Recursive logit-based meta-inverse reinforcement learning for driver-preferred route planning
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DOI: 10.1016/j.tre.2024.103485
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- 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.
- Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
- Dieter, Peter & Caron, Matthew & Schryen, Guido, 2023. "Integrating driver behavior into last-mile delivery routing: Combining machine learning and optimization in a hybrid decision support framework," European Journal of Operational Research, Elsevier, vol. 311(1), pages 283-300.
- 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).
- Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
- 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.
- Mai, Tien & Frejinger, Emma & Fosgerau, Mogens, 2015. "A nested recursive logit model for route choice analysis," MPRA Paper 63161, University Library of Munich, Germany.
- 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.
- 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.
- Fosgerau, Mogens & Frejinger, Emma & Karlström, Anders, 2013. "A link based network route choice model with unrestricted choice set," Working papers in Transport Economics 2013:10, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
- Fosgerau, Mogens & Frejinger, Emma & Karlstrom, Anders, 2013. "A link based network route choice model with unrestricted choice set," MPRA Paper 48707, University Library of Munich, Germany.
- Zhang, Pujun & Liu, Shan & Shi, Jia & Chen, Liying & Chen, Shuiping & Gao, Jiuchong & Jiang, Hai, 2023. "Route planning using divide-and-conquer: A GAT enhanced insertion transformer approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 176(C).
- 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.
- 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).
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Keywords
Route planning; Inverse reinforcement learning; Meta-learning; Recursive logit;All these keywords.
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