Analysis of the Effect of the Speed Factor on Highway Safety Using the Machine Learning Method

Abstract Speed is one of the most important factors that can significantly change the severity of accidents. Providing a model with predictive factors leads to designing traffic plans to promote safety. This study aims to create statistical models for accidents occurred on Firuzkuh highway, Iran. Moreover, the probability of each type of accident was determined using the logit model. Various modeling methods, such as backward, forward, and entering methods, were evaluated to find the best method. Finally, since the backward method had the best performance in terms of R2 and goodness of fit, the logit model of accidents was created. According to the model, the independent variables of the 12-24 hours, rainy weather, a speed of 81-95 and 96-110 km/h, the lack of attention ahead and the Pride brand of vehicle increased the severity of accidents, while the variables with negative coefficients of Tuesdays, the summer and spring seasons, sunny weather, a male driver, and daylight, reduced the severity of accidents.