An Ensemble Model of Short-Term Traffic Flow Forecasting on Freeway

Abstract

This paper puts forward an ensemble model based on uncertainty simulation and multiple model combination for short-term traffic flow prediction. Firstly, the initial condition of traffic flow be simulated by using a normal distribution, which makes a better description of the real traffic flow situation. Secondly, an ensemble prediction model with different types of prediction methods has been presented in order to catch any possible change of trend in the traffic flow. In the ensemble model, In order to improve the prediction accuracy, this paper put forwards an equivalence test and dispersion adaptability test for choosing the most effective methods in the ensemble system. Finally, a case study be given to show the performance of the ensemble model. Predication result shows that this model has a good performance with freeway traffic flow, It is capable of providing more detail about the traffic volume, it provide mean value and standard deviation value of traffic volume in the next period, which is most important information for traffic managers and travelers.

Short-term Traffic Flow Prediction Method Based on Balanced Binary Tree and K-Nearest Neighbor Nonparametric Regression

“…However, the complexity of road network and the random behavior of vehicles in intersections, vehicular entrances, ramps and others are very uncertain for short-term traffic flow prediction. How to take these random factors into full consideration and make a relatively accurate prediction is a key and difficult issue [1] .…”