A New Car-Following Model Based on the Epsilon-Support Vector Regression Method using the Parameters Tuning and Data Scaling Techniques

Traffic simulation is a powerful tool for analyzing and solving several transportation issues and traffic problems. however,all traffic micro-simulation models require a suitable car-following model to show the real situation in the best way possible. several car-following models have been proposed. an obvious disadvantage of the former models is the great number of parameters which are difficult to calibrate. moreover,any change in these parameters creates considerable disturbances. in this paper,a car-following model was proposed using the epsilon-support vector regression (ε-svr) method whose output is the acceleration of the following car. radial basis function was used as the kernel of the ε-svr method,and the model parameters were tuned using the grid search method. the best values for the parameters were obtained. furthermore,linear scaling in the interval [−1,1] was used for both the training and testing input data,and the method was proven to more accurate than the case where no scaling was used. accordingly,a car-following model with the mean squared error equal to 0.005 and the squared correlation coefficient equal to 0.98 was proposed using the function estimation method through the ε-svr method. finally,the ε-svr output was compared with the results of the well-known car-following models,including helly linear model,the ghr model,and the gipps model. it was shown that,when using the scaling and parameters tuning techniques,the proposed method was more accurate compared to all three models mentioned above. moreover,a function fitting artificial neural network was run for this purpose and the outputs showed that the result of the ε-svr method is better than that of the function fitting method by the proposed ann. implementing a microscopic validation of the proposed model showed that it can be used in the drivers’ assistance devices and other purposes of intelligent transportation systems. © 2017,iran university of science and technology.