Traffic Congestion Prediction: A Spatial-Temporal Context Embedding and Metric Learning Approach

Abstract

In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.