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Indexed by:期刊论文

Date of Publication:2017-02-01

Journal:COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Included Journals:SCIE、EI、ESI高被引论文

Volume:32

Issue:2

Page Number:154-169

ISSN No.:1093-9687

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.