-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
Publication: Journal of Transportation Engineering
Volume 142, Issue 6
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 -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 -nearest neighbor model is an effective approach in predicting the short-term traffic condition.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (71571026, 51578112, and 51208079), the Trans-Century Training Program Foundation for Talents from the Ministry of Education of China (NCET-12-0752), Liaoning Excellent Talents in University (LJQ2012045), and the Fundamental Research Funds for the Central Universities (3013-852019 and 3132015062).
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© 2016 American Society of Civil Engineers.
History
Received: Jul 25, 2014
Accepted: Sep 22, 2015
Published online: Feb 16, 2016
Published in print: Jun 1, 2016
Discussion open until: Jul 16, 2016
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