Performance of State-Shared Multiagent Deep Reinforcement Learning Controlled Signal Corridor with Platooning-Based CAVs-Reference-Cited by-同舟云学术

Performance of State-Shared Multiagent Deep Reinforcement Learning Controlled Signal Corridor with Platooning-Based CAVs

Published:2023-08 Issue:8 Volume:149 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:

Song Li¹^ORCID,Fan Wei “David”²^ORCID

Affiliation:

1. Associate Professor, School of Transportation and Logistics Engineering, Wuhan Univ. of Technology, Wuhan, Civil Bldg., Room 404, 1178 Heping Blvd., Wuhan 430063, China; Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of North Carolina at Charlotte EPIC Bldg., Room 3366, 9201 University City Blvd., Charlotte, NC 28223-0001. ORCID: .

2. Director, USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Charlotte, NC 28223-0001; Professor, Dept. of Civil and Environmental Engineering, Univ. of North Carolina at Charlotte, EPIC Bldg., Room 3261, 9201 University City Blvd., Charlotte, NC 28223-0001 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-7768

Reference27 articles.

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3. Ault J. and G. Sharon. 2021. “Reinforcement learning benchmarks for traffic signal control.” In Proc. NeurIPS 2021. La Jolla CA: Neural Information Processing Systems Foundation.

4. Multi-agent deep reinforcement learning for large-scale traffic signal control;Chu T.;IEEE Trans. Intell. Transp. Syst.,2020

5. Harth Michael L. M. and K. Bogenberger. 2021. “Automated calibration of traffic demand and traffic lights in SUMO using real-world observations.” In Vol. 2 of Proc. SUMO Conf. 133–148. Hanover Germany: TIB Open Publishing.

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