Traffic signal priority control based on deep reinforcement learning

Abstract

Abstract: The existing traffic signal priority control system cannot effectively adapt to the increasingly complex and changing urban traffic environment, and is prone to cause unnecessary delays to non-priority vehicles when implementing traffic signal priority control. To address this shortcoming, this study proposes improvements from both the control strategy and optimization objectives. Based on the real-time traffic information of intersections and combining the characteristics of deep reinforcement learning to efficiently process high-dimensional continuous data and self-learning, a priority control strategy based on deep reinforcement learning is proposed to improve the deep double-Q network based on a dueling architecture to solve the signal priority control strategy. At the same time, a dual-objective optimization incentive function is constructed to balance the access needs of both public transport vehicles and general traffic,aiming to reduce delays of public transport vehicles while maximizing the capacity of intersections. Finally, a simulation environment is built using the SUMO to compare the improved algorithm with the three base class algorithms of D3QN,DDQN and Q-Learning. The experimental results show that the improved algorithm can effectively improve the access efficiency of buses and social vehicles in a coordinated manner.

Key words: transit signal priority, traffic signal control, deep reinforcement learning, SUMO simulation platform

CLC Number:

WANG Zhi-wen, YU Yu-ling, YANG Kang-kang, WANG Hao-xu, MIAO Wei. Traffic signal priority control based on deep reinforcement learning[J]. Journal of Lanzhou University of Technology, 2026, 52(3): 91-101.

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URL: https://journal.lut.edu.cn/EN/

https://journal.lut.edu.cn/EN/Y2026/V52/I3/91

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