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The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach

Author

Listed:
  • Chenfeng Xiong

    ()

  • Xiqun Chen

    ()

  • Xiang He

    ()

  • Wei Guo

    ()

  • Lei Zhang

    ()

Abstract

Discrete choices are often analyzed statically. The limitations of static models become more obvious when employing them in more long-term travel demand forecasting. The research gap lies in a theoretical model which is dynamically formulated, and in readily available longitudinal data sources. To address this, a heterogeneous hidden Markov modeling approach (HMM) is proposed in this paper to model dynamic discrete choices. Both longitudinal and cross-sectional heterogeneity are considered. The approach is demonstrated on a travel mode choice application using ten-wave Puget Sound Transport Panel data coupled with some other supplementary data sources. Results indicate that travelers’ long-term life-cycle stages have an enduring impact when shifted to different mode choice states, wherein sensitivities to travel time and cost vary. Empirical results are put in line with static discrete choice models. The paper demonstrates that the family of HMM models provide the best fitting model. The dynamic model has superior explanatory power in fitting longitudinal data and thus shall provide more accurate estimates for planning and policy analyses. The proposed approach can be generalized to study other short/mid-term travel behavior. The estimated model can be easily calibrated and transferred for applications elsewhere. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Chenfeng Xiong & Xiqun Chen & Xiang He & Wei Guo & Lei Zhang, 2015. "The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach," Transportation, Springer, vol. 42(6), pages 985-1002, November.
  • Handle: RePEc:kap:transp:v:42:y:2015:i:6:p:985-1002
    DOI: 10.1007/s11116-015-9658-2
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    References listed on IDEAS

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    1. Goulias, Konstadinos G., 1999. "Longitudinal analysis of activity and travel pattern dynamics using generalized mixed Markov latent class models," Transportation Research Part B: Methodological, Elsevier, vol. 33(8), pages 535-558, November.
    2. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    3. R M Pendyala & R Kitamura & D V G Prasuna Reddy, 1998. "Application of an activity-based travel-demand model incorporating a rule-based algorithm," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 25(5), pages 753-772, September.
    4. Vij, Akshay, 2013. "Incorporating the Influence of Latent Modal Preferences in Travel Demand Models," University of California Transportation Center, Working Papers qt7nq9p0cv, University of California Transportation Center.
    5. Vij, Akshay, 2013. "Incorporating the Influence of Latent Modal Preferences in Travel Demand Models," University of California Transportation Center, Working Papers qt7ng2z24q, University of California Transportation Center.
    6. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    7. Hess, Stephane & Train, Kenneth E., 2011. "Recovery of inter- and intra-personal heterogeneity using mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 973-990, August.
    8. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    9. Karthik Srinivasan & P. Bhargavi, 2007. "Longer-term changes in mode choice decisions in Chennai: a comparison between cross-sectional and dynamic models," Transportation, Springer, vol. 34(3), pages 355-374, May.
    10. Kitamura, Ryuichi, 1990. "Panel Analysis in Transportation Planning: An Overview," University of California Transportation Center, Working Papers qt86v0f7zh, University of California Transportation Center.
    11. Vij, Akshay & Carrel, André & Walker, Joan L., 2013. "Incorporating the influence of latent modal preferences on travel mode choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 164-178.
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    Cited by:

    1. repec:kap:transp:v:44:y:2017:i:4:d:10.1007_s11116-016-9732-4 is not listed on IDEAS
    2. repec:kap:transp:v:45:y:2018:i:3:d:10.1007_s11116-016-9751-1 is not listed on IDEAS
    3. Chenfeng Xiong & Lei Zhang, 2017. "Dynamic travel mode searching and switching analysis considering hidden model preference and behavioral decision processes," Transportation, Springer, vol. 44(3), pages 511-532, May.

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