Bayesian optimization of support vector machine for regression prediction of short-term traffic flow

Short-term traffic flow prediction plays a crucial component in transportation management and deployment. In this paper, a novel regression framework for short-term traffic flow prediction with automatic parameter tuning is proposed, with the SVR being the primary regression model for traffic flow prediction and the Bayesian Optimization being the major method for parameters selection. First, the preprocessing of raw traffic flow is carried out by seasonal difference to eliminate the non-stationary of the data. Then, Support Vector Regression model is trained by the pre-processed data. In order to optimize the model parameters, the generalization performance of SVR is modeled as a sample from a Gaussian process (GP). Bayesian optimization determines the parameters configuration of the regression model by optimizing the acquisition function over the GP. Finally, the optimal short-term traffic flow regression model is constructed through repeated GP update and iteratively multiple training of the model. Experiment results show that the accuracy of proposed method is superior to methods of classical SARIMA, MLP-NN, ERT and Adaboost.