Short-Term Traffic Prediction Based on Dynamic Tensor Completion

• Published in: IEEE transactions on intelligent transportation systems Vol. 17; no. 8; pp. 2123 - 2133
• Main Authors: Huachun Tan, Yuankai Wu, Bin Shen, Jin, Peter J., Bin Ran
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Control systems; Data models; dynamic tensor completion; Dynamics; Effectiveness; Heuristic algorithms; Intelligent transportation systems; Mathematical analysis; Methods; missing data; multi-mode information; Network management systems; Predictive models; Short-term traffic flow prediction; Tensile stress; Tensors; Time series analysis; Traffic congestion; Traffic engineering; Traffic flow
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2015.2513411

Short-term traffic prediction plays a critical role in many important applications of intelligent transportation systems such as traffic congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traffic data. In this paper, we present a novel short-term traffic flow prediction approach based on dynamic tensor completion (DTC), in which the traffic data are represented as a dynamic tensor pattern, which is able capture more information of traffic flow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traffic flow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the efficacy of the proposed approach is validated on the experiments of traffic flow prediction, particularly when dealing with incomplete traffic data.