Analysis of causes of traffic accidents for taxi drivers based on MIMIC and machine learning

Through questionnaire survey, information on individual attributes, physical fatigue perception, work pressure, risky driving behavior and traffic accident experience of 2391 taxi drivers was collected. The multi-indicator multi-cause (MIMIC) model was used for path analysis to explore the inducing effect of physical fatigue perception and risky driving behavior on traffic accidents, and to verify the influence of gender, age and work pressure on physical fatigue perception and distracted driving behavior. Four machine learning algorithms, logistic regression, naive Bayes, support vector machine and random forest, were selected to predict taxi accidents. The results show that the increase of physical fatigue perception, negligent driving behavior, aggressive driving behavior and distracted driving behavior can lead to an increase in accident rate, and gender, age and work pressure will affect the frequency of physical fatigue perception and distracted driving behavior. The accident prediction model based on machine learning has excellent effect, among which the random forest has the best prediction effect. The prediction accuracy of using single feature variables "risky driving behavior", "distracted driving behavior" and "physical fatigue perception" is acceptable. When the personal attribute indicators of "work pressure", "age" and "gender" are introduced, the prediction accuracy is further improved.