TY  - JOUR
PY  - 2015//
TI  - Traffic speed data imputation method based on tensor completion
JO  - Computational intelligence and neuroscience
A1  - Ran, Bin
A1  - Tan, Huachun
A1  - Feng, Jianshuai
A1  - Liu, Yunbo
A1  - Wang, Wuhong
SP  - 364089
EP  - 364089
VL  - 2015
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1687-5265
UR  - http://dx.doi.org/10.1155/2015/364089
ID  - ref1
ER  -