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An exploratory study of instance-based learning for route choice with random travel times

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  • Tang, Yue
  • Gao, Song
  • Ben-Elia, Eran

Abstract

Availability of individual-level longitudinal data provides the opportunity to better understand travelers' day-to-day learning behavior, enabling more accurate predictions of traffic patterns in a network with random travel times. In this paper, an instance-based learning (IBL) model that can capture the recency, hot stove and payoff variability effects embedded in travelers' day-to-day learning processes is developed for route-choice based on the power law of forgetting and practice. Experiments based on synthetic datasets show that the true parameter values of the IBL model can be consistently retrieved and the model can potentially predict different traffic patterns compared to non-learning models. The IBL model is compared with a baseline learning model using an experimental dataset of repeated route-choice. Estimation results show that the IBL model reveals higher sensitivity to perceived travel time and achieves better model fit. Cross validation experiments suggest that the forecasting ability of the IBL model is consistently better than the baseline learning model. Practical considerations for choice modeling are further discussed.

Suggested Citation

  • Tang, Yue & Gao, Song & Ben-Elia, Eran, 2017. "An exploratory study of instance-based learning for route choice with random travel times," Journal of choice modelling, Elsevier, vol. 24(C), pages 22-35.
  • Handle: RePEc:eee:eejocm:v:24:y:2017:i:c:p:22-35
    DOI: 10.1016/j.jocm.2017.03.004
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    2. Guevara, C. Angelo & Tang, Yue & Gao, Song, 2018. "The initial condition problem with complete history dependency in learning models for travel choices," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 850-861.
    3. 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.
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    5. Prateek Bansal & Daniel Hörcher & Daniel J. Graham, 2022. "A dynamic choice model to estimate the user cost of crowding with large‐scale transit data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 615-639, April.

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