文章基本信息

标题：An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction
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作者：Lun Zhang ; Lun Zhang ; Qiuchen Liu 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2013
卷号：96
页码：653-662
DOI：10.1016/j.sbspro.2013.08.076
语种：English
出版社：Elsevier
摘要：AbstractIn order to accurately predict the short-term traffic flow, this paper presents a k-nearest neighbor (KNN) model. Short-term urban expressway flow prediction system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the predication plan. At first, preprocess the original data and then standardized the effective data in order to avoid the magnitude difference of the sample data and improve the prediction accuracy. At last, a short-term traffic prediction based on k-NN nonparametric regression model is developed in the Matlab platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the predicting result is analyzed. Results show that the accuracy of the proposed method is over 90 percent and it also rereads that the feasibility of the methods is used in short-term traffic flow prediction.
关键词：Prediction;Short-term Traffic flow;Nonparametric Regression Model;k-NN;Urban Expressway.