Red Light Running Violations Research Articles

Severity analysis of red-light–running behavior at signalized intersections

Red-light running (RLR) is one of the riskiest behaviors at signalized intersections. This study utilizes intersection- and corridor-level characteristics to identify variables that impact the frequency and severity of RLR violations. The severity of RLR is defined based on the time when the violations happened. That is, less severe violations are the ones that occur within 3 seconds of the signal light turning red (RLR3), while more severe violations happen between 3 and 10 seconds of the signal light turning red (RLR10). Results of the zero-inflated negative binomial model showed that an increase in intersection delay and split failure increases the frequency of both types of RLR severities. An increase in the yellow interval, cycle length, and number of lanes reduces the frequency of RLR3 but increases the frequency of RLR10. Furthermore, based on the factor importance analysis conducted through the random forest, it was shown that split failure is one of the most predictive variables for RLR3 and RLR10. The findings of this research could help transportation agencies in different geographic jurisdictions utilize the calibrated models to understand the impact of varying intersection- and corridor-based characteristics on the frequency and severity of RLR.

Factors Affecting Red-Light Running of Pedestrians at Signalized Intersections

Hundreds of pedestrians have died and many have been injured in the past decades as a result of Red Light Running (RLR) infractions. According to the United States Department of Transportation, 846 pedestrians have died and 143,000 have been injured in 2019 due to RLR violations. The majority of previous studies have focused on pedestrian behavior at an intersection, whereas only a few have looked into pedestrian RLR violations. The main objectives of this research are to find the pedestrians’ RLR rate in Sri Lanka and to find ways to reduce the RLR rate of pedestrians at the signalized crossing. Video observation surveys were conducted to collect data at three signalized intersections within Kandy city limits during weekdays for two hours per site. Pedestrian demographic variables such as gender and age; crossing characteristics such as crossing type, direction, crossing speed etc; and site characteristics such as crossing length, pedestrian green time, etc. were recorded. Chi-square and binary logistic regression tests were done. Results showed that out of 178 females, 130 had compliance with signal phases and out of 386 males, only 215 had compliance with RLR of a pedestrian. Furthermore, other independent variables such as age, crossing type, direction etc. were also associated with compliance RLR of pedestrians under Chi-square results. Based on the results of binary logistic regression, the variables such as gender, crossing type, number of traffic lanes, and pedestrian speed are significant when decreasing the log of probability -0.658, -3.040, -1.022 and -2.556 of compliance for RLR respectively. Variables that crosswalk utilization are also significant when increasing the log of probability of RLR 1.406 of compliance for RLR. The results would help develop safer pedestrian infrastructures and engineering countermeasures as well as assist the researchers and practitioners in better understanding pedestrian crossing behavior at signalized intersections. KEYWORDS:Pedestrian’s violation,Road safety, Signalized crosswalk, Red-light running.

Developing a two-stage auditory warning system for safe driving and eco-driving at signalized intersections: A driving simulation study

In-vehicle intersection warning systems represent a promising approach for informing drivers of potential danger to reduce crashes and improve intersection safety. However, there is limited research on drivers’ eco-driving performances, such as fuel consumption and emission, when drivers adapt their behaviors to the systems. In this study, an innovative two-stage in-vehicle intersection warning system was proposed to reduce red-light running (RLR) violations. Forty-five drivers participated in a simulated driving experiment and their driving performances at the intersections were evaluated to examine the effectiveness of the warning system. The measures included stop/go decision, RLR rate, average speed and deceleration, brake transition time, brake level, fuel consumption, and emission of CO and NOx. The results indicated that the warning system had a positive effect on drivers’ safe driving and eco-driving performances, such as reducing the RLR rate, advancing and smoothing the deceleration and reducing fuel consumption and emission. Moreover, the impact of warning on drivers’ performances varied with the time to the onset of yellow light. The research has practical implications for the automobile industry to use vehicle-to-infrastructure technology in the design of in-vehicle warning systems to improve driver behaviors at intersections.

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.

