Intelligent Traffic Signal Control Algorithm Based on Sumtree DDPG

被引：0
|
作者：
Huang H. [1 ,3 ,4 ]
Hu Z.-Q. [2 ]
Wang L.-H. [1 ,3 ,4 ]
Lu Z.-M. [1 ,3 ,4 ]
Wen X.-M. [1 ,3 ,4 ]
机构：
[1] School of Information and Communications Engineering, Beijing University of Posts and Telecommunications, Beijing
[2] School of Computer and Information Engineering, Hubei University, Wuhan
[3] Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing
[4] Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing
来源：
Hu, Zhi-Qun (zhiqunhu520@163.com) | 1600年 / Beijing University of Posts and Telecommunications卷 / 44期
关键词：
Deep deterministic policy gradient; Deep reinforcement learning; Multiple intersections; Smart transportation; Traffic signal control;
D O I：
10.13190/j.jbupt.2020-006
中图分类号：
学科分类号：
摘要：
A multi-intersection intelligent traffic signal control algorithm based on sumtree deep deterministic policy gradient(Sumtree DDPG)is proposed. Through real-time observation of intersection data, the cycle length, phase sequence and phase duration of the traffic signal can be intelligently adjusted to improve the efficiency of intersections. Meanwhile, the empirical data storage mode based on sumtree structure can improve the sampling efficiency and accelerate the algorithm convergence. Compared with fixed signal timing and signal timing algorithm based on traffic flow weight, a simulation is carried out that the proposed algorithm obtains good performance in vehicle queue length, vehicle waiting time and vehicle average speed in dynamic environment. © 2021, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
引用
收藏
页码：97 / 103
页数：6
相关论文
共 14 条
  • [1] Araghi S, Khosravi A, Creighton D., A review on computational intelligence methods for controlling traffic signal timing, Expert Systems with Applications, 42, 3, pp. 1538-1550, (2015)
  • [2] Sims A G, Finlay A B., SCATS, splits and offsets simplified (SOS), Australian Road Research, 12, 4, pp. 17-33, (1984)
  • [3] Lo H K., A reliability framework for traffic signal control, IEEE Transactions on Intelligent Transportation Systems, 7, 2, pp. 250-260, (2006)
  • [4] Abdulhai B, Pringle R, Karakoulas G J., Reinforcement learning for true adaptive traffic signal control, Journal of Transportation Engineering, 129, 3, pp. 278-285, (2003)
  • [5] Genders W, Razavi S., Using a deep reinforcement learning agent for traffic signal control
  • [6] Richter S, Aberdeen D, Yu J., Natural actor-critic for road traffic optimisation, NIPS 2006, Proceedings of the 19th International Conference on Neural Information Processing Systems, pp. 1169-1176, (2006)
  • [7] Pang Hali, Gao Weilong, Deep deterministic policy gradient for traffic signal control of single intersection, 2019 Chinese Control and Decision Conference (CCDC), pp. 5861-5866, (2019)
  • [8] Casas N., Deep deterministic policy gradient for urban traffic light control
  • [9] Lillicrap T P, Hunt J J, Pritzel A, Et al., Continuous control with deep reinforcement learning, ICLR 2016: International Conference on Learning Representations, pp. 1-14, (2016)
  • [10] Schaul T, Quan J, Antonoglou I, Et al., Prioritized experience replay, ICLR 2016: International Conference on Learning Representations, pp. 1-21, (2016)
12