yao (Corresponding Editor)Senior Research Economist and DirectorTransportation and Logistics ProgramInstitute for Economic AdvancementUniversity of ArkansasLittle Rock, AR 72204Phone: (501) 569-8453Email: wxyao@ualr.eduCALL FOR PAPERS 99

JOURNAL OF TRANSPORTATION AND STATISTICSGuidelines for Manuscript SubmissionPlease note: Submission of a paper indicates theauthor’s intention to publish in the Journal of Transportationand Statistics (JTS). Submission of a manuscriptto other journals is unacceptable. Previouslypublished manuscripts, whether in an exact or approximateform, cannot be accepted. Check with the ManagingEditor if in doubt.Scope of JTS: JTS publishes original research usingplanning, engineering, statistical, and economic analysisto improve public and private mobility and safety inall modes of transportation. For more detailed information,see the Call for Papers on page 98.Manuscripts must be double spaced, including quotations,abstract, reference section, and any notes. Allfigures and tables should appear at the end of themanuscript with each one on a separate page. Do notembed them in your manuscript.Because the JTS audience works in diverse fields,please define terms that are specific to your area ofexpertise.Electronic submissions via email to the ManagingEditor are strongly encouraged. We can accept PDF,Word, Excel, and Adobe Illustrator files. If you cannotsend your submission via email, you may send a diskor CD by overnight delivery service or send a hardcopyby the U.S. Postal Service (regular mail; seebelow). Do not send disks or CDs through regularmail.Hardcopy submissions delivered to BTS by the U.S.Postal service are irradiated. Do not include a disk inyour envelope; the high heat will damage it.The cover page of your manuscript must include thetitle, author name(s) and affiliations, and the telephonenumber and surface and email addresses of all authors.Put the Abstract on the second page. It should beabout 100 words and briefly describe the contents ofthe paper including the mode or modes of transportation,the research method, and the key results and/orconclusions. Please include a list of keywords todescribe your article.Graphic elements (figures and tables) must be calledout in the text. Graphic elements must be in black ink.We will accept graphics in color only in rare circumstances.References follow the style outlined in the ChicagoManual of Style. All non-original material must besourced.International papers are encouraged, but please besure to have your paper edited by someone whose firstlanguage is English and who knows your researcharea.Accepted papers must be submitted electronically inaddition to a hard copy (see above for information onelectronic submissions). Make sure the hard copy correspondsto the electronic version.Page proofs: As the publication date nears, authorswill be required to proofread and return article pageproofs to the Managing Editor within 48 hours ofreceipt.Acceptable software for text and equations is limitedto Word and LaTeX. Data behind all figures, maps,and charts must be provided in Excel, Word, or Delta-Graph. American Standard Code for InformationInterchange (ASCII) text will be accepted but is lessdesirable. Acceptable software for graphic elements islimited to Excel, DeltaGraph, or Adobe Illustrator. Ifother software is used, the file supplied must have an.eps or .pdf extension. We do not accept PowerPoint.Maps are accepted in a variety of Geographic InformationSystem (GIS) programs. Files using .eps or.pdf extensions are preferred. If this is not possible,please contact the Managing Editor. Send your fileson a CD-ROM via overnight delivery service.Send all submission materials to:Marsha Fenn, Managing EditorJournal of Transportation and StatisticsBTS/RITA/USDOT400 7th Street, SW, Room 7412Washington, DC 20590Email: marsha.fenn@dot.gov101

REVIEWERS for 2004AEric AmelBill AndersonDelta Air Lines, Atlanta, GABoston University, Boston, MABSandy BalkinDavid BallardDavid BanksAlfred BrandowskiMichael BronziniPfizer Inc., New York, NYGRA, Inc., Jenkintown, PADuke University, Durham, NCGdansk University of Technology,Gdansk Wrzeszcz, PolandGeorge Mason University, Fairfax, VACKen CampbellFrancis CarrCharles ChambersGang-Len ChangStephen ClarkOak Ridge National Laboratory, Knoxville, TNCharles Stark Draper Laboratory, Inc., Cambridge, MAInterVISTAS-ga 2 Consulting, Washington, DCUniversity of Maryland at College Park,College Park, MDLeeds City Council, Leeds, United KingdomFSteven FeinbergPhilip Hans FransesCarnegie Mellon University, Pittsburgh, PAErasmus University, Rotterdam, NetherlandsGNicholas GarberLaurie GarrowJean-Phillipe GervaisRalph GillmannThea GrahamDavid GreeneStephen GreavesMike GreenwaldRichard GrubergUniversity of Virginia, Charlottesville, VAGeorgia Institute of Technology, Atlanta, GAUniversité Laval, Québec, CanadaFederal Highway Administration, USDOT,Washington, DCFederal Aviation Administration, USDOT,Washington, DCOak Ridge National Laboratory, Knoxville, TNUniversity of Sydney, Sydney, AustraliaUniversity of Wisconsin, Milwaukee, WIFederal Highway Administration, USDOT,Washington, DCHJady HandalMark HansenKeith HofsethHo-Ling HwangFederal Aviation Administration, USDOT,Washington, DCUniversity of California, Berkeley, CAInstitute for Water Resources, Alexandria, VAOak Ridge National Laboratory, Knoxville, TN103

