Temporal Graph Convolutional Networks for Traffic Speed Prediction Considering External Factors

Abstract

Traffic speed prediction is an important part of intelligent transportation systems (ITS). If road traffic speed is predicted accurately, we can provide not only evidence for urban traffic managers, but also support for other road services such as path planning. Traditional prediction models usually ignore the spatio-temporal dependencies of the traffic dynamics and influences of external factors. This paper proposes a Temporal Graph Convolutional Networks (GTCN) which is composed of spatio-temporal component and external component to solving the traffic speed prediction problem. The spatio-temporal component integrates k-order spectral graph convolution and dilated casual convolution to capture the spatio-temporal dependencies. The external component takes social factors such as day of the week into account. To further improve the prediction accuracy, we consider the road structure features and point of interest (POI) during the construction of the sensor station graph. We evaluate the prediction model on two datasets from the Caltrans Performance Measurement System (CalTrans PeMS). Experiments show that the proposed GTCN model obtains high accuracy and outperforms state-of-the-art baselines.