Dual Graph for Traffic Forecasting

Traffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only focus on nodes (intersections) or edges (road segments) traffic forecasting alone. As a result, they could hardly provide a complete description of future traffic on road networks. Actually, nodes and edges traffic are interrelated. Both of them are important for traffic safety and efficiency, and neither one is negligible. In this paper, we exploit nodes and edges information together and make traffic forecasting on nodes and edges simultaneously. We propose a novel dual graph framework, called DualGraph, to model the propagation behavior of traffic on road networks. Inside our framework, we develop a DualMap block to simulate the recursive interactions between nodes and edges. The interaction process is realized by a message passing mechanism of nearby information flow. We employ the Simulation of Urban MObility (SUMO) software to generate real-world traffic data to illustrate the effectiveness of our method. We also empirically evaluate our model on public traffic datasets. The results show that even for node or edge traffic forecasting alone, our model still outperforms compared ones, especially for long term (one hour) prediction.