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

TL;DR

This paper introduces a novel adaptive traffic signal control method using deep reinforcement learning with a unique road partitioning and multi-channel state representation, improving traffic flow optimization.

Contribution

It presents a new road partition formula and multi-channel state representation for reinforcement learning-based traffic control, enhancing performance over fixed cell length methods.

Findings

Improved traffic flow optimization compared to fixed cell length methods.

Demonstrated transferability of the approach across different traffic scenarios.

Effective integration of variable cell length and multi-channel state representation.

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.