A hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data-Reference-Cited by-同舟云学术

A hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data

Published:2023-10-30 Issue: Volume: Page:1-23
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Wei Lei¹²,Chen Peng¹,Mei Yu¹³,Sun Jian⁴,Wang Yunpeng¹

Affiliation:

1. School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, Beihang University, Beijing, China

2. Chair of Traffic Process Automation, Technische Universität Dresden, Dresden, Germany

3. Department of Intelligent Transportation System, Baidu, Beijing, China

4. Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computer Science Applications,Aerospace Engineering,Automotive Engineering,Information Systems,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2023.2270428

Reference40 articles.

1. A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks

2. A real-time network-level traffic signal control methodology with partial connected vehicle information

3. Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

4. Reserve Capacity for a Road Network Under Optimized Fixed Time Traffic Signal Control

5. Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control