Tensor decomposition of transportation temporal and spatial big data: A brief review

Recent development in sensing and communication technologies has made the collection of a large amount of traffic data easy and transportation engineering has entered the big data era. The massive traffic data provides some good opportunities for Intelligent Transportation System (ITS), while some great challenges because of its characteristics of large value, variety, velocity, veracity, and volume. In recent few years, tensor decomposition has played an important role in traffic data analytic solutions and attached great interest from both academic and industrial areas. In this paper, the preliminary background and the implementation of tensor decomposition are presented. Then, some recent studies of tensor decomposition for traffic data imputation, traffic state prediction, and analysis of travel pattern are reviewed. Furthermore, advantages and disadvantages are discussed. Finally, remaining challenges of the application of tensor decomposition in transportation engineering are pointed out.