Traffic Flow Prediction Under Emergency Based on SVR Classification Regression

To improve the prediction accuracy of urban traffic flow under the emergency traffic accident, according to the characteristics of traffic accidents, put forward a kind of classification based on support vector regression (SVR) forecasting model, considering the characteristics of accident impact the change trend of traffic flow, according to the severity of the accident emergency traffic accident can be divided into minor impact, major impact and serious impact, and the relative delay time of actual accidents is used for cluster analysis to verify the validity of the classification method. Then, support vector machine is used to build different prediction models for the three accident categories, and the traffic flow under the corresponding categories is predicted. The validity of the proposed method is verified by analyzing the traffic data on the viaduct in Yan’an City. The results show that compared with the traditional SVR model, the prediction results based on the SVR classification regression method are more consistent with the actual data, and the traffic flow prediction errors under three types of accidents are 7.5959%, 9.9542% and 8.5704%, respectively. Therefore, the prediction accuracy of SVR classification regression model is better than that of traditional SVR model, and it is a more suitable and reliable method for traffic flow prediction in the case of urban traffic accidents.