Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model

Abstract

Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator to analyze the road occupancy status and formulate dynamic and flexible traffic control in advance to improve the road capacity. It can also provide more precise navigation guidance for the road users in future. However, it is hard to predict spatiotemporal traffic flow data in large scale promptly with high accuracy caused by complex interrelation and nonlinear dynamic nature. With development of deep learning and other technologies, many prediction networks could predict traffic flow with accumulated historical data in time series. In consideration of the regional characteristics of traffic flow, the emerging Graph Convolutional Network (GCN) model is systematically introduced with representative applications. Those successful applications provide a possible way to contribute fast and proper traffic control strategies that could relieve traffic pressure, reduce potential conflict, fasten emergency response, etc.