Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction

Techniques for using multi-way traffic patterns for traffic prediction is gaining importance. One of the possible techniques for representing the multi-way traffic patterns is tensors. Tensor decomposition is used to generate low-rank approximations of the original tensor that is subsequently used for traffic volume prediction. However, the existing tensor-based approaches do not consider certain important mutual relationships among the locations like temporal traffic reciprocity that can improve the prediction accuracy. In this paper, we introduce TeDCaN, a "Tensor Decomposition method with Characteristic Network" constraints that generate low rank approximations of the original tensor considering the traffic reciprocity at different pair of locations. Investigations using large traffic datasets from 2 different cities reveal that the prediction accuracy of TeDCaN considerably outperforms several state-of-art baselines for cases when complete traffic data is available as well as situations when a certain fraction of the data is missing - a likely scenario in many real datasets. We discover that TeDCaN achieves around 20% reduction in the RMSE scores as compared to the baselines. TeDCaN is applicable in many operations on such a big traffic network where the existing models would either be inapplicable or hard to perform. As one of the major yields, TeDCaN generates a "reduced dimensional network embedding" that captures the similarity of the nodes considering the traffic volume as well as the reciprocity of traffic between the nodes.