Traffic flow forecasting with particle swarm optimization and support vector regression

Author

Jianming Hu ; Pan Gao ; Yunfei Yao ; Xudong Xie

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing, China

fYear

2014

fDate

8-11 Oct. 2014

Firstpage

2267

Lastpage

2268

Abstract

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.

Keywords

particle swarm optimisation; regression analysis; road traffic; support vector machines; PSO; SVR; particle swarm optimization; support vector regression; traffic flow forecasting; traffic flow prediction; Algorithm design and analysis; Educational institutions; Fitting; Forecasting; Particle swarm optimization; Prediction algorithms; Support vector machines; particle swarm algorithm (PSO); support vector regression (SVR); traffic flow forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on

Conference_Location

Qingdao

Type

conf

DOI

10.1109/ITSC.2014.6958049

Filename

6958049

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=154844