Reinforcement Learning Ramp Metering Control for Weaving Sections in a Connected Vehicle Environment
Weaving sections constitute critical roadway bottlenecks along freeways and highways. The intense lane-changing behavior along these sections significantly reduces the roadway capacity. With the development of wireless communication technologies, connected vehicles (CVs) may be leveraged to develop advanced traffic management systems to increase the capacity of weaving sections. In this paper, the authors develop a ramp-metering control algorithm based on reinforcement learning techniques to increase the weaving section capacity. The control system monitors weaving section arrival rates from connected vehicles and searches for the optimal ramp-metering rate using a novel Q-learning algorithm. Simulation of a single weaving section showed that the system increases the weaving section capacity by approximately 3.5% and significantly mitigates congestion on the mainline freeway. The second example of two weaving sections shows even larger savings. Specifically, the maximum discharge flow rates through the two weaving sections increases by 12% and 20%, respectively. The system also reduces the network-wide fuel consumption and greenhouse gas emission levels by approximately 10%.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Yang, Hao
- Rakha, Hesham
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Conference:
- Transportation Research Board 96th Annual Meeting
- Location: Washington DC, United States
- Date: 2017-1-8 to 2017-1-12
- Date: 2017
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 96th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Algorithms; Control systems; Highway capacity; Machine learning; Mobile communication systems; Ramp metering; Weaving sections
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01628871
- Record Type: Publication
- Report/Paper Numbers: 17-03689
- Files: TRIS, TRB, ATRI
- Created Date: Mar 15 2017 9:09AM