IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v164y2022ics1366554522001715.html
   My bibliography  Save this article

Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions

Author

Listed:
  • Liu, Shan
  • Jiang, Hai

Abstract

Personalized route recommendation aims to recommend routes based on users’ route preference. The vast amount of GPS trajectories tracking driving behavior has made deep learning, especially inverse reinforcement learning (IRL), a popular choice for personalized route recommendation. However, current IRL studies assume that the traffic condition is static and approximate the expected state visitation frequencies to update the neural network. This study improves the IRL to recommend personalized routes considering real-time traffic conditions. We also improve the expected state visitation frequency calculation based on characteristics of ride-hailing and taxi trajectories to calculate the gradient of the neural network. In addition, the graph attention network is employed to capture the spatial dependencies between road segments. Numerical experiments using real ride-hailing trajectories in Chengdu, China validate our model. At last, a statistical test is conducted, and route preferences reflected by the same driver’s empty trajectories and occupied trajectories are found to have significant differences.

Suggested Citation

  • Liu, Shan & Jiang, Hai, 2022. "Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
  • Handle: RePEc:eee:transe:v:164:y:2022:i:c:s1366554522001715
    DOI: 10.1016/j.tre.2022.102780
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554522001715
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tre.2022.102780?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    2. Yang, Lin & Kwan, Mei-Po & Pan, Xiaofang & Wan, Bo & Zhou, Shunping, 2017. "Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 1-27.
    3. Mai, Tien & Fosgerau, Mogens & Frejinger, Emma, 2015. "A nested recursive logit model for route choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 100-112.
    4. Broach, Joseph & Dill, Jennifer & Gliebe, John, 2012. "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1730-1740.
    5. Fosgerau, Mogens & Frejinger, Emma & Karlstrom, Anders, 2013. "A link based network route choice model with unrestricted choice set," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 70-80.
    6. Kotiloglu, S. & Lappas, T. & Pelechrinis, K. & Repoussis, P.P., 2017. "Personalized multi-period tour recommendations," Tourism Management, Elsevier, vol. 62(C), pages 76-88.
    7. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    2. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    3. Zhang, Pujun & Lei, Dazhou & Liu, Shan & Jiang, Hai, 2024. "Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Oijen, Tim P. & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "Estimation of a recursive link-based logit model and link flows in a sensor equipped network," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 262-281.
    2. 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.
    3. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    4. Zhang, Pujun & Lei, Dazhou & Liu, Shan & Jiang, Hai, 2024. "Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    5. 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.
    6. Wong, Melvin & Farooq, Bilal & Bilodeau, Guillaume-Alexandre, 2016. "Next Direction Route Choice Model for Cyclist Using Panel Data," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319265, Transportation Research Forum.
    7. Stefan Flügel & Nina Hulleberg & Aslak Fyhri & Christian Weber & Gretar Ævarsson, 2019. "Empirical speed models for cycling in the Oslo road network," Transportation, Springer, vol. 46(4), pages 1395-1419, August.
    8. Anowar, Sabreena & Eluru, Naveen & Hatzopoulou, Marianne, 2017. "Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 66-78.
    9. Mogens Fosgerau & Mads Paulsen & Thomas Kj{ae}r Rasmussen, 2021. "A perturbed utility route choice model," Papers 2103.13784, arXiv.org, revised Sep 2021.
    10. Ospina, Juan P. & Duque, Juan C. & Botero-Fernández, Verónica & Montoya, Alejandro, 2022. "The maximal covering bicycle network design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 222-236.
    11. Paulsen, Mads & Rich, Jeppe, 2023. "Societally optimal expansion of bicycle networks," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    12. Meyer de Freitas, Lucas & Becker, Henrik & Zimmermann, Maëlle & Axhausen, Kay W., 2019. "Modelling intermodal travel in Switzerland: A recursive logit approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 200-213.
    13. Yao, Rui & Bekhor, Shlomo, 2022. "A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 273-294.
    14. Meister, Adrian & Felder, Matteo & Schmid, Basil & Axhausen, Kay W., 2023. "Route choice modeling for cyclists on urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    15. Evanthia Kazagli & Michel Bierlaire & Matthieu de Lapparent, 2020. "Operational route choice methodologies for practical applications," Transportation, Springer, vol. 47(1), pages 43-74, February.
    16. McArthur, David Philip & Hong, Jinhyun, 2019. "Visualising where commuting cyclists travel using crowdsourced data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 233-241.
    17. Li, Dawei & Feng, Siqi & Song, Yuchen & Lai, Xinjun & Bekhor, Shlomo, 2023. "Asymmetric closed-form route choice models: Formulations and comparative applications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    18. 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.
    19. 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.
    20. Nuñez, Javier Yesid Mahecha & Bisconsini, Danilo Rinaldi & Rodrigues da Silva, Antônio Nélson, 2020. "Combining environmental quality assessment of bicycle infrastructures with vertical acceleration measurements," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 447-458.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:164:y:2022:i:c:s1366554522001715. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.