Intelligent Control Scheme for Traffic Signals at Multi-intersections based on Pedestrian Safety

Abstract

Traffic congestion has become a more and more serious problem. Meanwhile, pedestrian safety problem is another issue under the circumstance of multiple modes of vehicles that needed to be dealt with. And these two problems can be effectively solved if the traffic signal is set reasonably. This paper proposes an intelligent control scheme for traffic signals at multi-intersections based on pedestrian safety, which is designed by using multi-agent deep reinforcement learning and self-attention mechanism. And Centralized training decentralized execution is also applied in this paper which can effectively downsize the action dimension to make the model learn much faster. Though pedestrian safety and vehicle efficiency are two contradictory factors, this paper dexterously designs the reward function to let the model learn the optimal timing scheme. And the model outperforms the fixed-timed scheme and deep Q-learning scheme in the simulation traffic environment, with fewer conflicts between pedestrians and vehicles, shorter average delay time and higher average speed of vehicles at the same time.