Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series. (18th August 2014)

• Record Type: Journal Article
• Authors: Yang, Zhaosheng; Bing, Qichun; Lin, Ciyun; Yang, Nan; Mei, Duo
• Other Names: Wang Wuhong Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-08-18
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/184632
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.