中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊

重庆交通大学学报(自然科学版) ›› 2012, Vol. 31 ›› Issue (4): 828-831.DOI: 10.3969/j.issn.1674-0696.2012.04.23

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K 近邻短期交通流预测

方琴1,李永前2   

  1. 1. 贵州大学土木建筑工程学院,贵州贵阳550003; 2. 贵阳市公安交通管理局,贵州贵阳550081
  • 收稿日期:2011-10-01 修回日期:2012-03-21 出版日期:2012-08-15 发布日期:2015-03-12
  • 作者简介:方琴( 1967—) ,女,江苏丰县人,副教授,工学硕士,主要从事城市交通规划与管理方面的研究工作。E-mail:fang8513055@yahoo.com.cn.
  • 基金资助:
    贵阳市科技局项目( 2009001)

On K-nearest Neighbor Short-Term Traffic Flow Prediction

Fang Qin1,Li Yong-qian2   

  1. 1.Civil Engineering Department,Guizhou Universitym,Guiyang 550003,Guizhou,China; 2.Guiyang Traffic Administration Bureau,Guiyang 550081,Guizhou,China
  • Received:2011-10-01 Revised:2012-03-21 Online:2012-08-15 Published:2015-03-12

摘要: 从分析短时交通流特性入手,利用非参数回归中K近邻的方法,对道路交通流量进行短期预测;采用贵阳市道 路交通流量的实际数据进行验证。结果表明:K近邻非参数回归预测模型能较为准确的进行道路短期交通流预测, 该方法可用于短期交通流预测。

关键词: 短期交通流预测, 非参数回归, K近邻

Abstract: Traffic flow prediction was analyzed from characteristics; the method of K-nearest neighbor was applied in the non-parametric regression for short-term forecast of road traffic flow; the road traffic-related data of Guiyang City was employed to validate the model. It proves that the K-nearest neighbor nonparametric regression model can predict the shortterm traffic flow more accurately and it also reveals that the feasibility of the methods is used in short-term traffic flow prediction.

Key words: short-term traffic flow prediction, non-parametric regression, K-nearest neighborhood

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