ITowhindul IslamUniversity of Guelph, Ontario, CanadaKAnne KoehlerYoung-Jun KweonMiami University, Oxford, OHUniversity of Texas, Austin, TXLJung-Taek LeeDou LongDom LordUniversity of Illinois, Chicago, ILLogistics Management Institute, McLean, VATexas A&M University, College Station, TXMFred ManneringJohn MaplesMike MargretaTom MazeSanal MazvancherylPatrick McCarthyMark McCordShaw-Pin MiaouEric MillerIan MoffatPurdue University, West Lafayette, INEnergy Information Administration, USDOE,Washington, DCResearch and Innovative Technology Administration,USDOT, Washington, DCIowa State University, Ames, IAState University of New York, Stony Brook, NYGeorgia Institute of Technology, Atlanta, GAOhio State University, Columbus, OHTexas A&M University, College Station, TXUniversity of Toronto, Toronto, CanadaUniversity of Stirling, Stirling, ScotlandNNagaraj NeerchalDebbie NeimeierBob NolandUniversity of Maryland, Baltimore County, MDUniversity of California, Davis, CAImperial College, London, EnglandOHarmen OppewalKaan OzbayMonash University, Victoria, AustraliaRutgers University, Piscataway, NJPBrian ParkRam PendyalaUniversity of Virginia, Charlottesville, VAUniversity of South Florida, Tampa, FLRRobert RaesideJohn RoseNapier University, Edinburgh, ScotlandUniversity of Sydney, Sydney, Australia104 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

SMiriam ScaglioneIrwin SilbermanKumares SinhaBrian SlobodaFrank SouthworthNikiforos StamatiadisHerman SteklerPeter StopherUniversity of Applied Sciences Valais,Sierre, SwitzerlandResearch and Innovative Technology Administration,USDOT, Washington, DCPurdue University, West Lafayette, INResearch and Innovative Technology Administration,USDOT, Washington, DCOak Ridge National Laboratory, Knoxville, TNUniversity of Kentucky, Lexington, KYGeorge Washington University, Washington, DCUniversity of Sydney, Sydney, AustraliaTJeremy TantrumKent TaylorTomar ToledoHarry TimmermansUniversity of Washington, Seattle, WANorth Carolina Department of Transportation,Raleigh, NCMassachusetts Institute of Technology, Cambridge, MATechnische Universiteit Eindhoven, Eindhoven,NetherlandsUJerry UllmanR.S. Radin UmarTexas A&M University, College Station, TXUniversiti Putra Malaysia, Serdang, MalaysiaWMartin WachsJoan WalkerSimon WashingtonSteve WelstandBilly WilliamsRobert WindleJames WinebrakeJeremy WuUniversity of California, Berkeley, CACaliper Corporation, Newton, MAUniversity of Arizona, Tucson, AZChevron Products Company, San Ramon, CANorth Carolina State University, Raleigh, NCUniversity of Maryland, College Park, MDJames Madison University, Harrisonburg, VAU.S. Census Bureau, Suitland, MDYMelinda YangChung-Hsing YehTed YoungloveBureau of Automotive Repair, Sacramento, CAMonash University, Victoria, AustraliaUniversity of California, Riverside, CAREVIEWERS FOR 2004 105

