Periodic Shift and Event-aware Spatio-Temporal Graph Convolutional Network for Traffic Congestion Prediction

Traffic congestion has a negative impact on our daily life. Predicting the trend of traffic congestion can provide a valuable guideline to address such problems. Most existing approaches focus on the tasks of predicting traffic volume or traffic speed, which do not effectively address the challenges of traffic congestion prediction. First, traffic congestion exhibits daily and weekly temporal patterns, but these patterns are not strictly the same, which indicates complicated long-term periodicity. Second, traffic congestion sparsely distributes over different periods of time, which leads to complex short-term and mid-term temporal dependencies. Third, since traffic congestion will propagate to adjacent road segments over time, it exhibits complex spatio-temporal correlations. To address them, we propose a periodic shift and event-aware spatio-temporal graph convolutional network for traffic congestion prediction. Specifically, we propose to capture the differences and similarities of long-term periodic temporal patterns to handle the complicated long-term periodicity. To effectively capture short-term and mid-term temporal dependencies, we regard a continuous time sequence of the congested condition as a traffic congestion event, and then adopt the widely-used long short-term memory model to learn the sequential dependencies of traffic congestion events. Finally, we integrate the graph convolutional network into the modeling of temporal dependencies to capture the complex spatio-temporal correlations. Extensive experiments demonstrate the superiority of our model. In addition, we deploy our model in production at Amap, and it achieves great performance improvement in terms of the F1-scorecompared to the production baseline. This confirms that our model is a practical solution for real-world congestion prediction services.