A Novel Abnormal Traffic Incident Detection Method Based on Improved Support Vector Machine

Yan Sun; Zhixiang Hou

doi:10.6180/jase.201803_21(1).0006

A Novel Abnormal Traffic Incident Detection Method Based on Improved Support Vector Machine

Yan Sun This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Zhixiang Hou²

¹Xi’an Fanyi University, Xi’an, Shaanxi, 710105, P.R. China
²College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, 410114, P.R. China

Received: December 19, 2016
Accepted: December 14, 2017
Publication Date: March 1, 2018

Download Citation: ||https://doi.org/10.6180/jase.201803_21(1).0006

ABSTRACT

In recent years,the traffic incident automatic detection technology has become a central issue in intelligent transportation field. In order to detect traffic incidents accurately, highway traffic incident detection model is setup based on the characteristics of highway traffic flow and the basic principles of traffic incident detection. This model includes data preprocessing module, construction of SVM and decision output module. Improved particle swarm optimization is adopted to optimize parameters of SVM model. By adjusting the penalty parameter and the size of the radial basis kernel parameter, we can make SVM has better classification performance. The simulation data is extracted from I-880 traffic dataset. By comparing and analyzing the simulation results, the SVM model based on improved particle swarm optimization achieves better comprehensive detection performance with less modeling time and higher detection accuracy, which provides important reference for highway abnormal traffic incident detection.

Keywords: Traffic Incident Detection, Particle Swarm Optimization, Support Vector Machine

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