Abstract: The frequency of highway congestion is increasing. In order to provide convenient travel path for drivers and slow down road traffic congestion, according to traffic statistics a highway traffic congestion prediction model based on improved genetic algorithm is designed. The fixed and mobile detection technology is used to collect macro traffic flow data such as flow, density and speed. Different identification and processing methods are adopted for abnormal parameters such as redundant data, missing data and error data to obtain effective and complete traffic flow data. The back propagation neural network and support vector machine regression network are used to improve the genetic algorithm. Two sub prediction models are established, and a hybrid prediction model is constructed by weighting the weights of the two models. According to the congestion prediction deviation of the sub prediction model, the weight coefficient of the hybrid prediction model is modified combined with the optimal weight combination strategy. The experimental results show that the design model can divide the traffic congestion level of the target expressway, and predict the congestion status according to the data of traffic flow, speed and occupancy rate, and the model has high prediction accuracy and ideal prediction effectiveness and accuracy.

Key words: traffic flow statistics; , highway traffic; , congestion prediction; , improved genetic algorithm; , support vector machine regression; , back propagation neural network