Short-term Traffic Demand Prediction using Graph Convolutional Neural Networks

Abstract. Short-term traffic demand prediction is one of the crucial issues in intelligent transport systems, which has attracted attention from the taxi industry and Mobility-on-Demand systems. Accurate predictions enable operators to dispatch their vehicles in advance, satisfying both drivers and passengers. This study aims to predict traffic demand over the entire city based on the Graph convolutional network (GCNN). Specially, we divide the study area into several non-overlap sub-regions. Each sub-region is treated as a node, and a traffic demand graph is constructed. Then, we build three graph convolution networks based on three different weighted adjacency matrices, which represent three graph structures. Furthermore, a data-driven graph convolutional network (DDGCNN) is developed, which can capture the correlation between pairs of sub-regions automatically. Finally, we compare our models with other prediction methods, including three GCNNs with a normal adjacency matrix, an existing data-driven graph convolutional neural network, historical average, and random forest. Results show that the weighted adjacency matrix can improve the prediction performance compared with a normal adjacency matrix. In addition, we proved that our DDGCNN outperforms other predictors in three aspects, i.e., performance over the test set, performance over the time aspect, and the performance over the spatial aspect.