Traffic Prediction With a Spectral Graph Neural Network

Abstract

Traffic prediction is an essential and challenging task for traffic management and commercial purposes, such as estimating arrival time for delivery services. Machine learning methods for traffic prediction usually treat traffic conditions as time-series due to obvious temporal patterns. Recently, spatial relationships among roads in a road network have also been used to improve traffic prediction. This study proposes a novel method to predict traffic conditions such as speed using a graph convolutional neural network with a spectral adjacency matrix (GCN-Spectral). Unlike a spatial adjacency matrix representing physical connections between road segments, a spectral matrix represents the correlation between road segments in terms of traffic conditions. The GCN-Spectral model is evaluated by comparing with a multi-layer perceptron model (MLP), as a non-spatial model, and a graph convolutional neural network with a spatial adjacency matrix (GCN-Spatial). The data used in this study are GPS probe data collected from taxis in Bangkok. Empirical results show that the GCN-Spectral with a combination matrix model mostly outperforms GCN-Spatial models in the Bangkok dataset. However, MLP performs the best in most cases.