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Journal of Advanced Transportation
Volume 2018, Article ID 2935248, 21 pages
Research Article

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, Shandong 255049, China
2College of Transportation, Jilin University, Changchun 130022, China

Correspondence should be addressed to Qiang Shang; moc.361@785vgnaiqgnahs

Received 26 August 2017; Revised 3 December 2017; Accepted 14 December 2017; Published 8 January 2018

Academic Editor: Taha H. Rashidi

Copyright © 2018 Qiang Shang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness.