Analysis of Red Light Running Crashes Based on Quasi-Induced Exposure and Multiple Logistic Regression Method

Abstract

According to recent national statistics, red light running crashes represent a significant safety problem at signalized intersections. To examine the overall characteristics of red light running crashes, this study used the 1999 to 2001 Florida crash database to investigate the crash propensity related to traffic environments, driver characteristics, and vehicle types. The quasi-induced exposure concept and multiple logistic regression technique were used to perform this analysis. The results showed that traffic factors including number of lanes, crash time, weather, highway character, day of week, urban or rural location, speed limit, driver age, alcohol or drug use, physical defect, driver residence, and vehicle type were significantly associated with the risk of red light running crashes. Furthermore, it confirmed that there were significant interaction effects between the risk factors, including crash time and highway character, number of lanes and urban or rural location, weather condition and driver age, driver age and gender, alcohol or drug use and gender, and type of vehicle and gender.

What leads to severe mountainous freeway crashes in southeast of China?

“…Coefficients of Age 2_10 and Age are 0,177 and −0,143, which are significant with p-value less than 0,001. According to calculation it is not most likely to have severe mountainous freeway crashes when the driver age is about 40 and that is to say both older and younger drivers are more likely to be involved in severe crashes, which is consistent with the study of Yan et al [27]. That is because younger drivers have less driving experiences and older drivers' bodies are not as strong as those of the middle-aged.…”

Selection and assessment of the relevant data for reducing the number of red-light running

“…That means that there were a different number of RLR violations due to different vehicle categories, similar found by Huang and Chin (2009). Considering the total number of vehicles in traffic flow and number of RLR violation in each particular vehicle category, it could be concluded that the most dangerous vehicle category is passenger cars, comparing to Yan et al (2005) where truck drivers have higher red-light violation rate. One of the most important goals for decision-makers due to RLR is to reduce the total number of RLR, maybe the second or the third goal is to reduce RLR rates.…”

“…In addition, Wu et al (2012) have found that cyclists often made RLR at the beginning and at the end of red phase. Yan et al (2005) highlighted that there are correlation between accidents happened on traffic light intersection and data such as: drivers age (the youngest drivers have the highest red-light violations rate), drivers type (truck drivers have high red-light violation rate), intersection type (Y-shaped intersections have a higher percentage of violations), etc. Besides, average traffic flow speeds, traffic volume rate, the green split, the number of through and crossing lanes, signal coordination, vehicle's approaching speed, driver's age and gender, etc, are also contributing factors that have important influence on driver's decision at intersections (Liu et al 2012).…”

The efficiency of using non-culpable crash-claim involvements from insurance data as a means of estimating travel exposure for road user sub-groups

“…Most recently, af Wahlberg (2009) has commented on the high subjectivity of culpability determination, although accepting its use as an exposure surrogate where nonculpable drivers can be shown to reflect a random sample of the overall population. Still, the method has continued to be used in a number of studies comparing risk for different categories of road users (Haque, Chin & Huang, 2008;Huang & Chin, 2009;Voas, Tippetts, Romana, Fisher & Kelley-Baker, 2007;Yan, Radwan & Birriel, 2005) and some confirmation of its underlying tenets has been claimed (Chandraratna & Stamatiadis, 2009). …”