Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)

• Published in: Transportation research. Part C, Emerging technologies Vol. 114; pp. 189 - 204
• Main Authors: Yu, Byeonghyeop, Lee, Yongjin, Sohn, Keemin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2020
• Subjects: Generative adversarial framework; Graph convolutional neural network (GCN); Spatio-temporal dependencies; Traffic management; Traffic state forecasting; Graph convolutional neural network (GCN); Traffic management; Spatio-temporal dependencies; Traffic state forecasting; Generative adversarial framework
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2020.02.013

•The present study proposed a novel graph convolution model to forecast future traffic speeds.•The proposed model differentiated the intensity of connecting to neighbor roads unlike existing GCNs.•The present study was focused on devising a GCN model that mimic true propagation patterns of traffic.•The proposed model shows promise for application to a real-time traffic forecasting system. The traffic state in an urban transportation network is determined via spatio-temporal traffic propagation. In early traffic forecasting studies, time-series models were adopted to accommodate autocorrelations between traffic states. The incorporation of spatial correlations into the forecasting of traffic states, however, involved a computational burden. Deep learning technologies were recently introduced to traffic forecasting in order to accommodate the spatio-temporal dependencies among traffic states. In the present study, we devised a novel graph-based neural network that expanded the existing graph convolutional neural network (GCN). The proposed model allowed us to differentiate the intensity of connecting to neighbor roads, unlike existing GCNs that give equal weight to each neighbor road. A plausible model architecture that mimicked real traffic propagation was established based on the graph convolution. The domain knowledge was efficiently incorporated into a neural network architecture. The present study also employed a generative adversarial framework to ensure that a forecasted traffic state could be as realistic as possible considering the joint probabilistic density of real traffic states. The forecasting performance of the proposed model surpassed that of the original GCN model, and the estimated adjacency matrices revealed the hidden nature of real traffic propagation.