At head of title: National Cooperative Highway Research Program.

The objectives of this research project are to: (1) quantify the safety impact of red-light-running at intersections in Texas, and (2) provide guidelines for identifying truly problem intersections and whether enforcement or engineering countermeasures are appropriate. This report documents the findings from the first year of a two-year project. During the first year, data were collected to quantify the character and extent of red-light violations and related crashes in Texas. Data were also collected to quantify the effectiveness of intersection traffic control enforcement. The findings from a review of the literature on red-light-running and an analysis of red-light-running crash data are included in this report. A procedure for identifying and quantifying red-light-running-related crashes is described.

Red light running is one of the main contributing factors of crashes in urban areas in Florida and the United States. Nationwide, according to preliminary estimates by the Federal Highway Administration (FHWA) 2001, there were nearly 218,000 red-light running crashes at intersections. These crashes resulted in as many as 181,000 injuries and 880 fatalities, and an economic loss estimated at $14 billion per year nationwide, According to the Community Traffic Safety Team Florida Coalition (A statewide traffic safety group) there were 9,348 crashes involving red-light running in Florida and 127 fatalities in 1999. This research study focused on studying the red light running crashes and violations in the State of Florida. There were three primary objectives for this research. The first primary objective was to analyze the red light running crashes in Florida from 2002 to 2004. The data for this part was collected from the Crash Analysis Reporting System of the Florida Department of Transportation. These crashes are reported as "disregarded traffic signal" as far as the first contributing cause. The analysis focused on the influences of different factors on red light running crashes including the driver (age group, gender, and DUI history) and the environment (time of day, day of week, type of road, and weather). However, not all red light crashes are reported as "disregarded traffic signal". Therefore, representing red light running crashes only through "disregard traffic signal" noted reports would underestimate the extent of red light running effects at a given intersection. Therefore, the second objective was to review the long form crash reports to determine the actual number of crashes related to red light running. The analysis for a random sample of the crashes on the sate roads of Florida on the year 2004 showed that the percentage of crashes related to red light running reported on the database was found to be (3.13%), and the percentage of crashes related to red light running reported in the original crash repot filled by the police officer are much higher than reported(5.63%), which shows the importance of standardizing the format and coding process for the long form crashes conducted by the police officers to help accurately identify the real cause of the crash at the studied location. The third objective was to analyze the violations data given for five intersections and find if there is a correlation between the average rate of violations per hour and the frequency of red light running crashes. The analysis showed that utilizing the limited number of intersections used in the study, it appears that there is no correlation between the average violations per hour and the red light running crashes at the studied locations.

This study examines general characteristics of motor vehicle traffic crashes at intersections by analyzing the association of the critical reason with several crash factors such as driver's sex and age, traffic control device, critical pre-crash event, and atmospheric condition. The National Motor Vehicle Crash Causation Survey data collected at crash scenes between 2005 and 2007 is used in statistical analyses such as descriptive analysis, generalized logit model, and configural frequency analysis.