Traffic signal timing via deep reinforcement learning

被引：377
作者：
Li L. [1 ,2 ]
Lv Y. [3 ]
Wang F.-Y. [3 ]
机构：
[1] Department of Automation, Tsinghua University, Beijing
[2] Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing
[3] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
来源：
Li, Li (li-li@tsinghua.edu.cn) | 1600年 / Institute of Electrical and Electronics Engineers Inc.卷 / 03期
关键词：
deep learning; deep reinforcement learning; reinforcement learning; Traffic control;
D O I：
10.1109/JAS.2016.7508798
中图分类号：
学科分类号：
摘要：
In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed. © 2014 Chinese Association of Automation.
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收藏
页码：247 / 254
页数：7
相关论文
共 32 条
  • [1] Mirchandani P., Head L., A real-Time traffic signal control system: Architecture, algorithms, and analysis, Transportation Research, Part C: Emerging Technologies, 9, 6, pp. 415-432, (2001)
  • [2] Papageorgiou M., Diakaki C., Dinopoulou V., Kotsialos A., Wang Y.B., Review of road traffic control strategies, Proceedings of the IEEE, 91, 12, pp. 2043-2067, (2003)
  • [3] Mirchandani P., Wang F.Y., Rhodes to intelligent transportation systems, IEEE Intelligent Systems, 20, 1, pp. 10-15, (2005)
  • [4] Chen B., Cheng H.H., A review of the applications of agent technology in traffic and transportation systems, IEEE Transactions on Intelligent Transportation Systems, 11, 2, pp. 485-497, (2010)
  • [5] Li L., Wen D., Yao D.Y., A survey of traffic control with vehicular communications, IEEE Transactions on Intelligent Transportation Systems, 15, 1, pp. 425-432, (2014)
  • [6] Bellemans T., De Schutter B., De Moor B., Model predictive control for ramp metering of motorway traffic: A case study, Control Engineering Practice, 14, 7, pp. 757-767, (2006)
  • [7] Timotheou S., Panayiotou C.G., Polycarpou M.M., Distributed traffic signal control using the cell transmission model via the alternating direction method of multipliers, IEEE Transactions on Intelligent Transportation Systems, 16, 2, pp. 919-933, (2015)
  • [8] Wang F.Y., Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, IEEE Transactions on Intelligent Transportation Systems, 11, 3, pp. 630-638, (2010)
  • [9] Wang F.Y., Agent-based control for networked traffic management systems, IEEE Intelligent Systems, 20, 5, pp. 92-96, (2005)
  • [10] Li L., Wen D., Parallel systems for traffic control: A rethinking, IEEE Transactions on Intelligent Transportation Systems, 17, 4, pp. 1179-1182, (2015)
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