Traffic flow forecasting with particle swarm optimization and support vector regression

In this paper, we propose an algorithm combining support vector regression (SVR) and particle swarm optimization (PSO) for traffic flow prediction. The algorithm uses SVR to establish prediction model and uses PSO to optimize the parameters of the model. Based on the actual traffic data test, we prove that the integration of SVR and PSO is applicable and performs better than multiple linear regression and BP neural network in traffic flow prediction.