Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach-Reference-Cited by-同舟云学术

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Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach

Published:2024-10 Issue: Volume:188 Page:103067
ISSN:0191-2615
Container-title:Transportation Research Part B: Methodological
language:en
Short-container-title:Transportation Research Part B: Methodological

Author:

Ying Chengshuo^ORCID,Chow Andy H.F.^ORCID,Yan Yimo,Kuo Yong-Hong^ORCID,Wang Shouyang

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Research Grants Council, University Grants Committee

General Research Fund of Shanghai Normal University

Publisher

Elsevier BV

Reference68 articles.

1. Solving the train dispatching problem via deep reinforcement learning;Agasucci;J. Rail Transp. Plan. Manage.,2023

2. Reinforcement Learning and Optimal Control;Bertsekas,2019

3. A matheuristic for the integrated disruption management of traffic, passengers and stations in urban railway lines;Bešinović;IEEE Trans. Intell. Transp. Syst.,2022

4. The multi-objective railway timetable rescheduling problem;Binder;Transp. Res. C,2017

5. An overview of recovery models and algorithms for real-time railway rescheduling;Cacchiani;Transp. Res. B,2014

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