Red Light Running Crashes Research Articles

Dynamic All-Red Extension: An Innovative Safety Countermeasure to Treat Red Light Running Crashes

The purpose of this project was to evaluate the safety effectiveness of Dynamic All-Red Extension (DARE) systems. North Carolina Department of Transportation has used DARE to combat red light running (RLR) crashes on major road approaches of selected rural, isolated traffic signals since 2011. Sixteen intersections were included in the before–after analysis. DARE systems in this study were implemented to target a specific crash type: frontal impact crashes (angle, left turn, right turn, head on) caused by vehicles traveling through the intersection and running the red light. Although there was some variability in the magnitude and significance of the results among treatment groups, there was a general reduction in the frequency of target RLR crashes. The crash modification factor (CMF) using empirical Bayes (EB) methodology was 0.93 (7% reduction in target RLR crashes) for all 16 treatment sites; for seven multilane at two-lane sites where DARE was the sole treatment, CMF was 0.65 (35% reduction in target RLR crashes), which was statistically significant at the 95% confidence level. The benefit–cost ratio of the treatment was 143:1, realized from low installation cost and the high cost of RLR crashes. The treatment appeared promising as a systemic safety treatment applied to isolated intersections with higher speed limits, especially for intersections with multilane major roads. Safety professionals should consider adding this safety countermeasure to the limited list of tools available to treat RLR crashes. DARE was inexpensive, demonstrated safety benefits, and caused minimal delays in rural applications without creating driver habituation.

Red-light running violation during car following at high-speed signalized intersections

At signalized intersections, red-light running crashes can lead to serious injuries or deaths. Red-light running is significantly influenced by intersection factors and human factors. To test driver response to circular yellow and circular red indications, an experiment was performed using a driving simulator. Data included 1,272 observations from 53 participants with a red-light running violation rate of 3.7%. A study was conducted to examine whether drivers decided to run a red light when a circular red light was displayed at signalized intersections while being closely followed. Participants' heterogeneity was accounted for using a mixed logit model.The study found that drivers ran red lights more frequently when they had a longer time to reach the intersection and a shorter tailway. No statistically significant difference was found between the following vehicle classifications and red-light violations. Additionally, results of the mixed logit model did not find a significant connection between gender of drivers and their driving behavior approaching signalized intersections. All red-light running violations occurred when the time to intersections was either 4.5 s or 5.5 s. About 60% of red-light violations exceed the posted speed limit of 45 mph, running the red light with an average speed of 47 mph. Furthermore, 59% of red-light running violations occurred between 1-1.5 s after the circular red signal commenced.

A before-after full bayes multivariate intervention model to estimate the safety effectiveness of red light cameras

Objective Intersection-related crashes account for approximately 40% of all crashes and tend to be more severe. Red-light running (RLR) crashes are most severe as almost half of these crashes result in injuries and fatalities. To reduce RLR crashes, agencies have been deploying red light cameras (RLCs). The main objective of this study was to evaluate the safety effectiveness of RLCs in the City of Miami Beach, Florida. Method The full Bayes (FB) approach was conducted based on five treatment intersections with six RLCs and 14 comparison intersections without RLCs. The analysis focused on target crash types, including rear-end, sideswipe, and angle/left-turn/right-turn crashes, and crash severity. Results The FB analysis indicated a significant sudden drop in all types of target crashes immediately after the installation of RLCs. Compared to the before-period, the after-period experienced: fewer angle/left-turn/right-turn crashes, fewer sideswipe crashes, and more rear-end crashes. The sideswipe and angle/left-turn/right-turn crashes dropped immediately after the installation of RLCs and then continued to increase, but they were still lower than the before- period. The rear-end crashes dropped immediately after the installation of RLCs and then continued to increase, but they increased at a steeper rate. Major and minor approaches AADT, higher speed limit, longer amber time, length of pedestrian crosswalk, and number of driveways within the intersection influence area increased the frequency of total target, PDO, and FI crashes. Intersections with all-red interval more than two seconds, major approach with more than two through lanes, and minor approach with more than one through lane, on the contrary, resulted in a fewer number of the total target, PDO, and FI crashes. The treatment indicator showed that treatment intersections experienced fewer FI, angle/left-turn/right-turn, and sideswipe crashes and more total, PDO, and rear-end crashes compared to the non-treatment intersections. Conclusion This study provides reliable estimates of the safety effectiveness of RLCs since it accounts for uncertainties in the data, regression-to-the-mean, and spillover effects.

