A Short-Term Traffic Flow Forecasting Method Based on Support Vector Regression Optimized by Genetic Algorithm

摘要

This paper uses support vector regression to predict short-term traffic flow, and studies the feasibility of support vector regression in short-term traffic flow prediction. The short-time traffic flow has many influencing factors, which are characterized by nonlinearity, randomness and periodicity. Therefore, support vector regression algorithm has advantages in dealing with such problems. In order to improve the prediction accuracy of the support vector regression, this paper uses genetic algorithm to optimize the support vector regression and other parameters to obtain the global optimal solution. The optimal parameters are used to construct the support vector regression prediction model. This paper selects the traffic flow data of the California Department of Transportation (PEMS) database to verify the feasibility and effectiveness of the model proposed in this paper.