Use of High-Resolution Signal Controller Data to Identify Red Light Running

Abstract

Intersection crashes are a safety concern for many transportation agencies, and crashes related to red light running (RLR) vehicles are of particular interest. Many camera-based RLR detection systems are controversial with the public, and there is relatively little published literature on the methodologies. This study proposes a methodology that combines high-resolution signal controller data with conventional stop bar loop detection to identify vehicles that enter the intersection after the start of red, when many of the most serious RLR crashes occur. The methodology was validated with on-site video collection at several locations, and the algorithm was refined to reduce the incidence of false RLR indications. One case study demonstrated that an increase on the side street of the green split from 20% to 24% of the cycle length was associated with a 34% reduction in daily RLR counts and a reduction in the likelihood of RLR by a factor of 1.7—a substantial safety improvement for minimal cost. Law enforcement and transportation agencies can use this technique to more efficiently manage and deploy safety resources, especially in cases for which detailed crash histories are unknown or infrequent.