期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 18, 期 11, 页码 3204-3217出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2017.2687620
关键词
Variable speed limit; reinforcement learning; congestion; freeway; bottleneck
资金
- National Natural Science Foundation of China [51508094, 51322810]
- Natural Science Foundation of Jiangsu Province [BK20150612]
The primary objective of this paper was to incorporate the reinforcement learning technique in variable speed limit (VSL) control strategies to reduce system travel time at freeway bottlenecks. A Q-learning (QL)-based VSL control strategy was proposed. The controller included two components: a QL-based offline agent and an online VSL controller. The VSL controller was trained to learn the optimal speed limits for various traffic states to achieve a long-term goal of system optimization. The control effects of the VSL were evaluated using a modified cell transmission model for a freeway recurrent bottleneck. A new parameter was introduced in the cell transmission model to account for the overspeed of drivers in unsaturated traffic conditions. Two scenarios that considered both stable and fluctuating traffic demands were evaluated. The effects of the proposed strategy were compared with those of the feedback-based VSL strategy. The results showed that the proposed QL-based VSL strategy outperformed the feedback-based VSL strategy. More specifically, the proposed VSL control strategy reduced the system travel time by 49.34% in the stable demand scenario and 21.84% in the fluctuating demand scenario.
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