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Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation

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  • Srinivasan, Karthik K.
  • Mahmassani, Hani S.
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    Abstract

    This paper focuses on modeling unobserved effects in route-switching dynamics under advanced traveler information systems (ATIS). The analysis explicitly accounts for the presence of heterogeneity in behavior and a general stochastic pattern for the unobservables. The dynamic kernel logit (DKL) framework (also referred to as dynamic mixed logit) is proposed and applied to model route-switching dynamics (with 55 repeated decisions per user), based on data from interactive simulator experiments. In contrast to the multinomial probit framework, the DKL is well-suited for calibrating dynamic travel behavior models with a large number of panel periods. To increase computational efficiency, the proposed formulation exploits a components of variance scheme to represent the correlation of error-terms (both within-day and day-to-day). The empirical results indicate that unobserved effects account significantly for the observed variability in route-switching behavior. Among the observed effects, users' route-switching behavior is influenced by the nature, timeliness, and extent of real-time information, as also its quality. In addition, route switching is influenced by the level-of-service attributes on the alternative routes and users' prior traffic experience. Among the unobserved effects, the results present evidence of considerable heterogeneity in route switching. The significance of experience variables, and the correlation of unobservables over time and within-day, indicate the presence of dynamic learning and adjustment processes in user behavior under ATIS. Although observed and unobserved preference and response heterogeneity are all significant, the largest improvement in model fit is achieved by incorporating observed heterogeneity followed by unobserved preference and response heterogeneity respectively. These findings have significant applications in route assignment models under information, design and evaluation of ATIS products and services, and assessment of various policy measures aimed at travel demand management.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 37 (2003)
    Issue (Month): 9 (November)
    Pages: 793-814

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    Handle: RePEc:eee:transb:v:37:y:2003:i:9:p:793-814

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    1. Bhat, Chandra R., 1998. "Accommodating flexible substitution patterns in multi-dimensional choice modeling: formulation and application to travel mode and departure time choice," Transportation Research Part B: Methodological, Elsevier, vol. 32(7), pages 455-466, September.
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    7. Brownstone, David & Bunch, David S & Train, Kenneth, 1999. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," University of California Transportation Center, Working Papers qt45f996hh, University of California Transportation Center.
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    Cited by:
    1. Ben-Elia, Eran & Ettema, Dick, 2011. "Rewarding rush-hour avoidance: A study of commuters' travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 567-582, August.
    2. Tillema, Taede & Ben-Elia, Eran & Ettema, Dick & van Delden, Janet, 2013. "Charging versus rewarding: A comparison of road-pricing and rewarding peak avoidance in the Netherlands," Transport Policy, Elsevier, vol. 26(C), pages 4-14.
    3. Lei Zhang & David Levinson, 2006. "Determinants of Route Choice and the Value of Traveler Information," Working Papers 200808, University of Minnesota: Nexus Research Group.
    4. de Moraes Ramos, Giselle & Daamen, Winnie & Hoogendoorn, Serge, 2013. "Modelling travellers' heterogeneous route choice behaviour as prospect maximizers," Journal of choice modelling, Elsevier, vol. 6(C), pages 17-33.
    5. Ben-Elia, Eran & Shiftan, Yoram, 2010. "Which road do I take? A learning-based model of route-choice behavior with real-time information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 249-264, May.
    6. Jou, Rong-Chang & Lam, Soi-Hoi & Liu, Yu-Hsin & Chen, Ke-Hong, 2005. "Route switching behavior on freeways with the provision of different types of real-time traffic information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 445-461, June.
    7. Eran Ben-Elia & Robert Ishaq & Yoram Shiftan, 2013. "“If only I had taken the other road...”: Regret, risk and reinforced learning in informed route-choice," Transportation, Springer, vol. 40(2), pages 269-293, February.
    8. Shanjiang Zhu & David Levinson & Lei Zhang, 2007. "An Agent-based Route Choice Model," Working Papers 000089, University of Minnesota: Nexus Research Group.

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