Traffic flow forecasting with Particle Swarm Optimization and Support Vector Regression

被引：0

作者：

Hu, Jianming ^{[1
,2
]}

Gao, Pan ^{[1
]}

Yao, Yunfei ^{[1
]}

Xie, Xudong ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Beijing Key Lab Cooperat Vehicle Infrastruct Syst, Beijing 100084, Peoples R China

来源：

2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2014年

关键词：

support vector regression (SVR); particle swarm algorithm (PSO); traffic flow forecasting;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose an algorithm combining support vector regression (SVR) and particle swarm optimization (PSO) for traffic flow prediction. The algorithm uses SVR to establish prediction model and uses PSO to optimize the parameters of the model. Based on the actual traffic data test, we prove that the integration of SVR and PSO is applicable and performs better than multiple linear regression and BP neural network in traffic flow prediction.

引用

页码：2267 / 2268

页数：2

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