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Abstract

Road traffic safety has always been a topic of special concern for countries around the world. With changes in weather conditions and a significant increase in the number of highways and vehicles, traffic accidents occur frequently. To effectively predict traffic accidents and reduce the amount of accidents, a support vector machine based traffic accident prediction model under extreme weather conditions is proposed. Firstly, the relationship between different extreme weather conditions and road traffic events is analyzed. Then, genetic algorithms are used to optimize accident data, and support vector machines are fused for accident prediction. The results show that on the AVOID dataset, when the iteration reaches the 32nd time, the loss function value of the research method is the smallest, 10⁻⁵; the loss function of other algorithms is significantly larger. When the accuracy of the research method is 80% and 90%, the corresponding recall rates are 87.89% and 79.98%, respectively. Among the prediction errors of different algorithms, the max relative error of the research method is 1.214%, and the mini relative error is 0.0213%. Compared to other models, the overall error of the research method decreased by 2.021%. In the prediction application of non-serious accidents, the research method has the highest prediction accuracy of 94.53%. The above results indicate that the proposed accident prediction model can accurately predict different types of accidents under extreme weather conditions, providing certain technical support for the prevention and development of traffic accidents.

Keywords

support vector machine highway traffic accident prediction genetic algorithm extreme weather accident rate data optimization feature optimization

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A prediction model for expressway traffic accidents under extreme weather conditions based on support vector machine

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