摘要
Red light running at signalized intersections is a major safety concern in the United States. Statistics show that approximately 45 percent of crashes at intersections caused by red light running re- sult in severe injuries and fatalities, while only approximately 30 percent of all other types of intersec- tion crashes cause injuries or fatalities. Over the past decade, many US cities and counties have de- ployed red light running photo enforcement systems for signalized intersections within their jurisdictions to potentially reduce red light running related crashes. This study proposes an empirical Bayesian （ EB ） before-after analysis method that computes a weighed sum of crashes observed in the field and crashes predicted by safety performance functions （SPFs） to mitigate regression-to-mean biases for analyzing crash reduction effects of red light running enforcement. The analysis explicitly considers red light run- ning related crash types, including head-on, rear-end, angle, tuming, sideswipe in the same direction, and sideswipe in the opposite direction; and crash severity levels classified as fatal, injury, and proper- ty damage only （PDO）. A computational study is conducted to examine the effectiveness of the Chica- go program with red light running photo enforcement systems deployed for nearly two hundred signal- ized intersections. It is revealed that the use of red light running photo enforcement on the whole is pos- itive, as demonstrated by reductions in all types of fatal crashes by 4-48 percent, and injury-related an- gle crashes by 1 percent. However, it slightly raises PDO-related angle crashes and moderately increa- ses injury and PDO related rear-end crashes. The safety effectiveness of red light running photo en- forcement is sensitive to intersection location.
Red light running at signalized intersections is a major safety concern in the United States. Statistics show that approximately 45 percent of crashes at intersections caused by red light running re- sult in severe injuries and fatalities, while only approximately 30 percent of all other types of intersec- tion crashes cause injuries or fatalities. Over the past decade, many US cities and counties have de- ployed red light running photo enforcement systems for signalized intersections within their jurisdictions to potentially reduce red light running related crashes. This study proposes an empirical Bayesian （ EB ） before-after analysis method that computes a weighed sum of crashes observed in the field and crashes predicted by safety performance functions （SPFs） to mitigate regression-to-mean biases for analyzing crash reduction effects of red light running enforcement. The analysis explicitly considers red light run- ning related crash types, including head-on, rear-end, angle, tuming, sideswipe in the same direction, and sideswipe in the opposite direction; and crash severity levels classified as fatal, injury, and proper- ty damage only （PDO）. A computational study is conducted to examine the effectiveness of the Chica- go program with red light running photo enforcement systems deployed for nearly two hundred signal- ized intersections. It is revealed that the use of red light running photo enforcement on the whole is pos- itive, as demonstrated by reductions in all types of fatal crashes by 4-48 percent, and injury-related an- gle crashes by 1 percent. However, it slightly raises PDO-related angle crashes and moderately increa- ses injury and PDO related rear-end crashes. The safety effectiveness of red light running photo en- forcement is sensitive to intersection location.
基金
partially supported by US Department of Transportation/Illinois Center for Transportation (No.D7752 2008-04435-04)
