Design of Deep Reinforcement Learning Approach for Traffic Signal Control at Three-way Crossroads

Abstract

Traffic light control (TSC) is an important and challenging real-world problem with the aim of reducing travel time as well as saving energy. Recent researches have numerous attempts to apply intelligent methods for TSC at four-way crossroads to solve the traffic light scheduling problem. However, there is the limitation of researches on efficient TSC at three-way crossroads. Therefore, this paper introduces a novel TSC solution for three-way crossroad environment (TW-TSC). The proposed TSC method is designed based on a deep reinforcement learning approach, namely Soft Actor-Critic (TWSAC). Firstly, we create a simulation environment for three-way crossroads which consists of numerous transportation and two parallel lanes using Unity framework. Secondly, to achieve practical movements of transportation in three-way crossroads, we carefully design agents which have a high impact to the transportation movement, notably the time to wait for traffic light, the velocity of transportation, and the number of transportation passing successfully. Finally, to achieve TW-TSC efficiency, we propose a novel reward function together with a design of TWSAC algorithm. Experimental results show that the proposed TWSAC in TW-TSC achieves higher performance than both fixed-time TSC methods and relevant RL algorithms.

A multi‐agent deep reinforcement learning approach for traffic signal coordination

“…Canh et al [25] introduce a SAC algorithm for a threeway crossroad environment. The proposed algorithm performs superiorly compared to the other algorithms, including PPO, Twin Delayed Deep Deterministic Policy Gradient (TD3), and Deep Deterministic Policy Gradient (DDPG) when evaluated in the signal optimization simulation environment at the three-way crossroad.…”