Traffic Flow Prediction Based on Improved SVR for VANET

For Vehicle Ad-Hoc Network (VANET), the accurate traffic flow information may offer useful guidance to drivers. In the past few years, applications of big data bring about opportunities for intelligent traffic control and management. This paper uses an optimization of support vector regression (SVM) as a way of traffic flow prediction. Particle swarm optimization (PSO) is applied to the parameter optimization of support vector regression (SVR), which improves the performance of the prediction system. The experimental results show that the method has good prediction effect.