title: Traffic speed data imputation method based on tensor completion.
abstract: Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS). In this paper, we handle this issue by a novel tensor-based imputation approach. Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data. This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume. The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches.
authors: Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
authors: Bin
authors: B
authors: Ran
authors: Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China.
authors: Huachun
authors: 0000-0001-5968-103X
authors: H
authors: Tan
authors: Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China.
authors: Jianshuai
authors: J
authors: Feng
authors: College of Computer and Information Engineering, Beijing Technology and Business University, Beijng 100048, China.
authors: Ying
authors: Y
authors: Liu
authors: Department of Transportation Engineering, Beijing Institute of Technology, Beijing 100081, China.
authors: Wuhong
authors: W
authors: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D010363:Pattern Recognition, Automated / Q000379:methods; D018401:Sample Size; D012815:Signal Processing, Computer-Assisted