Prediction model of passenger transport volume in metropolitan region based on support vector machine

The sample data set of metropolitan region's passenger transport volume was divided into training set, testing set and examining set, the loss function's parameters and penalty factor were determined according to the final prediction error criterion, the ε-insensitive loss function and the Gaussian kernel function were chosen to decrease prediction complexity, and a prediction model of metropolitan region's passenger transport volume was proposed based on support vector machine. The passenger transport volume of Beijing-Tianjin-Hebei Metropolitan Region was predicted by gradual changing the parameter values of loss function, penalty factor and Gaussian kernel function. Prediction result shows that the average relative error is 0.15%, and the total changing trends of prediction value and testing value are same, so the model is credible.