Title: Short-term traffic flow prediction based on optimised support vector regression

Authors: Yang Xu; Da-wei Hu; Bing Su

Addresses: School of Automobile, Chang'an University, Xi'an 710064, China; School of Economics and Management, Xi'an Technological University, Xi'an 710021, China ' School of Automobile, Chang'an University, Xi'an 710064, China ' School of Economics and Management, Xi'an Technological University, Xi'an 710021, China

Abstract: In order to provide accurate and reliable prediction of short-term traffic flow to realise intelligent transportation control, support vector machine (SVM) regression method is established to predict short-term traffic flow. Then, parameter selection optimisation model for SVM is studied. Support vector penalty coefficient and the parameters of the kernel function play an important role in learning precision and generalisation ability of regression model. So, a kind of improved artificial fish swarm algorithm is used to optimise the SVM regression to select the optimal parameters. The experiment results show that the proposed scheme can effectively reduce mean absolute percentage error and mean square error in the real traffic flow forecasting. The proposed scheme can improve the prediction precision of the short-term traffic flow.

Keywords: short-term traffic flow prediction; support vector machine regression; artificial fish; accuracy.

DOI: 10.1504/IJADS.2017.087176

International Journal of Applied Decision Sciences, 2017 Vol.10 No.4, pp.305 - 314

Received: 14 Dec 2016
Accepted: 05 Mar 2017
Published online: 06 Oct 2017 *

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