Investigating the Factors Affecting the Severity of Motorcycle Accidents (Case Study: Roads of East of Guilan Province)

This research aimed to investigate the factors affecting the severity of motorcycle crashes in the east of Guilan province using a sequential probit model to provide practical solutions for reducing the severity of motorcycle riders' crashes. In this regard, crash data of motorcycle riders of the east of Guilan province was used. Considering two levels of rigidity and fatigue, as a dependent variable, a discrete selection model corresponding to this category of accidents was considered. At first, using the Chi-square test, the significance of the model was shown. Then, the effect of independent variables in the model was determined and considering the significant variables in the final model, the effective factors and their impact on the occurrence of accidents were identified. In summary, the results showed that among the seven independent variables, road surface status, lighting conditions, age and weekly levels with a significant level of 0.95, road surface status with the highest coefficient, had the most effect on the incidence of road accidents. Among other variables, then the brightness and age conditions were the most effective with the coefficients of 0.668 and 0.474, respectively. The climate variable with a reduction coefficient of -0.357 had the highest effect as a decreasing factor in the occurrence of accidents.