ACDRL: An actor–critic deep reinforcement learning approach for solving the energy-aimed train timetable rescheduling problem under random disturbances-Reference-Cited by-同舟云学术

ACDRL: An actor–critic deep reinforcement learning approach for solving the energy-aimed train timetable rescheduling problem under random disturbances

Published:2022-11 Issue: Volume:8 Page:1350-1357
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Liao Jinlin^ORCID,Wu Guilian,Chen Hao,Ni Shiyuan,Lin Tingting,Tang Lu

Publisher

Elsevier BV

Subject

General Energy

Reference20 articles.

1. Cooperative control of metro trains to minimize net energy consumption;Bai;IEEE Trans. Intell. Transp. Syst.,2020

2. J. Cai, J. Xun, X. Xiao, X. Liu, X. Ji, Timetable Optimization Model with Considering Regenerative Braking Power for Urban Rail, in: 2020 Chinese Automation Congress, CAC, 2020, pp. 3018–3023.

3. @e tractive resistance of electric locomotives and cars, general electric, boston, MA, USA;Davis,1926

4. I.E. Demirci, H.B. Celikoglu, Timetable Optimization for Utilization of Regenerative Braking Energy: A Single Line Case over Istanbul Metro Network, in: 2018 21st International Conference on Intelligent Transportation Systems, ITSC, 2018, pp. 2309–2314.

5. Timetable recovery after disturbances in metro operations: An exact and efficient solution;Gkiotsalitis;IEEE Trans. Intell. Transp. Syst.,2022

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