Analysis of Driver and Vehicle Characteristics Involved in Red-Light Running Crashes: Isfahan, Iran

Red-light running is a prevalent sight at road intersections that vehicles pass without caring for the light. A red-light runner not only risks his life, but also the safety of other users. Elaborating features of at-fault drivers that issue crashes could result in recognizing important factors and lead decision-makers to find out actions they could start taking to prevent them. This paper aims to identify the driver and vehicle characteristics affecting red-light running crash occurrence at signalized intersections of Isfahan province in Iran. Based on the 2012–2016 Isfahan crash database, the classification and regression tree technique along with Quasi-induced exposure concept were used in this research for five independent variables and a target variable of “driver status” with two levels of at-fault and not-at-fault. According to the dataset, 9765 drivers were involved in red-light running crashes during 5 years. It was determined that vehicle type, license type, driver age, and education, respectively, are significantly associated with the red-light running crashes occurrence. The results revealed that vehicle type is the most important variable that affects red-light violations; this could be due to their various skid marks and maneuverability. Also, drivers with an age range of 18–22.5 years are usually at-fault as they have more risky behaviors and less driving experience. The tree model separated drivers into three age groups: younger than 22.5, 22.5 to 51.5, and older than 51.5. The youngest and oldest driver groups have the largest probability to be at-fault. Besides, drivers with vehicle type two-wheel and “other”, drivers who are younger than 22.5 with the automobile, and drivers whose driving license is type 2 are more likely to be at-fault in red-light running crashes. Some countermeasures such as education programs in driving skills and hazard concept could be helpful. The education level of the driver is a relatively new variable in traffic safety research used in this study, which indicated that it has a significant effect on drivers being at-fault. That is, by increasing the education level, they are less likely to be at-fault. Efficient countermeasures could be separating theoretical certificating classes according to the education level of the driver with more emphasis on violations in lower education levels, increasing the amount of traffic fines and prohibiting drivers with poor driving history for a period of time.

Prediction of Driver’s Stop-Go Decision at Signalized Intersection Based on EEG

Red light running (RLR) results in a large number of collisions and injuries of motor vehicles in the world. One of the main causes of RLR crashes is that drivers are unable to make the right decisions at intersections. In recent years, researchers have studied the influence of drivers’ behavior on their stop-go decisions and used behavior to predict driver’s decision-making. However, few studies used EEG to predict drivers’ stop-go decisions. This study proposes a method based on BPNN to predict drivers’ stop-go decisions using EEG data and provides a new research direction for the study of drivers’ stop-go behavior.

Red-Light-Running Crashes' Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Database.

Red-light running (RLR) has been identified as one of the prominent contributing factors involved in signalized intersection crashes. In order to reduce RLR crashes, primarily, a better understanding of RLR behavior and crashes is needed. In this study, three RLR crash types were extracted from the general estimates system (GES), including go-straight (GS) RLR vehicle colliding with go-straight non-RLR vehicle, go-straight RLR vehicle colliding with left-turn (LT) non-RLR vehicle, and left-turn RLR vehicle colliding with go-straight non-RLR vehicle. Then, crash features within each crash type scenario were compared, and risk analyses of GS RLR and LT RLR were also conducted. The results indicated that for the GS RLR driver, the speed limit displayed the highest effects on the percentages of GS RLR collision scenarios. For the LT RLR driver, the number of lanes displayed the highest effects on the percentages of LT RLR collision scenarios. Additionally, the drivers who were older than 50 years, distracted, and had a limited view were more likely to be involved in LT RLR accidents. Furthermore, the speeding drivers were more likely to be involved in GS RLR accidents. These findings could give a comprehensive understanding of RLR crash features and propensities for each RLR crash type.

