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

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Suggested Citation:"T57054 txt_140.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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ship decisions of San Francisco Bay area residents will be presented at the innovative modeling conference. The important findings from this application are as follows. First, BE attributes affect residential choice and car own- ership decisions. Thus, policy decisions regarding changes in BE characteristics must be evaluated in the joint context of both decisions, so that spatial relocation patterns and car ownership changes can be analyzed. Such a complete picture enables a comprehensive assess- ment of potential travel- related changes due to BE poli- cies. Second, the authors’ findings support the notion that the commonly used population and employment density measures are actually proxy variables for such BE measures as street block density and transit accessi- bility. Third, in the context of car ownership decisions, both household demographics and BE characteristics are influential, with household demographics having a stronger effect. Fourth, there is variation in sensitivity to BE attributes due to both demographic and unobserved factors, in both residential choice as well as car owner- ship decisions. But, while the study examined demo- graphic interactions and allowed random variations in sensitivity to several BE characteristics, most did not turn out to be statistically significant. Among demographics, income is a key variable in affecting the sensitivity to BE attributes and related variables. Unobserved household- specific factors also play an important role in the sensi- tivity to commute time and street block density (in the residential choice model) and employment density and street block density (in the car ownership model). Ignor- ing such systematic and random variations in sensitivity to BE attributes will, in general, lead to inconsistent results regarding the effect of BE attributes on travel behavior decisions, which can, in turn, lead to inappro- priate policy decisions. Fifth, household income is the dominant factor in residential sorting. Specifically, low- income households consciously choose to (or are con- strained to) locate in neighborhoods with low commute costs, long commute times, and high employment den- sity compared with their high- income counterparts. Such low- income households also intrinsically choose to own fewer cars. Thus, ignoring income effects in car owner- ship (and, by extension, other travel decisions) can lead to an inflated effect of BE and related variables on travel behavior decisions. Other demographic factors that impact residential sorting based on car ownership pref- erences correspond to the presence of senior adults in the household and whether or not a person lives alone. Finally, and rather surprisingly, the results did not sup- port the notion of residential sorting in car ownership propensity based on unobserved household factors. This implies that independent models of residential choice and car ownership choice (after accommodating the res- idential sorting effects of demographics) are adequate to examine BE effects on car ownership choice, in the cur- rent empirical context. But, in general, it is important to consider the methodology developed in this paper to control for the potential presence of self- selection due to both observed and unobserved household factors. Only by estimating the joint model can one conclude about the potential presence or absence of self- selection effects due to unobserved factors. REFERENCES Badoe, D. A., and E. J. Miller. 2000. Transportation–Land Use Interaction: Empirical Findings in North America, and Their Implications for Modeling. Transportation Research Part D: Transport and Environment, Vol. 5, No. 4, pp. 235–263. Berry, S., J. A. Levinsohn, and A. Pakes. 1995. Automobile Prices in Market Equilibrium. Econometrica: Journal of the Econometric, Vol. 63, No. 4, pp. 841–890. Bhat, C. R., and R. Gossen. 2004. A Mixed Multinomial Logit Model Analysis of Weekend Recreational Episode Type Choice. Transportation Research Part B: Methodological, Vol. 38, No. 9, pp. 767–787. Bhat, C. R., and A. Lockwood. 2004. On Distinguishing Between Physically Active and Physically Passive Episodes and Between Travel and Activity Episodes: An Analysis of Weekend Recreational Participation in the San Francisco Bay Area. Transportation Research Part A: Policy and Practice, Vol. 38, No. 8, pp. 573–592. Bhat, C. R., and S. K. Singh. 2000. A Comprehensive Daily Activity–Travel Generation Model System for Workers. Transportation Research Part A: Policy and Practice, Vol. 34, No. 1, pp. 1–22. Bhat, C. R., and S. Srinivasan. 2005. A Multidimensional Mixed Ordered–Response Model for Analyzing Weekend Activity Participation. Transportation Research Part B: Methodological, Vol. 39, No. 3, pp. 255–278. Bhat, C. R., and H. Zhao. 2002. The Spatial Analysis of Activ- ity Stop Generation. Transportation Research Part B: Methodological, Vol. 36, No. 6, pp. 557–575. Bhat, C. R., S. Srinivasan, and K. W. Axhausen. 2005. An Analysis of Multiple Interepisode Durations Using a Unify- ing Multivariate Hazard Model. Transportation Research Part B: Methodological, Vol. 39, No. 9, pp. 797–823. Boarnet, M. G., and R. Crane. 2001. The Influence of Land Use on Travel Behavior: Specification and Estimation Strategies. Transportation Research Part A: Policy and Practice, Vol. 35, No. 9, pp. 823–845. Boarnet, M. G., and S. Sarmiento. 1998. Can Land- Use Policy Really Affect Travel Behavior? A Study of the Link Between Nonwork Travel and Land- Use Characteristics. Urban Studies, Vol. 35, No. 7, pp. 1155–1169. Crane, R. 2000. The Influence of Urban Form on Travel: An Interpretive Review. Journal of Planning Literature, Vol. 15, No. 1, pp. 3–23. 140 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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