DRIVER PERFORMANCE THROUGH THE YELLOW PHASE USING VIDEO CAMERAS AT URBAN SIGNALIZED INTERSECTIONS

The main objective of this research is to examine the influencing parameters of driver performance through the yellow phase at urban signalized intersections with and without red-light running (RLR) cameras. Data were collected to include the intersection type, vehicle type, turning movement type, whether the vehicle position is in a platoon or not, presence of RLR cameras, green light flash devices, pedestrians, and pavement markings. A total of 2168 driver observations were extracted. Only 33.3% of the drivers stopped before the stop line, 59% of the drivers passed the intersection through the yellow phase, and 7% of the drivers committed RLR violations. The results showed that drivers were more likely to stop before the stop line through the yellow phase at locations with RLR cameras, green light flash devices, pavement markings, where pedestrians were present, and at a four-leg intersection. Chi-square tests indicated that all parameters had a significant impact on driver performance, except for the type of turning movement.

Effect of warning message on driver’s stop/go decision and red-light-running behaviors under fog condition

The red-light-running (RLR) warning system has substantial potentials in helping drivers make proper stop/go decisions and reducing the RLR violations. Adverse foggy weather degrades drivers’ performances and may also affect the effectiveness of the RLR warning system. However, limited research has been conducted regarding the impact of the RLR warning on driving performances under foggy weather. Thus, this study aims to explore drivers’ decision-making process and RLR behaviors at intersection dilemma zones and evaluate the effectiveness of the RLR auditory-warning (RLR-AW) system in both fog and clear weather conditions. A concept of the RLR-AW system was proposed and designed in a driving simulator experiment. The simulated driving with the RLR-AW system was conducted in both clear and foggy weather conditions. The results show that drivers took compensation actions in fog while approaching the intersection, such as driving at lower speeds and using harder maximum brakes. The RLR-AW was able to reduce RLR rates in both clear and fog conditions, and drivers tended to respond more quickly and take smoother brake reactions with the RLR-AW provided. Moreover, the RLR-AW showed more remarkable influences on drivers’ behaviors in fog with higher decrement in brake reaction time and maximum deceleration rate. Overall, findings of the study shed light on the design of in-vehicle RLR-AW system and highlight the necessity of drivers applying the system in adverse weather conditions.

Determination and Utilization of Dilemma Zone Length and Location for Safety Assessment of Rural High-Speed Signalized Intersections

The objective of the present study is to see how the dilemma zone length and location are related to intersection safety at rural high-speed signalized intersections. For that, dilemma zones at 30 rural signalized intersection approaches where similar traffic operations and land-use conditions are present were identified, and traffic conflicts associated with the dilemma zones were investigated. Drivers’ stop or go behaviors as well as vehicle dynamics (e.g., vehicle speeds and locations) during the yellow and red clearance intervals were collected and analyzed to determine the dilemma zone length and location of each site. Red-light running violations and vehicles’ abrupt stops were also collected to investigate the relationship between such conflicts and dilemma zone length and location. As a result, two dilemma zone conflict models were developed. The analysis results show that the conflict models are accurate enough to predict the safety level of high-speed signalized intersections using the two dilemma zone variables. Results show that the chance of intersection angle conflicts increases if the dilemma zone is located farther from the intersection stop bar. Results also show that there would be a high chance of rear-end conflicts if the dilemma zone length is longer. The models were validated with additional datasets, and acceptable root means square error and mean absolute percentage error values were obtained as a result.

Mining Characteristics of Speeding and Red Light Running Violations Using Association Rules

Mining Characteristics of Speeding and Red Light Running Violations Using Association Rules

