Intersection crashes are a safety concern for many transportation agencies, and those 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 is validated using on-site video collection at several locations, and the algorithm was refined to reduce the incidence of false RLR indications. One case study demonstrates that an increase in side street green split from 20% to 24% of cycle length is 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. Additionally, law enforcement and transportation agencies can utilize this technique to more efficiently manage and deploy safety resources, especially in cases where detailed crash histories are unknown or too infrequent.
Thermal image sensors for stop bar presence detection have recently been introduced to the traffic industry as an alternative to cameras sensitive only to the visual spectrum. This new detection technology, from two manufacturers, was evaluated side by side with a video detection system. Inductive loops were used for a comparison to identify discrepancies that warranted the manual establishment of ground truth of the video images. The video camera and two thermal sensors operated simultaneously over a 24-h period, and discrepancies in the loop versus thermal or video detection calls were validated from recorded video. No missed call events longer than 10 s were observed, and only a modest number of false calls were made by the test systems. The bias in activation and termination times was also evaluated during day and night operation for each system. The study found that the median time difference in activating a detection zone was about 1 s when the day and night operations of the detection system were compared with a video camera. This finding is consistent with past studies that reported nighttime detection challenges related to headlight projections. However, the thermal cameras had virtually no change in median activation times when day and nighttime operations were compared. This encouraging finding suggests that integrating cameras sensitive to the infrared spectrum holds considerable promise for improving the quality of nighttime video detection.
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