An Actual Traffic Prediction Algorithm Based on Particle Swarm

In order to mitigate network congestion caused by the rapid growth of traffic,a novel traffic prediction algorithm PSATP (Particle Swarm-ARIMA model based Traffic Prediction) is proposed.In this algorithm,the prediction method is presented by ARIM A model and Kalman filter at first,and the prediction accuracy is improved with particle swarm optimization.Then,a simulation with actual data was conducted to study the relationship between delay,dropping rate buffer and utilization rate.The simulation results show that,compared to other algorithm,PSATP has better performance.