Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

Vehicle-road collaboration positively promotes vehicle development and intelligent transportation. The construction of intelligent transportation cannot be separated from optimizing traffic signal timing, which is crucial for improving traffic efficiency, reducing congestion, and minimizing accident risks. Nowadays, reinforcement learning (RL) has emerged as an effective method for traffic signal timing optimization. However, many current RL-based approaches ignore the variations among different intersections, reducing the traffic efficiency. In this paper, we propose a novel method for traffic signal timing optimization, which models the problem of traffic timing optimization as an importance-oriented decision making problem. To achieve this, we first construct a directed adjacency graph based on the real road network. Then, a graph attention neural network (GAT) is utilized to estimate the importance of each intersection. Finally, we introduce the nodes importance into the reward function to find the optimal traffic light timing scheme. Extensive experiments demonstrate that our proposed method achieves higher traffic efficiency, compared to existing RL-based traffic signal timing optimization methods which ignore the intersection importance. Moreover, our method fits well with different RL algorithms, including Q-learning, DQN, Sarsa, DDPG and A3C.