The Application of Nonparametric Regressive Algorithm for Short-term Traffic Flow Forecast

被引：3
|
作者：
Wang, Xinying [1 ]
Juan, Zhicai [2 ]
Liu, Miao [3 ]
Sun, Yuan [4 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130023, Peoples R China
[2] Shanghai Jiao Tong Univ, Inst Transportat Studies, Shanghai, Peoples R China
[3] Daqing Petroleum Inst, Dept Comp Sci, Qingdao, Peoples R China
[4] Jilin Univ, Comp Teaching Res Ctr, Changchun, Peoples R China
来源：
PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND COMPUTER SCIENCE, VOL III | 2009年
关键词：
short-term traffic flow predictions; KNN; nonparametric regressive algorithm;
D O I：
10.1109/ETCS.2009.707
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Short-term traffic flow forecast is an important topic in the research field of intelligent transportation systems. The article analyses the preliminary results in the short-term traffic flow forecast, takes full advantage of the characteristics of K-neatest neighbor (KNN) classifiers, and builds a model based on nonparametric regressive algorithm. The historical and metrical data is classified by KNN, and the state vector is constructed by utilizing the output of KNN classifier. Traffic flow forecasting for the next period is entirely based on the state vectors. The experimental results show that the model was verified more accurate.
引用
收藏
页码：767 / +
页数：2
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