Evaluation of red-light camera enforcement using traffic violations

Abstract The State of Qatar started to use red-light cameras in 2007 at key signalized intersections and the rate of installation has subsequently increased. In 2017, 19.2% of signalized intersections are equipped with red-light cameras. In many cases, the cameras are not installed on all approaches to the intersections. The purpose of this study is to compare the red-light running violations on approaches with and without red-light running enforcement cameras at the same intersections. Actual field observations were used in this study. Different variables were investigated, including the day of the week, time of day, traffic volume, the possibility of glare on an approach, and the lengths of the yellow and all-red times. A regression tree model was used to explain the characteristics associated with the violations. The results showed that the number of violations on low-volume approaches was five times higher than on high-volume approaches. The results also showed that the presence of the cameras significantly lowered red-light running violations. High-volume approaches without cameras had an approximately eight times higher rate of violations than high-volume approaches with cameras. The analysis also showed that bringing the all-red interval closer to the values recommended by the Institute of Transportation Engineers formula may bring down the rates of violations for low-volume approaches. As with any observational data mining method, the study could benefit from a larger sample size. The method used in the study was effective and is easily transferable to other locations. The results of this study can be used in developing new strategies to improve safety at signalized intersections.

Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations

The Type II dilemma zone describes the road segment to a signalized intersection where drivers have difficulties to decide either stop or go at the onset of yellow signal. Such phenomenon can result in an increased crash risk at signalized intersections. Different types of warning systems have been proposed to help drivers make decisions. Although the warning systems help to improve drivers’ behavior, they also have several disadvantages such as increasing rear-end crashes or red-light running (RLR) violations. In this study, a new warning system called pavement marking with auxiliary countermeasure (PMAIC) is proposed to reduce the dilemma zone and enhance the traffic safety at signalized intersections. The proposed warning system integrates the pavement marking and flashing yellow system which can provide drivers with better suggestions about stop/go decisions based on their arriving time and speed. In order to evaluate the performance of the proposed warning system, this paper presents a cellular automata (CA) simulation study. The CA simulations are conducted for four different scenarios in total, including the typical intersection without warning system, the intersection with flashing green countermeasure, the intersection with pavement marking, and the intersection with the PMAIC warning system. Before the specific CA simulation analysis, a logistic regression model is calibrated based on field video data to predict drivers’ general stop/go decisions. Also, the rules of vehicle movements in the CA models under the influence by different warning systems are proposed. The proxy indicators of rear-end crash and potential RLR violations were estimated and used to evaluate safety levels for the different scenarios. The simulation results showed that the PMAIC countermeasure consistently offered best performance to reduce rear-end crash and RLR violation. Meanwhile, the results indicate that the flashing-green countermeasure could not effectively reduce either rear-end crash risk or RLR violations. Also, it is found that the pavement-marking countermeasure has positive effects on reducing the rear-end risk while it may increase the probability of RLR violation. Lastly, the implementation of the proposed warning system is discussed with the consideration of connected-vehicle technology. It is expected that the dilemma zone issues can be efficiently addressed if the proposed countermeasure can be employed within connected vehicle technology.

Estimation of red-light running frequency using high-resolution traffic and signal data

Red-light-running (RLR) emerges as a major cause that may lead to intersection-related crashes and endanger intersection safety. To reduce RLR violations, it’s critical to identify the influential factors associated with RLR and estimate RLR frequency. Without resorting to video camera recordings, this study investigates this important issue by utilizing high-resolution traffic and signal event data collected from loop detectors at five intersections on Trunk Highway 55, Minneapolis, MN. First, a simple method is proposed to identify RLR by fully utilizing the information obtained from stop bar detectors, downstream entrance detectors and advance detectors. Using 12 months of event data, a total of 6550 RLR cases were identified. According to a definition of RLR frequency as the conditional probability of RLR on a certain traffic or signal condition (veh/1000veh), the relationships between RLR frequency and some influential factors including arriving time at advance detector, approaching speed, headway, gap to the preceding vehicle on adjacent lane, cycle length, geometric characteristics and even snowing weather were empirically investigated. Statistical analysis shows good agreement with the traffic engineering practice, e.g., RLR is most likely to occur on weekdays during peak periods under large traffic demands and longer signal cycles, and a total of 95.24% RLR events occurred within the first 1.5s after the onset of red phase. The findings confirmed that vehicles tend to run the red light when they are close to intersection during phase transition, and the vehicles following the leading vehicle with short headways also likely run the red light. Last, a simplified nonlinear regression model is proposed to estimate RLR frequency based on the data from advance detector. The study is expected to helpbetter understand RLR occurrence and further contribute to the future improvement of intersection safety.

