Research of Short-Term Traffic Volume Prediction Based on Kalman Filtering

This paper, based on the Kalman filter theory, established short-term traffic volume model. Kalman filter model has a good static stability, and adopts iterative method for optimal estimation of traffic. But the regular Kalman filtering traffic volume prediction established without considering the influencing factors on traffic and time-lag. Analyzing the influence factors of traffic volume based on grey entropy, and selects the main influencing factors by the size of grey entropy to establish the prediction of short-term traffic volume. Based on this, utilize the internet of things for data collection, and make a simulation experiment on a road in Shenyang. Simulation results show that model has good adaptability, high prediction accuracy on various states of traffic volume. It is a kind of effective traffic flow forecasting model.