Short-Term Traffic Speed Prediction for an Urban Corridor

详细信息    查看全文

• 作者：Baozhen Yao ; Chao Chen ; Qingda Cao ; Lu Jin ; Mingheng Zhang ; Hanbing Zhu and Bin Yu
• 刊名：Computer-Aided Civil and Infrastructure Engineering
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：32
• 期：2
• 页码：154-169
• 全文大小：1465K
• ISSN：1467-8667

文摘

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.