Variable Speed Limit Control for Mixed Traffic Flow on Highways Based on Deep-Reinforcement Learning-Reference-Cited by-同舟云学术

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Variable Speed Limit Control for Mixed Traffic Flow on Highways Based on Deep-Reinforcement Learning

Published:2024-03 Issue:3 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:

Gao Heyao¹^ORCID,Jia Hongfei²,Wu Ruiyi¹^ORCID,Huang Qiuyang³,Tian Jingjing¹^ORCID,Liu Chao¹^ORCID,Wang Xiaochao³

Affiliation:

1. Graduate Student, School of Transportation, Jilin Univ., Changchun 130022, China. ORCID:

2. Professor, School of Transportation, Jilin Univ., Changchun 130022, China (corresponding author).

3. Graduate Student, School of Transportation, Jilin Univ., Changchun 130022, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8116

Reference45 articles.

1. Ahmic K. A. Tahirbegovic A. Tahirovic D. Watzenig and G. Stettinger. 2020. “Simulation framework for platooning based on gazebo and SUMO.” In Proc. 2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVs) New York: IEEE.

2. Variable Speed Limits: Safety and Operational Impacts of a Candidate Control Strategy for Freeway Applications

3. Operational and safety impacts of integrated variable speed limit with dynamic hard shoulder running;Arora K.;J. Intell. Transport. Syst.,2023

4. Testbed of V2X infrastructure for autonomous vehicles;Bouchemal N.;Ann. Telecommun.,2021

5. Coalitional model predictive control on freeways traffic networks;Chanfreut P.;IEEE Trans. Intell. Transp. Syst.,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Q‐network learning‐based active speed management under autonomous driving environments;Computer-Aided Civil and Infrastructure Engineering;2024-06-03

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