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Mathematical Problems in Engineering
Volume 2014, Article ID 184632, 8 pages
Research Article

Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

1State Key Laboratory of Automobile Simulation and Control, School of Traffic, Jilin University, Changchun 130025, China
2College of Transportation, Jilin University, Changchun 130025, China
3Jilin Province Key Laboratory of Road Traffic, College of Transportation, Jilin University, Changchun 130025, China
4College of Mechanical Science and Engineering, Jilin University, Changchun 130025, China

Received 3 June 2014; Accepted 31 July 2014; Published 18 August 2014

Academic Editor: Wuhong Wang

Copyright © 2014 Zhaosheng Yang 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.


Short-time traffic flow prediction is necessary for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). In order to improve the effect of short-term traffic flow prediction, this paper presents a short-term traffic flow multistep prediction method based on similarity search of time series. Firstly, the landmark model is used to represent time series of traffic flow data. Then the input data of prediction model are determined through searching similar time series. Finally, the echo state networks model is used for traffic flow multistep prediction. The performance of the proposed method is measured with expressway traffic flow data collected from loop detectors in Shanghai, China. The experimental results demonstrate that the proposed method can achieve better multistep prediction performance than conventional methods.