Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning

Published:2024-09 Issue:9 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Wen Longhui¹,Zhou Wei²^ORCID,Liu Jiajun³,Ren Gang⁴,Zhang Ning⁵

Affiliation:

1. Ph.D. Candidate, Intelligent Transportation System Research Center, Southeast Univ., 2 Southeast University Rd., Nanjing 211189, China; Ph.D. Candidate, School of Transportation, Southeast Univ., 2 Southeast University Rd., Nanjing 211189, China.

2. Ph.D. Candidate, Intelligent Transportation System Research Center, Southeast Univ., 2 Southeast University Rd., Nanjing 211189, China. ORCID: .

3. Ph.D. Candidate, Intelligent Transportation System Research Center, Southeast Univ., 2 Southeast University Rd., Nanjing 211189, China.

4. Professor, School of Transportation, Southeast Univ., 2 Southeast University Rd., Nanjing 211189, China.

5. Associate Professor, Intelligent Transportation System Research Center, Southeast Univ., 2 Southeast University Rd., Nanjing 211189, China (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8407

Reference35 articles.

1. Bertsekas, D. 2019. Reinforcement learning and optimal control. Nashua, NH: Athena Scientific.

2. Chang C. S. and C. M. Kwan. 2004. “Evaluation of evolutionary algorithms for multi-objective train schedule optimization.” In Proc. Australasian Joint Conf. on Artificial Intelligence 803–815. Berlin: Springer.

3. Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization;Chu W.;Adv. Mech. Eng.,2016

4. Multiagent System–Based Near-Real-Time Trajectory and Microscopic Timetable Optimization for Rail Transit Network

5. Energy Efficient Metro Train Running Time Rescheduling Model for Fully Automatic Operation Lines

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