An Hourly Meteorological Model for Fog Accident Discriminant Based on Machine Learning Technology
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Abstract:
In order to further improve the ability of refined meteorological services for traffic safety in foggy weather, this study takes Jiangsu and Anhui expressway sections where frequent fog-caused accidents happen as examples, with the application of the disaster information and weather data from 2012 to 2018 to establish a support vector machine hybrid model for hourly fog accident detection based on variable selection and feature extraction. The model uses the recursive feature elimination method to select the important variables from accident time, geographic location, and meteorological environment, and then extracts the main features of the important variables by principal component analysis. The radial basis is used as the kernel function, and the optimal parameters are determined by network search. The results show that this support vector machine hybrid model can successfully identify 81.4% of the accident samples in the training set and 83.0% of the test set, and the AUC scores are both 0.946. The ability to identify fog accidents is superior to the support vector machine algorithm and the support vector machine algorithm based only on main variable selection or principal component analysis. The analysis of three typical examples also shows that the support vector machine hybrid model has certain identification and warning significance for the occurrence of traffic accidents under periodic or persistent foggy weather.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.