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Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis

Author

Listed:
  • Chenfeng Xiong

    (University of Maryland)

  • Di Yang

    (University of Maryland)

  • Jiaqi Ma

    (University of Cincinnati)

  • Xiqun Chen

    (Zhejiang University)

  • Lei Zhang

    (University of Maryland)

Abstract

As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.

Suggested Citation

  • Chenfeng Xiong & Di Yang & Jiaqi Ma & Xiqun Chen & Lei Zhang, 2020. "Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis," Transportation, Springer, vol. 47(2), pages 585-605, April.
    • Handle: RePEc:kap:transp:v:47:y:2020:i:2:d:10.1007_s11116-018-9900-9
    DOI: 10.1007/s11116-018-9900-9
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    References listed on IDEAS

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    1. Nobuhiro Sanko & Takayuki Morikawa, 2010. "Temporal transferability of updated alternative-specific constants in disaggregate mode choice models," Transportation, Springer, vol. 37(2), pages 203-219, March.
    2. Nobuhiro Sanko, 2014. "Travel demand forecasts improved by using cross-sectional data from multiple time points," Transportation, Springer, vol. 41(4), pages 673-695, July.
    3. Gunn, Hugh, 0. "Spatial and temporal transferability of relationships between travel demand, trip cost and travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(2-3), pages 163-189, April.
    4. Taha Rashidi & Abolfazl Mohammadian, 2011. "Household travel attributes transferability analysis: application of a hierarchical rule based approach," Transportation, Springer, vol. 38(4), pages 697-714, July.
    5. Cinzia Cirillo & Kay Axhausen, 2010. "Dynamic model of activity-type choice and scheduling," Transportation, Springer, vol. 37(1), pages 15-38, January.
    6. 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.
    7. 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.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    9. 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.
    10. 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.
    11. Karasmaa, Nina, 2007. "Evaluation of transfer methods for spatial travel demand models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 411-427, June.
    12. 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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