Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method

• URL: http://arxiv.org/abs/2602.12296v1
• Date: Sun, 01 Feb 2026 18:51:46 GMT
• Title: Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method
• Authors: Maojiang Deng, Shoufeng Lu, Jiazhao Shi, Wen Zhang,
• Abstract summary: This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO)<n>The proposed variable cell length and multi-channel state representation method excels compared to fixed cell length in optimization performance.
• Score: 9.247530742574316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road partition formula consisting of the sum of logarithmic and linear functions was proposed. The state variables are a vector composed of three channels: the number of vehicles, the average speed, and space occupancy. The set of available signal phases constitutes the action space, the selected phase is executed with a fixed green time. The reward function is formulated using the absolute values of key traffic state metrics - waiting time, speed, and fuel consumption. Each metric is normalized by a typical maximum value and assigned a weight that reflects its priority and optimization direction. The simulation results, using Sumo-TensorFlow-Python, demonstrate a cross-range transferability evaluation and show that the proposed variable cell length and multi-channel state representation method excels compared to fixed cell length in optimization performance.

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