Traffic fatalities prediction based on support vector machine

Ting Li; Yunong Yang; Yonghui Wang; Chao Chen; Jinbao Yao

doi:10.5604/08669546.1225447

Authors

Ting Li Dalian Maritime University, Transportation Management College, Dalian, PR China Author
Yunong Yang Dalian Maritime University, Transportation Management College, Dalian, PR China Author
Yonghui Wang Dalian Maritime University, Transportation Management College, Dalian, PR China Author
Chao Chen Dalian University of Technology, Automotive Engineering College, Dalian, PR China Author
Jinbao Yao Beijing Jiaotong University, School of Civil Engineering and Architecture, Beijing, PR China Author

DOI:

https://doi.org/10.5604/08669546.1225447

Keywords:

traffic accident, support vector machine, SVM, particle swarm optimization (PSO), PSO, prediction model, optimal parameters

Abstract

To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress.

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