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Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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Suggested Citation:"T57054 txt_090.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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underreporting rate to vary greatly (in Austin the rate was 12% or 31%, depending on the time threshold). • The actual data collection methods and instruc- tions to respondents can also influence the calculation of trip underreporting rates. In most studies, respondents are instructed not to report trips out of the geographi- cally defined study area and trips for commercial pur- poses. However, most early trip- detection algorithms did not distinguish between these types of trips and those reported by respondents, resulting in overreported trip- underreporting rates. In addition, as determined in the Laredo study, the survey process does not directly collect information about trips made by nonhousehold mem- bers driving the GPS- equipped vehicles. On the basis of a review of literature on trip underre- porting in regional household travel surveys and the development of associated correction factors, most trip underreporting is associated with households that own three or more vehicles, households with incomes of less than $50,000, and respondents under the age of 25. From a travel behavior perspective, respondents who travel substantially make several short trips (less than 5 min) and make trips of a discretionary nature are most likely to “forget” to record this travel (as has been sug- gested on parallel literature about trip chaining). The studies to date have clearly aided in identifying factors associated with trip underreporting in regional travel surveys. In this paper, the authors contribute to this existing literature and continuing discussion about GPS technology in travel surveys in several ways. First, in the current study (and unlike earlier studies), both the presence of trip underreporting by an individual and the level of trip underreporting by the individual are mod- eled. The separation of the presence of trip underreport- ing from the level of trip underreporting recognizes that different explanatory variables may affect these out- comes, that the same explanatory variable may affect these outcomes differently, or both. Second, the joint model also recognizes that the likelihood of trip under- reporting and the level of trip underreporting may be related to one another. For example, it is conceivable (if not likely) that individuals who are, by nature, less likely to be responsive to surveys are the ones who underreport and underreport substantially. Similarly, individuals who are, by nature, interested in the survey would be the ones less likely to underreport at all, and even if they did underreport, would do so only marginally. Third, in addition to jointly modeling trip underreporting and the level of trip underreporting, the empirical analysis in the current study considers a comprehensive set of variables related to driver demographics, driver travel characteris- tics, and driver adherence to survey protocol. Finally, this work translates the empirical analysis results to rec- ommendations about household travel survey proce- dures to reduce the magnitude of trip underreporting in future travel surveys. GPS AND TRAVEL SURVEY DATA The empirical analysis in the current paper uses data extracted from the Kansas City Regional Household Travel Survey conducted in spring 2004 under the spon- sorship of the Mid- America Regional Council and the Kansas and Missouri Departments of Transportation. As part of the Kansas City survey, complete demographic and travel behavior characteristics of 3,049 randomly sampled households were obtained, including details about 32,011 trips for 7,570 household members. The GPS component of the study involved equipping the vehi- cles of 294 households with GPS equipment to record all vehicle travel during the assigned travel period. Of the 294 households, both computer- assisted telephone inter- view (CATI) and GPS data are available for 228 house- holds. All subsequent analyses in the current paper focus on these 228 households, corresponding to 377 drivers and 2,359 vehicle trips. (For more details on the charac- teristics of these GPS households compared with the gen- eral survey participants as a whole, see NuStats.) Of the 377 drivers, 269 (or 71%) accurately reported all travel in their CATI survey, while 108 (or 29%) had at least one instance of a trip that was not reported. [A subtle, but important, point is that, for the underreport- ing analysis, the authors focused on the CATI- reported vehicle trips across all individuals in the household who drove each GPS- equipped vehicle. This focus allows a fair comparison between the CATI- reported vehicle trips and the GPS- detected vehicle trips. However, rather than confine the analysis of the determinants of underreport- ing to household- level characteristics, also included were person- level characteristics to accommodate person- specific tendencies to underreport. To accomplish this, the authors identified a primary driver for each GPS- equipped vehicle on the basis of information provided by respondents and used these primary- driver characteris- tics as explanatory variables in the analysis (along with household demographics). This approach is reasonable because each vehicle in this study was predominantly used by only one primary driver in the household (espe- cially within a short period of time, such as a survey day). Specifically, in the sample used for our analysis, there was car sharing of some form among household mem- bers in 6% of all households.] Among the 108 respon- dents who underreported, 53 (49%) missed one trip, 22 (20%) missed two trips, 11 (10%) missed three trips, six (5.5%) missed four trips, and 16 (14.5%) missed five or more trips. There was a narrow, long tail in the ≥5 missed- trips category, with one individual underreport- ing 17 trips. 90 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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