Short‐term traffic speed prediction for an urban corridor

Baozhen Yao, Chao Chen, Qingda Cao, Lu Jin, Mengjie Zhang, Hanbing Zhu, Bin Yu (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.
Original languageEnglish
Pages (from-to)154-169
Number of pages16
JournalComputer-Aided Civil and Infrastructure Engineering
Volume32
Issue number2
DOIs
Publication statusPublished - 21 Jul 2016
Externally publishedYes

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Support vector machines
Global positioning system
Neural networks

Cite this

Yao, Baozhen ; Chen, Chao ; Cao, Qingda ; Jin, Lu ; Zhang, Mengjie ; Zhu, Hanbing ; Yu, Bin . / Short‐term traffic speed prediction for an urban corridor. In: Computer-Aided Civil and Infrastructure Engineering. 2016 ; Vol. 32, No. 2. pp. 154-169.
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Short‐term traffic speed prediction for an urban corridor. / Yao, Baozhen; Chen, Chao; Cao, Qingda; Jin, Lu; Zhang, Mengjie; Zhu, Hanbing; Yu, Bin (Corresponding author).

In: Computer-Aided Civil and Infrastructure Engineering, Vol. 32, No. 2, 21.07.2016, p. 154-169.

Research output: Contribution to journalArticleAcademicpeer-review

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