  This research introduces an innovative method for adaptive traffic signal
control (ATSC) through the utilization of multi-objective deep reinforcement
learning (DRL) techniques. The proposed approach aims to enhance control
strategies at intersections while simultaneously addressing safety, efficiency,
and decarbonization objectives. Traditional ATSC methods typically prioritize
traffic efficiency and often struggle to adapt to real-time dynamic traffic
conditions. To address these challenges, the study suggests a DRL-based ATSC
algorithm that incorporates the Dueling Double Deep Q Network (D3QN) framework.
The performance of this algorithm is assessed using a simulated intersection in
Changsha, China. Notably, the proposed ATSC algorithm surpasses both
traditional ATSC and ATSC algorithms focused solely on efficiency optimization
by achieving over a 16% reduction in traffic conflicts and a 4% decrease in
carbon emissions. Regarding traffic efficiency, waiting time is reduced by 18%
compared to traditional ATSC, albeit showing a slight increase (0.64%) compared
to the DRL-based ATSC algorithm integrating the D3QN framework. This marginal
increase suggests a trade-off between efficiency and other objectives like
safety and decarbonization. Additionally, the proposed approach demonstrates
superior performance, particularly in scenarios with high traffic demand,
across all three objectives. These findings contribute to advancing traffic
control systems by offering a practical and effective solution for optimizing
signal control strategies in real-world traffic situations.