Predicting Driver Behavior during the Yellow Interval Using Video Surveillance.

At a signalized intersection, drivers must make a stop/go decision at the onset of the yellow signal. Incorrect decisions would lead to red light running (RLR) violations or crashes. This study aims to predict drivers’ stop/go decisions and RLR violations during yellow intervals. Traffic data such as vehicle approaching speed, acceleration, distance to the intersection, and occurrence of RLR violations are gathered by a Vehicle Data Collection System (VDCS). An enhanced Gaussian Mixture Model (GMM) is used to extract moving vehicles from target lanes, and the Kalman Filter (KF) algorithm is utilized to acquire vehicle trajectories. The data collected from the VDCS are further analyzed by a sequential logit model, and the relationship between drivers’ stop/go decisions and RLR violations is identified. The results indicate that the distance of vehicles to the stop line at the onset of the yellow signal is an important predictor for both drivers’ stop/go decisions and RLR violations. In addition, vehicle approaching speed is a contributing factor for stop/go decisions. Furthermore, the accelerations of vehicles after the onset of the yellow signal are positively related to RLR violations. The findings of this study can be used to predict the probability of drivers’ RLR violations and improve traffic safety at signalized intersections.

Red-light running traffic violations: A novel time-based method for determining a fine structure

In 2016, the monetary fine for a red-light running (RLR) traffic violation varies widely in the U.S., with a fine of $50 in North Carolina and as much as $490 in California. Currently, a scientific method for determining the monetary fine based on the safety impacts associated with such violations does not exist, thereby causing disparities in fine structures. This study develops a novel fine structure for RLR traffic violations based upon the estimated economic impact of potential crashes by RLR violations and estimated delays caused by providing all-red intervals to prevent potential conflicts. A physical model is developed to determine the crash probability at a discrete time after the traffic signal turns red. The Highway Capacity Software is also employed to estimate additional delay incurred by road users. Considering that the use of red-light cameras is increasing in the nation, while it is often criticized as a revenue instrument, policymakers need to develop an objective fine structure that closely reflects the risk a RLR vehicle poses to other drivers.

Red-light running violation prediction using observational and simulator data

In the United States, 683 people were killed and an estimated 133,000 were injured in crashes due to running red lights in 2012. To help prevent/mitigate crashes caused by running red lights, these violations need to be identified before they occur, so both the road users (i.e., drivers, pedestrians, etc.) in potential danger and the infrastructure can be notified and actions can be taken accordingly. Two different data sets were used to assess the feasibility of developing red-light running (RLR) violation prediction models: (1) observational data and (2) driver simulator data. Both data sets included common factors, such as time to intersection (TTI), distance to intersection (DTI), and velocity at the onset of the yellow indication. However, the observational data set provided additional factors that the simulator data set did not, and vice versa. The observational data included vehicle information (e.g., speed, acceleration, etc.) for several different time frames. For each vehicle approaching an intersection in the observational data set, required data were extracted from several time frames as the vehicle drew closer to the intersection. However, since the observational data were inherently anonymous, driver factors such as age and gender were unavailable in the observational data set. Conversely, the simulator data set contained age and gender. In addition, the simulator data included a secondary (non-driving) task factor and a treatment factor (i.e., incoming/outgoing calls while driving). The simulator data only included vehicle information for certain time frames (e.g., yellow onset); the data did not provide vehicle information for several different time frames while vehicles were approaching an intersection. In this study, the random forest (RF) machine-learning technique was adopted to develop RLR violation prediction models. Factor importance was obtained for different models and different data sets to show how differently the factors influence the performance of each model. A sensitivity analysis showed that the factor importance to identify RLR violations changed when data from different time frames were used to develop the prediction models. TTI, DTI, the required deceleration parameter (RDP), and velocity at the onset of a yellow indication were among the most important factors identified by both models constructed using observational data and simulator data. Furthermore, in addition to the factors obtained from a point in time (i.e., yellow onset), valuable information suitable for RLR violation prediction was obtained from defined monitoring periods. It was found that period lengths of 2–6m contributed to the best model performance.

