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A method of integrating correlation structures for a generalized recursive route choice model

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  • Mai, Tien

Abstract

We propose a way to estimate a generalized recursive route choice model. The model generalizes other existing recursive models in the literature, i.e., (Fosgerau et al., 2013b; Mai et al., 2015c), while being more flexible since it allows the choice at each stage to be any member of the network multivariate extreme value (network MEV) model (Daly and Bierlaire, 2006). The estimation of the generalized model requires defining a contraction mapping and performing contraction iterations to solve the Bellman’s equation. Given the fact that the contraction mapping is defined based on the choice probability generating functions (CPGF) (Fosgerau et al., 2013b) generated by the network MEV models, and these CPGFs are complicated, the generalized model becomes difficult to estimate. We deal with this challenge by proposing a novel method where the network of correlation structures and the structure parameters given by the network MEV models are integrated into the transport network. The approach allows to simplify the contraction mapping and to make the estimation practical on real data.

Suggested Citation

  • Mai, Tien, 2016. "A method of integrating correlation structures for a generalized recursive route choice model," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 146-161.
    • Handle: RePEc:eee:transb:v:93:y:2016:i:pa:p:146-161
    DOI: 10.1016/j.trb.2016.07.016
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    Cited by:

    1. Oyama, Yuki & Hato, Eiji, 2019. "Prism-based path set restriction for solving Markovian traffic assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 528-546.
    2. Mai, Tien & Frejinger, Emma & Fosgerau, Mogens & Bastin, Fabian, 2017. "A dynamic programming approach for quickly estimating large network-based MEV models," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 179-197.
    3. Malvika Dixit & Oded Cats & Niels van Oort & Ties Brands & Serge Hoogendoorn, 2024. "Validation of a multi-modal transit route choice model using smartcard data," Transportation, Springer, vol. 51(5), pages 1809-1829, October.
    4. Tran, Hung & Mai, Tien, 2024. "Network-based representations and dynamic discrete choice models for multiple discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 184(C).
    5. Mogens Fosgerau & Nikolaj Nielsen & Mads Paulsen & Thomas Kj{ae}r Rasmussen & Rui Yao, 2024. "Substitution in the perturbed utility route choice model," Papers 2409.08347, arXiv.org.
    6. Mai, Tien & Bastin, Fabian & Frejinger, Emma, 2017. "On the similarities between random regret minimization and mother logit: The case of recursive route choice models," Journal of choice modelling, Elsevier, vol. 23(C), pages 21-33.
    7. Evanthia Kazagli & Michel Bierlaire & Matthieu de Lapparent, 2020. "Operational route choice methodologies for practical applications," Transportation, Springer, vol. 47(1), pages 43-74, February.
    8. Mai, Tien & Bui, The Viet & Nguyen, Quoc Phong & Le, Tho V., 2023. "Estimation of recursive route choice models with incomplete trip observations," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 313-331.
    9. Oyama, Yuki & Hara, Yusuke & Akamatsu, Takashi, 2022. "Markovian traffic equilibrium assignment based on network generalized extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 135-159.
    10. Oskar Blom Västberg & Anders Karlström & Daniel Jonsson & Marcus Sundberg, 2020. "A Dynamic Discrete Choice Activity-Based Travel Demand Model," Transportation Science, INFORMS, vol. 54(1), pages 21-41, January.
    11. Papola, Andrea & Tinessa, Fiore & Marzano, Vittorio, 2018. "Application of the Combination of Random Utility Models (CoRUM) to route choice," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 304-326.
    12. Fiore Tinessa & Vittorio Marzano & Andrea Papola, 2021. "Choice probabilities and correlations in closed-form route choice models: specifications and drawbacks," Papers 2110.07224, arXiv.org.
    13. Tien Mai & The Viet Bui & Quoc Phong Nguyen & Tho V. Le, 2022. "Estimation of Recursive Route Choice Models with Incomplete Trip Observations," Papers 2204.12992, arXiv.org.
    14. 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.
    15. Yuki Oyama, 2022. "Capturing positive network attributes during the estimation of recursive logit models: A prism-based approach," Papers 2204.01215, arXiv.org, revised Jan 2023.
    16. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.
    17. Knies, Austin & Lorca, Jorge & Melo, Emerson, 2022. "A recursive logit model with choice aversion and its application to transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 47-71.
    18. Tien Mai & Fabian Bastin & Emma Frejinger, 2018. "A decomposition method for estimating recursive logit based route choice models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 253-275, September.

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