Research on Real-Time Dynamic Prediction Algorithm of Expressway Operation Situation Facing Severe Weather

Bad weather can negatively affect the normal operation of highways, and prediction of traffic dynamics under bad weather is an effective means to improve the efficiency of highway traffic and enhance safe operation under adverse weather conditions. To solve the above problems, this study establishes a DBN-AdaBoost prediction model based on highway weather data and traffic flow data. Firstly, the DBN model is used to extract the effective features of weather data, and the weights and biases of DBN are optimized continuously and iteratively. Then the BP-AdaBoost traffic pattern prediction model is constructed based on the effective features. Finally, the actual highway traffic flow data is selected for validation. The results show that the mean square error, mean absolute error, and mean square percentage error of the proposed DBN-AdaBoost prediction model are lower than those of other prediction models, and the prediction error is the smallest and the accuracy is the highest, which can complete the prediction of highway operation situation under severe weather.