Intersec2vec-TSC: Intersection Representation Learning for Large-Scale Traffic Signal Control

The intersection network constitutes the basic skeleton of the urban traffic environment, and informative representation of the intersection plays an important role in supporting the wide variety of applications in the intelligent transportation system. In this paper, we propose the Intersection to Vector model, named Intersec2vec, to achieve an accurate, efficient, and low-dimensional representation of each intersection in the large-scale intersection network. It introduces structural and temporal modules with attention mechanisms to specifically represent the evolution of intersection features in the spatiotemporal dimension, ensuring that intersections with stronger correlations have a higher probability of co-occurrence. Furthermore, our proposed Intersec2vec model is integrated into a traffic signal control method based on deep reinforcement learning by supporting more precise sub-area divisions, named Intersec2vec-TSC. For each sub-area, Intersec2vec-TSC adopts a hierarchical structure to design agents, where the upper agent determines the common cycle length based on the overall states of the sub-area, and lower agents jointly train a centralized evaluation network to achieve optimization of green time for each intersection. We conduct the experiment on 108 signalized intersections using real online car-hailing data, and the experimental results show that our proposed method significantly improves the stability of sub-area division and reduces the waiting time of cars during peak hours compared with other comparison methods.