Traffic Flow Prediction Algorithm Based on Flexible Neural Tree

Artificial intelligence has been widely used in traffic flow prediction. In this paper, we investigate how the seemingly disorganized behavior of traffic flow prediction could be well represented by using flexible neural tree (FNT).The traffic flow data of the previous two months were analyzed and trained to construct a flexible neural tree model. This paper investigates the changing law of traffic volume and makes scientific and reasonable prediction for future traffic volume. By using particle swarm optimization (PSO) algorithm to optimize the parameters of FNT to build a better prediction model. The proposed method has good adaptability and robustness. It can provide a reliable model for traffic flow prediction. According to the experimental results, the prediction model can accurately describe the changing trend of traffic flow.