Multi-objective deep reinforcement learning approach for adaptive traffic signal control system with concurrent optimization of safety, efficiency, and decarbonization at intersections.

This study introduces a novel approach to adaptive traffic signal control (ATSC) by leveraging multi-objective deep reinforcement learning (DRL) techniques. The proposed scheme aims to optimize control strategies at intersections while concurrently addressing the objectives of safety, efficiency, and decarbonization. Traditional ATSC schemes primarily emphasize traffic efficiency and often lack the ability to adapt to real-time dynamic traffic conditions. To overcome these limitations, the study proposes a DRL-based ATSC algorithm that integrates the Dueling Double Deep Q Network (D3QN) framework. The performance of the proposed algorithm is evaluated through a simulated intersection in Changsha, China. Specifically, the proposed ATSC algorithm outperforms both traditional ATSC and ATSC with efficiency optimization only algorithms by achieving more than a 16% reduction in traffic conflicts and a 4% reduction in carbon emissions. In terms of traffic efficiency, waiting time reduces by 18% compared to traditional ATSC, but slightly increases (0.64%) compared to DRL-based ATSC algorithm that integrates D3QN framework. This small increase indicates a trade-off between efficiency and other objectives such as safety and decarbonization. Moreover, the proposed scheme demonstrates superior performance specifically in highly traffic-demand scenarios in terms of all three objectives. The findings of this study contribute to the advancement of traffic control systems by providing a practical and effective solution for optimizing signal control strategies in real-world traffic scenarios.