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论文类型：期刊论文

发表时间：2016-04-01

发表刊物：PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT

收录刊物：SCIE、EI

卷号：169

期号：2

页面范围：88-96

ISSN号：0965-092X

关键字：mathematical modelling; traffic engineering; transport planning

摘要：Effective prediction of real-time traffic flow is important for traffic management and intelligent traffic systems. This paper proposes a hybrid model, consisting of the k-nearest neighbours (k-NN) method and the Kalman filter (KF) technique, to dynamically predict real-time traffic flow. In the model, the k-NN method predicts a baseline speed of traffic flow on the basis of historical travel data of the target road link. To reflect the dynamic evolution of traffic flow in the prediction, a KF-based algorithm that uses the latest travel data, is developed to adjust the baseline travel speed. The hybrid model is tested with global positioning system data of Foshan City, China. In the numerical test, the proposed hybrid model is compared with a single k-NN model based on the same database. The results show that the hybrid model can provide more accurate prediction and thus holds potential for use in practice.