Investigating the impact of driver and vehicle characteristics on the risk of red-light running crashes

Red-light running is one of the prevalent sights at signalized intersections that vehicles pass without caring for the light. A red- light runner not only ventures his life, but also the safety of other road users. Elaborating features of at-fault drivers and vehicles that issue the risk of crashes could result in recognizing important factors and lead decision-makers to find out actions they could prevent them. This study aims to identify the driver and vehicle characteristics affecting red-light running crash occurrence risk at signalized intersections of Iran. The methodology of the study was based on quasi-induced exposure concept and logistic regression model for 10 independent variables and one binary target variable of "driver status". The statistical population included 12445 red-light running crashes from 2012 to 2016. The results demonstrated that vehicle type, residence, license type, and education level affect drivers' fault status in these crashes. based on the logistic regression model, truck and emergency vehicles, foreign drivers, and type 2 driving license increase the risk of drivers' being at-fault. However, academic education level of drivers decreases at-fault risk. Finally, some countermeasures were suggested for decreasing the risk of red-light running crashes.