INDEX FOR VOLUME 7*AAccidents, traffic, Vol. 7(2/3):13–26Advanced Traveler Information Systems,Vol. 7(2/3):53–70AIC. See Akaike’s Information CriterionAir pollution, and unregistered motor vehicles,Vol. 7(2/3):1–12Air transportationairlineflight delay and cancellation analysis,Vol. 7(1):74–84low-cost carriers, Vol. 7(1):88–101regional carriers, Vol. 7(1):88–101traffic, Vol. 7(1):69–85networks, Vol. 7(1):87–101Air Travel Price Index, Vol. 7(2/3):41–52econometric forecasts, Vol. 7(1):7–21international freight, Vol. 7(2/3):93–97Akaike’s Information Criterion (AIC), Vol. 7(1):3Automatic vehicle identification, Vol. 7(2/3):53–70Aviation. See Air transportationBBayesiangeneralized cross validation, Vol. 7(2/3):56–70natural cubic splines, Vol. 7(2/3):54–70network model, Vol. 7(2/3):13–26smoothing splines, Vol. 7(2/3):53–70Bayesian Information Criterion (BIC), Vol. 7(1):3BIC. See Bayesian Information CriterionBorder crossingseconomics, Vol. 7(1):7–21forecast accuracy, Vol. 7(1):7–21international freight gateways, Vol. 7(2/3):93–97Bridges, traffic at international crossings, Vol. 7(1):7–21Buses, forecasting usage, Vol. 7(1):39–59CCaliforniacommodity inflows, Vol. 7(1):36Port of Los Angeles, international freight,Vol. 7(2/3):93–97unregistration rates of on-road vehicles,Vol. 7(2/3):1–12Cargo. See also Freightinternational, U.S./Mexico, Vol. 7(1):7–21CFS. See Commodity Flow SurveyCommodity Flow Survey (CFS), Vol. 7(1):23–37Commuting, forecasting train usage, Vol. 7(1):39–59Crashesand demographics, Vol. 7(2/3):13–26fatalities, Vol. 7(2/3):13–26intersections, Vol. 7(2/3):27–39road characteristics in, Vol. 7(2/3):13–26speed, Vol. 7(2/3):13–26weather, Vol. 7(2/3):13–26Cuidad Juárez, Mexico, border economics, Vol. 7(1):7–21DDemographic factors, crashes, Vol. 7(2/3):13–26EEconometric analysesairline networks, Vol. 7(1):87–101forecasting, Vol. 7(1):7–21, 87–101regional, Vol. 7(1):7–21Economic factors, price elasticities, Vol. 7(1):40Elasticity, priceairline, Vol. 7(1):96public transportation in Spain, Vol. 7(1):40El Paso, Texas, border economics, Vol. 7(1):7–21EMFAC (Emission FACtor) model, Vol. 7(2/3):2–4European Road Safety Charter, Vol. 7(1):62Exports, transportation of, Vol. 7 (2/3):93–97Extreme values, Vol. 7(2/3):41–52FFlorida, highway speeds, Vol. 7(2/3):71–86Forecasting, Vol. 7(1)accuracy, Vol. 7(1):51–57Akaike’s Information Criterion (AIC), Vol. 7(1):3Bayesian Information Criterion (BIC), Vol. 7(1):3borderplex econometric forecasting, Vol. 7(1):7–21definitionsaccuracy, Vol. 7(1):3calendar effects, Vol. 7(1):2ex-ante forecasts, Vol. 7(1):3ex-post forecasts, Vol. 7(1):3fit, Vol. 7(1):3hold-out samples, Vol. 7(1):2information criteria, Vol. 7(1):3Theil’s U, Vol. 7(1):3econometric, Vol. 7(1):7–21highway, Vol. 7(1):61–68pooling forecasts, Vol. 7(1):39–59, 87–101road safety forecasting, Vol. 7(1):61–68Freightcrossing borders, Vol. 7(2/3):93–97econometric forecasting, Vol. 7(1):7–21flows, Vol. 7(1):23–37planning, Vol. 7(1):23–37GGibbs sampler, Vol. 7(2/3):59–70Great Britain, road safety forecasting, Vol. 7(1):61–68HHighways, rural interstates, Vol. 7(2/3):71–86IIllinois, commodity inflows, Vol. 7(1):35* A complete index of all volumes of the journal is availableonline at www.bts.dot.gov.107

