Design of Intersection Control Algorithm Based on Deep Reinforcement Learning

Yi Zheng, Xiaohu Yang, Anping Wang, Fei Li, Chen Li () and Yaojun Gui
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Yi Zheng: Yunnan Communications Investment Group Investment Co., Ltd.
Xiaohu Yang: Yunnan Communications Investment Group Investment Co., Ltd.
Anping Wang: Yunnan Xuanhui Expressway Co., Ltd.
Fei Li: Yunnan Communications Investment Group Public Construction & Bridge Engineering Co., Ltd.
Chen Li: YCIC Broadvision Engineering Cousultants
Yaojun Gui: YCIC Broadvision Engineering Cousultants

A chapter in Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026), 2026, pp 295-301 from Springer

Abstract: Abstract Traditional traffic signal control (TSC) schemes rely on fixed timing or rule‑based adaptation, which lack real‑time responsiveness to stochastic and highly dynamic traffic flows. Deep reinforcement learning (DRL) has become a promising paradigm for adaptive signal control, yet value‑based methods such as DQN and DDQN suffer from overestimation bias, unstable training, and low sampling efficiency in complex urban intersection environments. This paper proposes a PPO-GAE control framework, which combines Proximal Policy Optimization (PPO) with Generalized Advantage Estimation (GAE) to achieve stable, efficient, and robust traffic signal timing optimization. PPO constrains policy updates within a trust region to avoid destructive updates and ensure training stability, while GAE effectively reduces gradient variance and improves the accuracy of advantage estimation for continuous traffic states. Experiments are carried out on the SUMO simulation platform using a real intersection in Xi’an. Results show that the proposed PPO-GAE method reduces cumulative delay by 41.2% and average queue length by 33.7% compared with the fixed‑time baseline, and outperforms DQN, DDQN-PER, and traditional adaptive methods.

Keywords: Intelligent Traffic Signal Control; Deep Reinforcement Learning; Proximal Policy Optimization; Generalized Advantage Estimation; SUMO; Urban Traffic Efficiency (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-689-0_27

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