Evaluation of countermeasures for red light running by traffic simulator–based surrogate safety measures

ABSTRACTObjective: The conflicts among motorists entering a signalized intersection with the red light indication have become a national safety issue. Because of its sensitivity, efforts have been made to investigate the possible causes and effectiveness of countermeasures using comparison sites and/or before-and-after studies. Nevertheless, these approaches are ineffective when comparison sites cannot be found, or crash data sets are not readily available or not reliable for statistical analysis. Considering the random nature of red light running (RLR) crashes, an inventive approach regardless of data availability is necessary to evaluate the effectiveness of each countermeasure face to face.Method: The aims of this research are to (1) review erstwhile literature related to red light running and traffic safety models; (2) propose a practical methodology for evaluation of RLR countermeasures with a microscopic traffic simulation model and surrogate safety assessment model (SSAM); (3) apply the proposed methodology to actual signalized intersection in Virginia, with the most prevalent scenarios—increasing the yellow signal interval duration, installing an advance warning sign, and an RLR camera; and (4) analyze the relative effectiveness by RLR frequency and the number of conflicts (rear-end and crossing).Results: All scenarios show a reduction in RLR frequency (−7.8, −45.5, and −52.4%, respectively), but only increasing the yellow signal interval duration results in a reduced total number of conflicts (−11.3%; a surrogate safety measure of possible RLR-related crashes). An RLR camera makes the greatest reduction (−60.9%) in crossing conflicts (a surrogate safety measure of possible angle crashes), whereas increasing the yellow signal interval duration results in only a 12.8% reduction of rear-end conflicts (a surrogate safety measure of possible rear-end crash).Conclusions: Although increasing the yellow signal interval duration is advantageous because this reduces the total conflicts (a possibility of total RLR-related crashes), each countermeasure shows different effects by RLR-related conflict types that can be referred to when making a decision. Given that each intersection has different RLR crash issues, evaluated countermeasures are directly applicable to enhance the cost and time effectiveness, according to the situation of the target intersection. In addition, the proposed methodology is replicable at any site that has a dearth of crash data and/or comparison sites in order to test any other countermeasures (both engineering and enforcement countermeasures) for RLR crashes.

Effects of red light running camera systems installation and then deactivation on intersection safety

IntroductionThe primary objective of this paper is to evaluate the safety impacts of red-light running camera (RLC) system installation and then deactivation at 48 intersections in Houston, Texas. The second objective is to evaluate the spillover effect at nearby non-treated intersections in Houston after the deactivation. MethodsTo accomplish study objectives, an Empirical Bayes (EB) before-after analysis was used. ResultsThe results indicate statistically significant collision reductions on all red-light running (RLR) crash types (37 percent) as well as right-angle RLR crashes (47 percent) at the treated intersections after RLC activation. By way of comparison, the RLC deactivation analysis indicated that crashes increased by 20 percent for all RLR crash types and by 23 percent in right-angle RLR crashes at the formerly treated intersections. After deactivation, all severity RLR crashes increased more than expected at nearby non-treated intersections, which indicates the possibility of an adverse spillover effect. However, fatal/injury crashes associated with rear-end decreased after deactivation at both formerly treated and non-treated intersections, although those rear-end crashes account for smaller proportions when compared to all crash types/right-angle crashes. Practical applicationsOverall, removing RLC treatments results in a negative reaction to the safety benefits that the treatment provides when it is in place and actively working and to the nearby intersections where the treatment has not been implemented. This study helps define the effects that RLCs have on safety at signalized intersections after installation and deactivation.

