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Agent-based Real-Time Signal Coordination in Congested Networks.
| 题名： | Agent-based Real-Time Signal Coordination in Congested Networks. |
| 作者： | Benekohal, R. F.; Medina, J. C. |
| 关键词： | Case Studies; Dynamic Programming; Implementation; Learning Methods; Performance Evaluation; Real Time Operations; Research Project; Traffic Congestion; Traffic Control; Traffic Signal Coordination; T |
| 摘要： | This study is the continuation of a previous NEXTRANS study on agent-based reinforcement learning methods for signal coordination in congested networks. In the previous study, the formulation of a real-time agent-based traffic signal control in oversaturated networks was described and exemplified through a case study. The agent-based control was implemented using two different reinforcement learning algorithms: Q-learning and approximate dynamic programming. Also, the performance of the network was evaluated using the max-plus algorithm to provide explicit coordination between the agents. The RL algorithms and max-plus showed satisfactory performance and were able to efficiently process traffic, reducing the frequency of queue spillbacks and preventing gridlocks. This study extends the previous implementations and describes the use of explicit coordinating mechanisms with Q-learning, mainly through a modified max-plus version developed throughout this research project. |
| 报告类型： | 科技报告 |
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