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Indexed by:期刊论文

Date of Publication:2016-06-01

Journal:JOURNAL OF TRANSPORTATION ENGINEERING

Included Journals:SCIE、ESI高被引论文、Scopus

Volume:142

Issue:6

ISSN No.:0733-947X

Key Words:Short-term traffic condition; Multi-time-step prediction model; k-nearest neighbor; Spatial-temporal parameters

Abstract:One of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the k-nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed k-nearest neighbor model is an effective approach in predicting the short-term traffic condition. (C) 2016 American Society of Civil Engineers.