Efficient and accurate traffic flow prediction via fast dynamic tensor completion

Abstract

Timely and accurate prediction of traffic flow plays an important role in improving living quality of the public, which greatly influences the polices and regulations to be enforced and abided by. In this paper, we focus on urban highway traffic prediction, and present a tensor completion based method, namely, DTC-F. It is conceived on the solid basis of dynamic tensor model for traffic prediction, and in this paper, fast low rank tensor completion and dynamic tensor structure are first combined to pursue high prediction performance. The proposed DTC-F method excavates the inner law of traffic flow data by taking account of multi-mode features, such as daily and weekly periodicity, spatial information, and temporal variations, etc. Empirical evaluation demonstrates the superiority of DTC-F, and indicates that the proposed method is potentially applicable in large and dynamic highway networks.