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Incorporating the Influence of Latent Modal Preferences in Travel Demand Models

  • Vij, Akshay
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    Latent modal preferences, or modality styles, are defined as behavioral predispositions towards a certain travel mode or set of travel modes that an individual habitually uses. They are reflective of higher-level orientations, or lifestyles, that are hypothesized to influence all dimensions of an individual’s travel and activity behavior. For example, in the context of travel mode choice different modality styles may be characterized by the set of travel modes that an individual might consider when deciding how to travel, her sensitivity, or lack thereof, to different level-of-service attributes of the transportation (and land use) system when making that decision, and the socioeconomic characteristics that predispose her one way or another. Travel demand models currently in practice assume that individuals are aware of the full range of alternatives at their disposal, and that a conscious choice is made based on a tradeoff between perceived costs and benefits associated with alternative attributes. Heterogeneity in the choice process is typically represented as systematic taste variation or random taste variation to incorporate both observable and unobservable differences in sensitivity to alternative attributes. Though such a representation is convenient from the standpoint of model estimation, it overlooks the effects of inertia, incomplete information and indifference that are reflective of more profound individual variations in lifestyles built around the use of different travel modes and their concurrent influence on all dimensions of individual and household travel and activity behavior. The objectives of this dissertation are three-fold: (1) to develop a travel demand model framework that captures the influence of modality styles on multiple dimensions of individual and household travel and activity behavior; (2) to test that the framework is both methodologically flexible and empirically robust; and (3) to demonstrate the value of the framework to transportation policy and practice.

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    File URL: http://www.escholarship.org/uc/item/7ng2z24q.pdf;origin=repeccitec
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    Paper provided by University of California Transportation Center in its series University of California Transportation Center, Working Papers with number qt7ng2z24q.

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    Date of creation: 01 Aug 2013
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    Handle: RePEc:cdl:uctcwp:qt7ng2z24q
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    1. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
    2. Samuel Bowles, 1998. "Endogenous Preferences: The Cultural Consequences of Markets and Other Economic Institutions," Journal of Economic Literature, American Economic Association, vol. 36(1), pages 75-111, March.
    3. Wiktor Adamowicz & Michel Hanemann & Joffre Swait & Reed Johnson & David Layton & Michel Regenwetter & Torsten Reimer & Robert Sorkin, 2005. "Decision Strategy and Structure in Households: A “Groups” Perspective," Marketing Letters, Springer, vol. 16(3), pages 387-399, December.
    4. Chandra Bhat & Ram Pendyala, 2005. "Modeling intra-household interactions and group decision-making," Transportation, Springer, vol. 32(5), pages 443-448, 09.
    5. Alderman, Harold, et al, 1995. "Unitary versus Collective Models of the Household: Is It Time to Shift the Burden of Proof?," World Bank Research Observer, World Bank Group, vol. 10(1), pages 1-19, February.
    6. Ben-Akiva, M. & Bolduc, D. & Bradley, M., 1993. "Estimation of Travel Choice Models with Randomly Distributed Values of Time," Papers 9303, Laval - Recherche en Energie.
    7. Ben-Elia, Eran & Ettema, Dick, 2009. "Carrots versus sticks: Rewarding commuters for avoiding the rush-hour--a study of willingness to participate," Transport Policy, Elsevier, vol. 16(2), pages 68-76, March.
    8. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    9. Anocha Aribarg & Neeraj Arora & Moon Young Kang, 2010. "Predicting Joint Choice Using Individual Data," Marketing Science, INFORMS, vol. 29(1), pages 139-157, 01-02.
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