Discrepancy analysis of driving performance of taxi drivers and non-professional drivers for red-light running violation and crash avoidance at intersections

Due to comfort, convenience, and flexibility, taxis have become increasingly more prevalent in China, especially in large cities. However, many violations and road crashes that occurred frequently were related to taxi drivers. This study aimed to investigate differences in driving performance between taxi drivers and non-professional drivers from the perspectives of red-light running violation and potential crash involvement based on a driving simulation experiment. Two typical scenarios were established in a driving simulator, which includes the red-light running violation scenario and the crash avoidance scenario. There were 49 participants, including 23 taxi drivers (14 males and 9 females) and 26 non-professional drivers (13 males and 13 females) recruited for this experiment. The driving simulation experiment results indicated that non-professional drivers paid more attention to red-light running violations in comparison to taxi drivers who had a higher probability of red-light running violation. Furthermore, it was found that taxi drivers were more inclined to turn the steering wheel in an attempt to avoid a potential collision and non-professional drivers had more abrupt deceleration behaviors when facing a potential crash. Moreover, the experiment results showed that taxi drivers had a smaller crash rate compared to non-professional drivers and had a better performance in terms of crash avoidance at the intersection.

Evaluation of Confirmation Lights on Red Light Running Violations by Time-into-Red Phase

Red light running (RLR) crashes at urban signalized intersections continue to be a serious safety concern for many communities across the United States. Recent national crash data indicated that 676 fatalities were caused by RLR in 2009, which represents approximately 10% of all intersection crashes and 2% of all roadway fatalities. This research studied confirmation lights and their effect on RLR violations’ time into red. Two sets of confirmation lights were installed at two busy urban intersections in Overland Park, Kansas. With the use of a binomial test of proportions, the study found that there was a significant increase in violations within the first second of the red indication 1 month after deployment. Three months after deployment, there was a decrease in violations over 1 s, but this decrease was statistically not significant. Although there was an observed increase in violation rate, the increase did not translate into violations occurring later in the red cycle. In the 3-month after-period, there was a decrease in the violation rate but an increase in violations within the first second of the red indication and a decrease in violations between 1.0 and 3.0 s. The results for this period were statistically not significant and which suggest that the confirmation light had little effect on a violation’s time into the red. In all periods after deployment, there were no violations occurring after 3 s into the red. This finding means that drivers did not use the confirmation lights to traverse the intersection during the all-red cycle.

Portable Traffic Signals in Conjunction with Pilot Car Operations at Two-Lane, Two-Way Rural Highway Work Zones

A portable traffic signal (PTS) system is designed to move traffic through two-way rural highway work zones with one lane open to traffic. The primary objective of this study was to evaluate the use of PTS systems in conjunction with pilot car operations and to compare three conditions as means to control traffic at one-lane, two-way work zones: ( a) flagging only, ( b) PTS with flagger, and ( c) PTS only. The measures of effectiveness were determined with respect to ( a) red light running (RLR) violation percentages, ( b) vehicle delay estimates, ( c) queue lengths, and ( d) green interval time. Comparisons of the operational parameters of average wait time, average queue length, and average green interval length indicated that all three conditions provided a similar level of operational efficiency. Percentages of RLR vehicles under the flagger-only, PTS-with-flagger, and PTS-only conditions were 1.1%, 1.3%, and 3.1%, respectively. A test of proportions indicated a significant difference in the number of violations between the three conditions. In addition, no significant difference was found in the number of RLR vehicles that followed an already-departed queue in the presence or the absence of a flagger. Furthermore, a significant difference was found in the number of vehicles that left the queue and the number of RLR vehicles that disregarded the traffic control under both conditions. These findings indicated more compliance when a flagger was present than when one was not. Results of an exploratory delay analysis indicated a 5% reduction in the total observed delay in the presence of a flagger.

