Design of Intersection Control Algorithm Based on Deep Reinforcement Learning

DOI

10.2991/978-94-6239-689-0_27How to use a DOI?

Keywords

Intelligent Traffic Signal Control; Deep Reinforcement Learning; Proximal Policy Optimization; Generalized Advantage Estimation; SUMO; Urban Traffic Efficiency

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yi Zheng
AU  - Xiaohu Yang
AU  - Anping Wang
AU  - Fei Li
AU  - Chen Li
AU  - Yaojun Gui
PY  - 2026
DA  - 2026/05/28
TI  - Design of Intersection Control Algorithm Based on Deep Reinforcement Learning
BT  - Proceedings of the 2026 2nd International Conference on Data Mining and Project Management (DMPM 2026)
PB  - Atlantis Press
SP  - 295
EP  - 301
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-689-0_27
DO  - 10.2991/978-94-6239-689-0_27
ID  - Zheng2026
ER  -