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.
Research on Real-Time Dynamic Prediction Algorithm of Expressway Operation Situation Facing Severe Weather
Lect. Notes Electrical Eng.
International Conference on Green Intelligent Transportation System and Safety ; 2022 ; Qinghuangdao, China September 16, 2022 - September 18, 2022
2024-09-29
11 pages
Article/Chapter (Book)
Electronic Resource
English
Expressway operation situation prediction method and device
European Patent Office | 2021
|Predictable severe weather analysis method for expressway
European Patent Office | 2020
|Expressway severe weather congestion node prediction method and emergency response system
European Patent Office | 2025
|Real-time crash prediction for expressway weaving segments
Online Contents | 2015
|Predicting Crashes on Expressway Ramps with Real-Time Traffic and Weather Data
Transportation Research Record | 2015
|