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A latent-class adaptive routing choice model in stochastic time-dependent networks

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Listed:
  • Ding-Mastera, Jing
  • Gao, Song
  • Jenelius, Erik
  • Rahmani, Mahmood
  • Ben-Akiva, Moshe

Abstract

Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler’s ability to look ahead in order to incorporate real-time information not yet available at the time of decision.

Suggested Citation

  • Ding-Mastera, Jing & Gao, Song & Jenelius, Erik & Rahmani, Mahmood & Ben-Akiva, Moshe, 2019. "A latent-class adaptive routing choice model in stochastic time-dependent networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 1-17.
    • Handle: RePEc:eee:transb:v:124:y:2019:i:c:p:1-17
    DOI: 10.1016/j.trb.2019.03.018
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    References listed on IDEAS

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    Cited by:

    1. Mikkel Thorhauge & Akshay Vij & Elisabetta Cherchi, 2021. "Heterogeneity in departure time preferences, flexibility and schedule constraints," Transportation, Springer, vol. 48(4), pages 1865-1893, August.
    2. Yuki Oyama, 2023. "Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes," Papers 2307.08646, arXiv.org.
    3. Marra, Alessio Daniele & Corman, Francesco, 2020. "Determining an efficient and precise choice set for public transport based on tracking data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 168-186.
    4. Mai, Tien & Yu, Xinlian & Gao, Song & Frejinger, Emma, 2021. "Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 42-58.

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