Short-term forecasting method for dynamic traffic flow based on stochastic forest algorithm

Abstract

In order to overcome the problems of low accuracy and long time-consuming in traditional short-term forecasting methods for dynamic traffic flow, a short-term forecasting method for dynamic traffic flow based on stochastic forest algorithm is proposed in this paper. This method chooses short-term forecasting equipment for dynamic traffic flow, eliminates invalid data from the collected data, and normalizes the available data to complete data preprocessing before traffic flow forecasting. A combined forecasting model is established to optimize the output of the pretreatment results and complete the dynamic traffic flow rate forecasting. On this basis, the stochastic forest algorithm is introduced to train the sampling set of flow rate decision tree and generate short-term flow decision tree to realize short-term forecasting of dynamic traffic flow. The experimental results show that the forecasting time of the proposed method is short, always less than 0.5 s, and the forecasting accuracy is high, with more than 97%, so it is feasible.