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

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.

Effect of Audio In-vehicle Red Light–Running Warning Message on Driving Behavior Based on a Driving Simulator Experiment

Objective: Drivers’ incorrect decisions of crossing signalized intersections at the onset of the yellow change may lead to red light running (RLR), and RLR crashes result in substantial numbers of severe injuries and property damage. In recent years, some Intelligent Transport System (ITS) concepts have focused on reducing RLR by alerting drivers that they are about to violate the signal. The objective of this study is to conduct an experimental investigation on the effectiveness of the red light violation warning system using a voice message.Methods: In this study, the prototype concept of the RLR audio warning system was modeled and tested in a high-fidelity driving simulator. According to the concept, when a vehicle is approaching an intersection at the onset of yellow and the time to the intersection is longer than the yellow interval, the in-vehicle warning system can activate the following audio message “The red light is impending. Please decelerate!” The intent of the warning design is to encourage drivers who cannot clear an intersection during the yellow change interval to stop at the intersection.Results: The experimental results showed that the warning message could decrease red light running violations by 84.3 percent. Based on the logistic regression analyses, drivers without a warning were about 86 times more likely to make go decisions at the onset of yellow and about 15 times more likely to run red lights than those with a warning. Additionally, it was found that the audio warning message could significantly reduce RLR severity because the RLR drivers’ red-entry times without a warning were longer than those with a warning.Conclusions: This driving simulator study showed a promising effect of the audio in-vehicle warning message on reducing RLR violations and crashes. It is worthwhile to further develop the proposed technology in field applications.

Red Light Cameras Unsuccessful in Reducing Fatal Crashes in Large US Cities

OBJECTIVE: In 2011, the Insurance Institute for Highway Safety (IIHS) evaluated changes in fatal red light running (RLR) and total fatal crash rates in cities that both never used and used red light traffic cameras (RLCs). The IIHS reported that RLC cities had larger decreases in both fatality rates. We replicated the IIHS study and then corrected for methodological errors that violated the IIHS study's validity. METHODS: Negative binomial models were executed for fatal RLR and total fatal crashes, first excluding one extreme outlier RLC city and then using matched case-control cities with similar fatal RLR rates in the before period. RESULTS: The camera variable was not statistically significant in these models or in the replication of the IIHS total fatal crash rate model. CONCLUSIONS: RLCs were not associated with reductions in fatal RLR or total fatal crash rates. Cities that employ RLCs may not improve the safety of their communities. Language: en

Effectiveness and Site Selection Criteria for Red Light Camera Systems

The objective of this paper is to evaluate the safety effectiveness of automated traffic enforcement systems, that is, red light cameras, installed at 254 signalized intersections in 32 jurisdictions in Texas. In addition, criteria for site selection were evaluated to provide analytical resources for camera installation. A before–after study by the empirical Bayesian methodology was performed to remove the regression-to-mean bias during the evaluation of treatments. The results indicate significant decreases in the incidences of all types of red light running (RLR) crashes and right-angle RLR crashes by 20% and 24%, respectively. A significant increase of 37% for rear-end RLR crashes was discovered. The study results suggest that a significant safety benefit for red light cameras is achieved if intersections have four or more RLR crashes per year or have two or more RLR crashes per 10,000 vehicles. Red light cameras show counterproductive results if intersections experience fewer than two RLR crashes per year or have one crash per 10,000 vehicles per year.

Red Light Running in Iowa

Red light running (RLR) is a serious safety issue that results in more than 100,000 crashes and 1,000 fatalities annually. In Iowa, RLR accounts for 21% of total crashes at signalized intersections and 35% of fatal and major-injury crashes at signalized intersections, according to data analyzed from 2001 to 2006. As transportation agencies struggle to address RLR, they turn increasingly to the use of red light camera enforcement. In Iowa, three communities currently use RLR camera enforcement. One of the communities, Davenport, installed cameras in 2004; 2 years of crash data after installation were available for analysis. A Bayesian statistical before-and-after analysis was used to evaluate the effectiveness of Davenport's RLR cameras. The analysis included control intersections. The results of the analysis indicated that the cameras were effective in reducing total crashes and RLR crashes. Results also indicated that rear-end crashes related to RLR did not increase at camera-enforced intersections.

