Distributed Multiagent Deep Reinforcement Learning for Multiline Dynamic Bus Timetable Optimization-Reference-Cited by-同舟云学术

Distributed Multiagent Deep Reinforcement Learning for Multiline Dynamic Bus Timetable Optimization

Published:2023-01 Issue:1 Volume:19 Page:469-479
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Yan Haoyang¹^ORCID,Cui Zhiyong²^ORCID,Chen Xinqiang³^ORCID,Ma Xiaolei¹^ORCID

Affiliation:

1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control and Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China

2. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA

3. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, China

Funder

National Key R&D Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9944026/09733265.pdf?arnumber=9733265

Reference30 articles.

1. Microscopic Traffic Simulation using SUMO

2. Robust timetable optimization for bus lines subject to resource and regulatory constraints

3. Optimization of single-line bus timetables considering time-dependent travel times: A case study of Beijing, China

4. A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm

5. Human-level control through deep reinforcement learning;mnih;Nature,2015

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