Imports, transportation of, Vol. 7(2/3):93–97IndexesAir Travel Price Index, Vol. 7(2/3):41–52extreme values, Vol. 7(2/3):41–52Fisher index, Vol. 7(2/3):41–52Laspeyres index, Vol. 7(2/3):43–52Paasche index, Vol. 7(2/3):43–52Taylor series, Vol. 7(2/3):41–52Törnqvist index, Vol. 7(2/3):41–52Intelligent transportation systems (ITS), Vol. 7(2/3):53–70MMalaysia, motorcycle crashes, Vol. 7(2/3):27–39Markov Chain Monte Carlo, Vol. 7(2/3):59–70Massachusetts, commodity inflows, Vol. 7(1):31Mexico, border economics, Vol. 7(1):7–21MOBILE (Mobile Source Emission Factor Model),Vol. 7(2/3):2ModelsBayesian network model for crashes, Vol. 7(2/3):13–26borderplex, Vol. 7(1):7–21Box-Jenkins ARIMA, Vol. 7(1):43, 63–64Dynamic Harmonic Regression, Vol. 7(1):43, 49–59Dynamic Transfer Function Causal Model,Vol. 7(1):43–49econometric, Vol. 7(1):87–101generalized linear model, Vol. 7(2/3):27–39gravity, Vol. 7(1):24input-output, Vol. 7(1):23–37prediction, Vol. 7(2/3):27–39structural, Vol. 7(1):8–21time series, Vol. 7(1):69–85traffic crashes, Vol. 7(2/3):13–26Motorcycles, crashes, Vol. 7(2/3):27–39Motor vehiclesautomatic vehicle identification, Vol. 7(2/3):53–70crashes, Vol. 7(2/3):13–26emissions, Vol. 7(2/3):1–12fatalities, Vol. 7(2/3):13–26, 27–39unregistration rates, Vol. 7(2/3):1–12NNAFTA. See North American Free Trade AgreementNational Highway Designation Act of 1995,Vol. 7(2/3):71–72National Maximum Speed Limit Act of 1974,Vol. 7(2/3):73New Yorkcommodity inflows, Vol. 7(1):32JFK International Airport, air cargo, Vol. 7(2/3)93–97North American Free Trade Agreement (NAFTA),Vol. 7(1):9, 18OOhio, commodity inflows, Vol. 7(1):34PPennsylvania, commodity inflows, Vol. 7(1):33Ports (air, land, and water), Vol. 7(2/3):93–97Public transportationfares, Vol. 7(1):40forecasts, Vol. 7(1):39–59RRailways, Vol. 7(1):79–84commuter usage, Vol. 7(1):39–59Regional analysiscommodity flow data, Vol. 7(1):23–37econometric forecasting, Vol. 7(1):7–21Regression models, Poisson, Vol. 7(1):64–65SSafetyforecastinghighway, Vol. 7(1):61–68road, Vol. 7(1):61–68modeling traffic crashes, Vol. 7(2/3):13–26motorcycle crashes in Malaysia, Vol. 7(2/3):27–39Sampling, unregistered vehicles, Vol. 7(2/3):3–6Seaports, international cargo, Vol. 7(2/3):93–97Slovenia, motor vehicle accidents, Vol. 7(2/3):13–26Southwest Airlines effect, Vol. 7(1):87–101Spain, public transport system forecasting use,Vol. 7(1):39–59Speed and speed limitsminimum and maximum speeds, Vol. 7(2/3):71–86National Highway Designation Act of 1995,Vol. 7(2/3):71–72National Maximum Speed Limit Act of 1974,Vol. 7(2/3):73on rural interstates, Vol. 7(2/3):71–86Surveys, unregistered vehicles, Vol. 7(2/3):1–12TTexasEl Paso, border economics, Vol. 7(1):7–21link travel time, Vol. 7(2/3):53–70Theil’s U, Vol. 7(1):3, 16–19, 21Time factors, travel time, Vol. 7(2/3):53–70Time series analysesintervention analysis, Vol. 7(1):69–85pooling forecasts, Vol. 7(1):87–101structural, Vol. 7(1):69–85Traffic management, monitoring, Vol. 7(2/3):74–86Trains. See RailwaysTransit. See Public transportationTransportation indicators, Vol. 7(1):69–85UUniform Vehicle Code, Vol. 7(2/3):72WWashington (state), commodity inflows, Vol. 7(1):37Water transportationinternational freight, Vol. 7(2/3):93–97vessel types, Vol. 7(2/3):97108 JOURNAL OF TRANSPORTATION AND STATISTICS V7, N2/3 2004

U.S. Department of TransportationResearch and Innovative Technology AdministrationBureau of Transportation StatisticsJOURNAL OF TRANSPORTATIONAND STATISTICSVolume 7 Numbers 2/3, 2004ISSN 1094-8848CONTENTSTHEODORE YOUNGLOVE, CARRIE MALCOLM, THOMAS D DURBIN,MATTHEW R SMITH, ALBERTO AYALA + SANDEE KIDDUnregistration Rates for On-Road Vehicles in CaliforniaMARJAN SIMONCIC A Bayesian Network Model ofTwo-Car AccidentsS HARNEN, RS RADIN UMAR, SV WONG + WI WAN HASHIMDevelopment of Prediction Models for Motorcycle Crashes atSignalized Intersections on Urban Roads in MalaysiaJANICE LENT Effects of Extreme Values on Price Indexes: The Caseof the Air Travel Price IndexBYRON GAJEWSKI + LAURENCE R RILETT Estimating Link TravelTime Correlation: An Application of Bayesian Smoothing SplinesVICTOR MUCHURUZA + RENATUS N MUSSA Speeds onRural Interstate Highways Relative to Posting the 40 mph MinimumSpeed LimitBook ReviewsData ReviewAmerica’s Freight Transportation Gateways: Connecting OurNation to Places and Markets Abroad, a new report by the Bureauof Transportation Statistics