Traffic Flow Forecasting Method based on Gradient Boosting Decision Tree

被引：0

作者：

Xia Ying ^{[1
]}

Chen Jungang ^{[1
]}

机构：

[1] Chongqing Univ Posts & Telecommun, Res Ctr Spatial Informat Syst, Chongqing, Peoples R China

来源：

PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON FRONTIERS OF MANUFACTURING SCIENCE AND MEASURING TECHNOLOGY (FMSMT 2017) | 2017年 / 130卷

关键词：

Traffic Flow Forecasting; Sliding Time Window; Temporal Correlation Search; Feature Extension; Gradient Boosting Decision Tree;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Accurate traffic flow forecasting is very important for intelligent transportation system. This paper proposes a traffic flow forecasting method based on gradient boosting decision tree. In the preprocess phase, sliding time window and feature extension of traffic data are designed on the basis of time series analysis and temporal correlation search is introduced to find prediction training set. In the prediction phase, gradient boosting decision tree is used to predict the traffic flow. Experimental results show that the traffic flow forecasting method based on gradient boosting decision tree is effective and can obtain higher prediction accuracy.

引用

页码：413 / 416

页数：4

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