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

Author

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  • 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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    2. Ali Ardeshiri & Akshay Vij, 2019. "A lifestyle-based model of household neighbourhood location and individual travel mode choice behaviours," Papers 1902.01986, arXiv.org, revised Nov 2019.
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    7. Chenfeng Xiong & Di Yang & Lei Zhang, 2018. "A High-Order Hidden Markov Model and Its Applications for Dynamic Car Ownership Analysis," Service Science, INFORMS, vol. 52(6), pages 1365-1375, December.
    8. Song, Yuchen & Li, Dawei & Liu, Dongjie & Cao, Qi & Chen, Junlan & Ren, Gang & Tang, Xiaoyong, 2022. "Modeling activity-travel behavior under a dynamic discrete choice framework with unobserved heterogeneity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    9. Prateek Bansal & Daniel Horcher & Daniel J. Graham, 2020. "A Dynamic Choice Model with Heterogeneous Decision Rules: Application in Estimating the User Cost of Rail Crowding," Papers 2007.03682, arXiv.org.
    10. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
    11. 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.
    12. Zhang, Yu & Li, Leiming, 2022. "Research on travelers’ transportation mode choice between carsharing and private cars based on the logit dynamic evolutionary game model," Economics of Transportation, Elsevier, vol. 29(C).
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    14. Stanislav S. Borysov & Jeppe Rich, 2021. "Introducing synthetic pseudo panels: application to transport behaviour dynamics," Transportation, Springer, vol. 48(5), pages 2493-2520, October.

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