Disaggregate Propensity Study on Red Light Running Crashes Using Quasi-Induced Exposure Method

Red light running (RLR) is a serious traffic hazard at signalized intersections. A number of studies have been undertaken to understand the characteristics and prevalence of highway users involved in RLR crashes. However, most of the studies did not consider crash exposure systematically. To overcome the difficulties in obtaining direct exposure information, this study made use of the quasi-induced exposure method to identify the significant driver-vehicle characteristics that influence RLR crash propensity. This was done by developing models of relative exposure for intersections and relative crash involvement ratio for RLR crashes. Using crash data in Singapore from 1993 to 2002, a disaggregate analysis identified a number of significant factors contributing to RLR crash propensity for different driver age and gender groups, and vehicle types under various road environments. The results show that the problem related to RLR crashes is not just related to driver or vehicle or the road environment but a combination of these factors. Arising from the results of this study, potential countermeasures in reducing the high RLR crash risk for specific highway user cohorts are discussed and recommended.

A Legal Approach to Reduce Red Light Running Crashes

Indecision at the onset of the yellow interval at signalized intersections is one of the main problems facing drivers, especially inexperienced ones. This indecision is fairly justified because the yellow interval duration varies at intersections and also because of the optional use of the all-red interval and its varied duration. This indecision has serious safety consequences for signalized intersections. This paper provides a theoretically based legal approach for reducing red light running crashes. It stresses that all-red intervals are not necessarily a cure for red light running, especially when drivers come to expect an additional safety increment and many try to misuse it. A theoretical analysis has been performed to provide information to aid drivers in deciding how far from the stop line they must stop at the onset of the yellow interval, and when and where they should proceed to cross the intersection. This information is essential in reducing red light running. Delineation of a yellow transverse line at the calculated decision line is proposed as well as instituting a traffic regulation to stop if such line is not crossed at the onset of the yellow interval. Another proposed traffic ordinance is to “yield before go” to vehicles within the intersection at the onset of green. Theoretical, legal, and logical evidence for reducing red light running accidents is provided based on the proposed traffic ordinances. However, statistical valuation is as yet impossible because such traffic ordinances have not been implemented, and there are no before and after data on which to base a statistical analysis.

Safety Impacts of Photo-Red Enforcement at Suburban Signalized Intersections

Photo-red enforcement entails cameras that photograph a vehicle entering an intersection after the traffic signal has turned red; a citation is then mailed to the vehicle's owner. Such a program was operated at 13 signals in Fairfax County, Virginia, between 2000 and 2005. This paper reports how photo-red enforcement affected crash frequency at the intersections. An empirical Bayes approach was used to detect the impact of the program on crashes while main-line traffic volume, yellow time, truck percentage, number of left-turning lanes, and speed limit were controlled. Cameras were definitely correlated with decreased red light running crashes (11% to 55%), increased rear-end crashes (32% to 85%), increased total crashes (0% to 23%), and decreased red light running injury crashes (5% to 64%). Cameras were probably associated with increased total injury crashes (5% drop to a 33% raise). These results are consistent with those of other studies with one notable exception: In the case of Fairfax County, the magnitude of the increase in rear-end crashes was larger than the magnitude of the decrease in angle crashes. That difference from other studies may have been the result of the investigators’ decision to study the crash narrative and diagram instead of relying only on collision type. Because the number of induced rear-end crashes was so much larger than the number of eliminated red light running (angle) crashes, a net safety gain would be realized if and only if severity of the latter were substantially greater than severity of the former.