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

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Suggested Citation:"T57054 txt_091.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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A comparison of the CATI- reported trips with the GPS- detected trips identified 280 GPS- detected trips that were not reported by the drivers in the CATI travel sur- vey. [The extraction of trips from the GPS traces was based on a multilevel trip detection algorithm developed by GeoStats, with several built- in checks to avoid ghost trips (such as due to starts and stops at street lights). The full details of the GPS processing are available in the study report by NuStats.] A descriptive analysis of trip underre- porting by driver demographics, driver travel characteris- tics, and driver adherence to survey protocols was undertaken. The results related to demographic charac- teristics suggested that drivers between the ages of 50 and 69 who were male, with low education levels, were not employed or were employed in sales–clerical occupations, working at locations characterized as residential, from single- adult or retired households, from one- or three- person households, and from 3+ vehicle households were the most likely to underreport trips. The driver travel characteristics indicated that drivers who made a rela- tively large number of total trips during the survey day, pursued long distance trips, and undertook trip chaining on the survey day were overrepresented in the pool of those who underreport. Finally, in the category of driver adherence to survey protocols, the results suggested that drivers who did not use their travel diaries for recording travel and who had their travel details reported by proxy were more likely to underreport. These descriptive statistics provide suggestive evi- dence of the effect of various driver attributes on the propensity to underreport trips. However, these are uni- dimensional statistics in that they do not control for the influence of other variables when the impact of any sin- gle variable is being examined. For instance, the gender difference in underreporting may be a manifestation of different travel patterns of men and women. Further, the descriptive analysis does not focus on the characteristics affecting the level of trip underreporting. To obtain a comprehensive picture of the factors affecting whether an individual underreports and the level of underreport- ing, it is necessary to pursue a multidimensional and comprehensive analysis that examines the effects of all potential determinants of both underreporting propen- sity and the level of underreporting propensity. The next section presents the model structure and empirical analy- sis for such a methodology. MODEL SUMMARY The approach adopted in this study used two equations: a binary model for whether an individual underreports or not and an ordered- response model for the number of trips underreported if there is underreporting at all. The methodology accounts for the correlation in error terms between the two equations. That is, it accounts for the potential presence of unobserved individual factors (such as, say, an overall disinclination to respond to surveys or substantial time constraints) that influences both whether an individual underreports and the level of underreporting. For a more detailed discussion of the modeling portion of this research, see Bricka and Bhat. The fundamental hypothesis underlying our empirical analysis was that trip underreporting is largely due to three areas of influence: who the driver is (driver demo- graphics such as household type, age, number of house- hold vehicles, employment status, etc.), the characteristics of trips made (total number of trips, average distance of trips, and level of trip chaining), and how well the driver adhered to the survey protocol (whether driver used the travel diary to record all travel and whether driver talked directly with interviewer). All exogenous inputs to the model were classified according to these broad categories. The final variable specifications for the binary model of underreporting and the ordered- response model for level of underreporting among underreporting individuals were developed by adopting a systematic procedure of eliminating statistically insignificant variables. Of course, as indicated earlier, the entire specification effort was also informed by the results of earlier studies and intuitive considerations. RESULTS AND IMPLICATIONS FOR SURVEY METHODS The results from our modeling effort provide important insights about underreporting tendencies in traditional household travel surveys. First, the underlying mecha- nism that represents whether an individual underreports or not is different from the mechanism that determines the level of underreporting. At the same time, there are common unobserved factors that influence both the underreporting propensity and the propensity associated with the level of underreporting. Consequently, it is important to use the joint binary- unordered response framework of the current study to analyze trip underre- porting and its magnitude. Second, the effect of driver demographics indicates that young adults (less than 30 years of age); men; individuals with less than high school education; unemployed individuals; individuals working in clerical and manufacturing professions; workers employed at residential, industrial, and medical land uses; and individuals in nuclear families are all more likely to underreport trips in household travel surveys than other respondents. Third, driver travel characteris- tics that affect the tendency to underreport include mak- ing a high number of trips on the survey day, traveling long distances per trip, and trip chaining. Fourth, drivers who do not use the travel diary to record their travel are 91USING GLOBAL POSITIONING SYSTEM DATA TO INFORM TRAVEL SURVEY METHODS

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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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