FACTORS AFFECTING ANALYSIS of THE SEVERITY of AUTOMOBILE AND ELECTRIC BICYCLE ACCIDENTS USING RANDOM FOREST MODEL

Abstract

The frequency of automobile and electric bicycle accidents has shown a rising trend. The occurrence of such accidents has caused great harm to the safety of electric bicycle drivers and passengers. In order to analyze the affecting factors of the of the severity of automobile and electric bicycle accidents, the data of automobile and electric bicycle accidents in a city from 2010 to 2019 were collected, and the severity of automobile and electric bicycle accidents was predicted by random forest model, and the importance of relevant factors was ranked. The results show that visibility, drivers' age and driving age, road cross section location, accident time and other factors have significant effects on the severity of electric bicycle drivers. The drivers' age of electric bicycle and automobile, the time of accident, the responsibility of accident and the severity of electric bicycle driver have significant effects on the severity of automobile bicycle driver. The research is of positive significance to reduce the severity of urban automobile and electric bicycle traffic accidents.