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

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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.

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Acknowledgements

The authors are grateful to Neil Kilgren and Carol Naito affiliated with the Puget Sound Regional Council for providing Puget Sound Transportation Panel data and supplemented Puget Sound regional skimming matrices. This research is financially supported by the National Science Foundation (NSF) and U.S. Department of Energy (DOE). We would like to acknowledge the research sponsors. The opinions in this paper do not necessarily reflect the official views of NSF or U.S. DOE. We are solely responsible for all statements in this paper.

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Authors and Affiliations

  1. Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA

    Chenfeng Xiong, Di Yang & Lei Zhang

  2. Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, OH, USA

    Jiaqi Ma

  3. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China

    Xiqun Chen

Authors
  1. Chenfeng Xiong
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  2. Di Yang
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  3. Jiaqi Ma
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  4. Xiqun Chen
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  5. Lei Zhang
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Contributions

CX: methodology development (lead), manuscript writing; DY: meta-analysis, data cleaning/processing (lead), model estimation, manuscript writing; JM: literature search and review, result check and validation, editing; XC: data collection, pre-processing for supplement data, editing; LZ: content planning, methodology development.

Corresponding author

Correspondence to Di Yang.

Appendix

Appendix

See Figs. 5, 6, 7 and 8.

Fig. 5
figure 5

Estimated carpool choice conditional log-odd densities versus the actual densities of the test data (wave 7, 8, 9, and 10)

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Fig. 6
figure 6

Estimated transit choice conditional log-odd densities versus the actual densities of the test data (wave 7, 8, 9, and 10)

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Fig. 7
figure 7

Estimated walk choice conditional log-odd densities versus the actual densities of the test data (wave 7, 8, 9, and 10)

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Fig. 8
figure 8

Estimated bike choice conditional log-odd densities versus the actual densities of the test data (wave 7, 8, 9, and 10)

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Xiong, C., Yang, D., Ma, J. et al. Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis. Transportation 47, 585–605 (2020). https://doi.org/10.1007/s11116-018-9900-9

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  • DOI: https://doi.org/10.1007/s11116-018-9900-9

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