Investigating the Impact of Driver and Vehicle Characteristics on the Risk of Red-Light Running Crashes

Document Type : Research Article


1 Associate professor, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

2 Ph.D. candidate, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

3 Master's degree, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran


Red-light running is one of the prevalent sights at signalized intersections that vehicles pass without caring for the light. A red-light runner ventures not only his life but also the safety of other road users. This study aims to identify the driver and vehicle characteristics affecting red-light running crash occurrence risk at signalized intersections of Iran. The study's methodology was based on the quasi-induced exposure concept and logistic regression model for ten 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 licenses increase the risk of drivers being at fault. However, the academic education level of drivers decreases at-fault risk. Finally, some countermeasures were suggested for reducing the risk of red-light running crashes.


Main Subjects

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