Evaluation of Intersection Confirmation Lights with Targeted Enforcement to Reduce Red Light Running Violations at Freeway Ramp Intersections

Red light running (RLR) is an event where a vehicle enters an intersection when the red signal is indicated. These events are known to cause hundreds of fatalities, thousands of injuries, and tens of thousands of property damage crashes annually. The objective of this research study was to evaluate the changes in RLR events at freeway on-ramp intersections in the presence of targeted enforcement stationed on the adjacent on-ramp. The enforcement officers were aided by confirmation lights that could only be seen by the officers stationed on the on-ramps. The primary performance metric used was the change in RLR rates between the before period and 1 month and 3 months after installation. It was found that the treatment sites experienced statistically significant reductions of 60% and 56% in violation rates during the respective study periods. The control sites also experienced 39% and 60% reductions in violation rates during the respective study periods. Additional analysis of the number of officers deployed and citations written was conducted to determine a possible reason for the reduction in violation rates at the control sites, but the results indicated no change in the enforcement structure between the before-and-after periods. It is possible that the effects at the study sites changed driver behavior over a wider area, which may have unexpectedly been observed at the control sites. This study showed that confirmation lights, when used in combination with targeted enforcement, can have a beneficial impact in reducing RLR violations at on-ramp signalized intersections, possibly over a wider area than expected.

Red-light running rates at five intersections by road user in Changsha, China: An observational study

The red-light running rate by type of road users has not been reported in China so far. We conducted an observation study to report the violation rate in Changsha, China. Portable digital devices were used to record red-light running violations at five selected intersections. The observation was performed for three days (weekday, weekend and holiday), four time periods per day and an hour per time period (peak and off-peak hours in the morning and in the afternoon). Violation rate was calculated as number of violations divided by total number of vehicles/pedestrians×100%. We used adjusted violation rate ratio (VRR) to quantify the effects of type of day and time period based on Poisson model. Totally, 162,124 vehicles (including motor vehicles, motorcycles and bicycles) and 31,649 pedestrians were recorded. The red-light running rate was 0.14% for motor vehicle drivers, far lowering than those for motorcyclists (18.64%), bicyclists (18.74%) and pedestrians (18.54%). The rate on holiday was 1.89 times that on weekday for drivers. The rate for motorcyclists was high in off-peak hours (adjusted VRR: 1.11), but low on weekend and on holiday (adjusted VRRs: 0.80 and 0.65). The rate for bicyclists was 32% lower on weekend than on weekday. For pedestrians, the rates were high on weekend and holiday and in off-peak hours (adjusted VRR: 1.09, 1.67 and 1.30). The red-light running rate of motor vehicle drivers is far lower than those for motorcyclists, bicyclists and pedestrians. The effects of type of day and time period on violation rate vary with road users, indicating the type of day and time period should be considered when developing and implementing interventions to reduce red-light running of different road users.

Value of Confirmation Lights and Their Unintended Impact on Driver Compliance and Deterrence to Running a Red Light

In the United States, red light running (RLR) continues to be a safety concern for many communities with signalized intersections. In 2012, the Insurance Institute for Highway Safety reported that nationwide, RLR had resulted in 683 fatalities and 133,000 injury-related crashes. Confirmation lights have been deployed in many communities as a countermeasure to aid law enforcement in reducing RLR violations; however, the effectiveness of confirmation lights has not been well documented. This research study was conducted to determine the effectiveness of confirmation lights at signalized intersections for the purpose of reducing RLR violations. Confirmation lights were installed for the protected left-turn movements at two signalized intersections in Lawrence, Kansas. In addition to the two treatment sites, 11 comparison sites, including six spillover intersections and five control intersections, were studied. A before–after analysis was conducted with field-based violation data. Without changing the local, traditional police department–run RLR enforcement program during the study period, video data were collected before installation, and 1 month and 3 months after installation. A test of proportions found a statistically significant 42% reduction in left-turning RLR violations 3 months after the installation of confirmation lights at the treatment sites. An analysis of RLR time into red with chi-square tests indicated that the system was less effective in changing the time from the onset of red until the mean RLR violation occurred. Overall, the presence of the confirmation lights at the signalized intersection indicated effectiveness in both reducing the number of RLR violations and also changing driver behavior.