Abstract：The adaptive traffic signal control method is adopted to effectively control the traffic lights at the urban road junctions, with the rapid growth of the traffic flow in Shenzhen. Shenzhen traffic police asked for a real-time, distributed and adaptive control on the basis of the self-developed smooth signal control. Joint innovation has developed the reinforcement learning based on the deep neural network. Through online learning of various traffic loads, and the real-time reasoning, the information control period, phase, phase sequence, signal cycle, split and phase difference are calculated. This paper reviews the reinforcement learning model used in the traffic signal control, and makes an evaluation on the spot.
刘义, 何均宏. 强化学习在城市交通信号灯控制方法中的应用[J]. 科技导报, 2019, 37(6): 84-90.
LIU Yi, HE Junhong. A survey of the application of reinforcement learning in urban traffic signal control methods. Science & Technology Review, 2019, 37(6): 84-90.
[1] 陆化普. 大数据及其在城市智能交通系统中的应用综述[J]. 交通运输系统工程与信息, 2015(10):45-51.Lu Huapu. Big data and its applications in urban intelligent transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2015(10):45-51.
[2] 杨文臣, 张轮, Zhu Feng. 多智能体强化学习在城市交通网络信号控制方法中的应用综述[J]. 计算机应用研究, 2018, 35(6):101-114. Yang Wenchen, Zhang Lun, Zhu Feng. Multi-agent reinforcement learning based traffic signal control for integrated urban network:Survey of state of art[J]. Application Research of Computers, 2018, 35(6):101-114.
[3] Li L, Lv Y S, Wang F Y. Traffic signal timing via deep reinforcement learning[J]. Acta Automatica Sinica, 2016, 3(3):247-254.
[4] Hamilton A, Waterson B, Cherrett T, et al. The evolution of urban traffic control:Changing policy and technology[J]. Transportation Planning & Technology, 2013, 36(1):24-43.
[5] Zhang J, Wang F Y, Wang K, et al. Data-driven intelligent transportation systems:A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4):1624-1639.
[6] Wu X, Liu H X. Using high-resolution event-based data for traffic modeling and control:An overview[J]. Transportation Research Part C, 2014, 42(2):28-43.
[7] Yau K L A, Qadir J, Khoo H L, et al. A Survey on reinforcement learning models and algorithms for traffic signal control[J]. ACM Computing Surveys, 2017, 50(3):1-38.
[8] Azimirad E, Pariz N, Sistani M B N. A novel fuzzy model and control of single intersection at urban traffic network[J]. IEEE Systems Journal, 2010, 4(1):107-111.
[9] Balaji P G, German X, Srinivasan D. Urban traffic signal control using reinforcement learning agents[J]. IET Intelligent Transport Systems, 2010, 4(3):177-188.
[10] Sutton R S, Barto A G. Reinforcement learning:An introduction[J]. IEEE Transactions on Neural Networks, 1998, 9(5):1054.
[11] Watkins C J C H, Dayan P. Q-learning[J]. Machine Learning, 1992, 8(3/4):279-292.
[12] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[13] Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540):529-533.
[14] Genders W, Razavi S. Using a deep reinforcement learning agent for traffic signal control[J]. arXiv preprint, 2016, arXiv:1611.01142.
[15] Tran D, Toulis P, Airoldi E M. Stochastic gradient descent methods for estimation with large data sets[J]. arXiv preprint, 2015, arXiv:1509.06459.
[16] Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning[J]. arXiv preprint, 2016, arXiv:1509.02971.
2019, Vol. 37 
