Correlation analysis of traffic accidents based on multiple model fusion

For a long time, China’s transportation safety production situation has been generally stable. However, the situation is still grim, with frequent accidents, and the number of deaths and accidents in road traffic accidents is still high. Therefore, it will be of great use to analyze and study the causes of traffic accidents. The main work of this paper is to explore the correlation between accident factors and traffic accident severity. According to the relevant knowledge of machine learning, the influence and correlation of human, vehicle, road and environmental factors on the severity of traffic accidents are analyzed by using three correlation coefficients and the maximum information coefficient of statistics. The aim is to improve the current road safety situation and thus reduce the occurrence of traffic accidents. The results show that the severity of traffic accidents has the greatest correlation with the types of casualties and whether there is police intervention, and has a great correlation with pedestrians, the number of vehicles causing traffic accidents and the level of roads.