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A Switching-Based Adaptive Dynamic Programming Method to Optimal Traffic Signaling
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DOI：10.1109/TSMC.2019.2930138.png)
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摘要
En 中文
The work presented in this paper concerns a switching-based control formulation for multi-intersection and multiphase traffic light systems. A macroscopic traffic flow modeling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of adaptive dynamic programming (ADP). The main advantage of the switching-based formulation is its capability to determine both when' to switch and which mode to switch on without the need to use the cycle-based average flow approximation typical of state-of-the-art formulations. In addition, the framework can handle different cycle times across intersections without the need for synchronization constraints and, moreover, minimum dwell-time constraints can be directly enforced to comply with minimum green/red times in each phase. The simulation experiments on a multi-intersection and multiphase traffic light systems are presented to show the effectiveness of the method.
Keyword:
Switches
Vehicle dynamics
Optimization
Reinforcement learning
Dynamic programming
Adaptation models
Adaptive dynamic programming (ADP)
dwell-time switching
model-based and switching-based optimization
traffic flow model
traffic signal operation
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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