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Detecting pattern changes in individual travel behavior: A Bayesian approach

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  • Zhao, Zhan
  • Koutsopoulos, Haris N.
  • Zhao, Jinhua

Abstract

Although stable in the short term, individual travel patterns are subject to changes in the long term. The ability to detect such changes is critical for developing behavior models that are adaptive over time. We define travel pattern changes as “abrupt, substantial, and persistent changes in the underlying patterns of travel behavior” and develop a methodology to detect such changes in individual travel patterns. We specify one distribution for each of the three dimensions of travel behavior (the frequency of travel, time of travel, and origins/destinations), and interpret the change of the parameters of the distributions as indicating the occurrence of a pattern change. A Bayesian method is developed to estimate the probability that a pattern change occurs at any given time for each behavior dimension. The proposed methodology is tested using pseudonymized smart card records of 3210 users from London, U.K. over two years. The results show that the method can successfully identify significant changepoints in travel patterns. Compared to the traditional generalized likelihood ratio (GLR) approach, the Bayesian method requires less predefined parameters and is more robust. The methodology presented in this paper is generalizable and can be applied to detect changes in other aspects of travel behavior and human behavior in general.

Suggested Citation

  • Zhao, Zhan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2018. "Detecting pattern changes in individual travel behavior: A Bayesian approach," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 73-88.
    • Handle: RePEc:eee:transb:v:112:y:2018:i:c:p:73-88
    DOI: 10.1016/j.trb.2018.03.017
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    References listed on IDEAS

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    Cited by:

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    4. Lin, Yuqian & Xu, Yang & Zhao, Zhan & Tu, Wei & Park, Sangwon & Li, Qingquan, 2024. "Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    5. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    6. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
    7. Krause, Cory M. & Zhang, Lei, 2019. "Short-term travel behavior prediction with GPS, land use, and point of interest data," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 349-361.
    8. Chen, Xian & Bai, Shuotian & Wei, Yongqin & Zhao, Yanhui & Yan, Peng & Jiang, Hai, 2023. "Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).

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