期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 50, 期 11, 页码 4160-4170出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2930138
关键词
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
资金
- Fundamental Research Funds for the Central Universities (RECON-STRUCT) [4007019109]
- Special Guiding Funds for Double First-Class [4007019201]
- National Natural Science Foundation of China [61673107, 61833005]
- National Ten Thousand Talent Program for Young Top-Notch Talents [W2070082]
- General Joint Fund of the Equipment Advance Research Program of Ministry of Education [6141A020223]
- Jiangsu Provincial Key Laboratory of Networked Collective Intelligence [BM2017002]
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.
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