Highway Traffic Accident Prediction Based on SVR Trained by Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 5886-5889 ◽  
Author(s):  
Zhen Qi Yang

The multi researches and experiments show that the future highway traffic accident situation is shown by the highway traffic accident prediction. In the paper, support vector regression trained by genetic algorithm is presented in highway traffic accident prediction. In the method, genetic algorithm is used to train the parameters of support vector regression. Firstly, the regression function of support vector regression algorithm is introduced, and the parameters of support vector regression are optimized by genetic algorithm. The computation results between G-SVR and SVR can indicate that the prediction ability for highway traffic accidents of G-SVR is better than that of SVR absolutely.

2012 ◽  
Vol 433-440 ◽  
pp. 2103-2108 ◽  
Author(s):  
Shao Feng Wei ◽  
Ying Zhang

Least squares support vector regression trained by genetic algorithm is proposed to predict the reliability of LAN/WLAN integration network,and genetic algorithm is adopted to optimize the parameters of least squares support vector regression in the paper. The influencing factors of network reliability usually include the number of node,the number of link,time delay and reliability of link. The comparison results of the prediction values between LSSVR and RBFNN and the comparison results of the prediction error between LSSVR and RBFNN are given in the paper.It is indicated that LSSVR has more excellent prediction ability than RBFNN.


2020 ◽  
Vol 10 (3) ◽  
pp. 613-630 ◽  
Author(s):  
Menad Nait Amar ◽  
Noureddine Zeraibi ◽  
Ashkan Jahanbani Ghahfarokhi

2012 ◽  
Vol 468-471 ◽  
pp. 579-582
Author(s):  
Wei Sun ◽  
Le Shen

Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the Support Vector Regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.


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