Release Time:2019-03-12  Hits:

Indexed by: Journal Article

Date of Publication: 2017-02-01

Journal: COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Included Journals: ESI高被引论文、EI、SCIE

Volume: 32

Issue: 2

Page Number: 154-169

ISSN: 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.