Red Light Running Rates Research Articles

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.

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.

(E-)Cyclists running the red light – The influence of bicycle type and infrastructure characteristics on red light violations

Red light running is one of the most common traffic violations among cyclists. From different surveys, we know that about 40% of all cyclists run a red light at least occasionally. However, specific data on red light running of e-bike riders (pedelec and S-pedelec riders), a population of cyclists that has been growing steadily in the past few years in Germany and elsewhere, is largely missing. Similarly unclear is the role of the used infrastructure (e.g., carriageway or bike path) or the intersection type on the riders’ propensity to run the red light. The goal of this study was to investigate the red light running behaviour of three different bicycle types (bicycle, pedelec, S-pedelec) in Germany, with specific focus on various infrastructure characteristics. We reanalysed data obtained in a naturalistic cycling study, in which we observed 90 participants riding their own bicycles (conventional bicycles, pedelecs, S-pedelecs) on their daily trips over four weeks each. The video material of these trips was annotated and analysed with regard to red light running. Overall, our participants experienced nearly 8000 red light situations. In 16.3% of these situations, they ran the red light, with nearly identical rates for cyclists, pedelec and S-pedelec riders. Red light running rates were lowest when cyclists rode on the carriageway, while the complexity of the intersection appeared to play a role as well. In general, red light running was more common when riders were about to turn right instead of turning left or riding straight through the intersection. Interestingly, we also observed a considerable number of cases in which the riders changed their used infrastructure (e.g., from the carriageway onto the pavement) to avoid a red light.

Predicting e-bike users’ intention to run the red light: An application and extension of the theory of planned behavior

Electric bike (e-bike) users in China have a high red light running (RLR) rate, contributing to a large number of accidents. This paper aims to examine the psychological motivation of e-bike users’ RLR intentions. A survey questionnaire was designed employing the construct of theory of planned behavior (TPB). The survey was performed in Chengdu, China in November 2016. We found that users older than 40 identify themselves as more cautious riders. Younger riders have higher intention to run the red light. E-bike users with car drivers’ licenses regard running the red light as a more difficult task to perform, and regard this behavior as more morally wrong. Hierarchical regression was used to analyze the data. The results showed that demographic variables (age, marriage status, and college degree), TPB variables (attitude and perceived behavioral control) and extended variables (moral norm and self-identity) are significant predictors for the intention of RLR behavior. The results could provide reference for designing more effective interventions and safety education programs for reducing e-bike users’ RLR rate.

Influences of motorcycle rider and driver characteristics and road environment on red light running behavior at signalized intersections

In Thailand, red light running is considered as one of the most dangerous behaviors at intersection. Red light running (RLR) behavior is the failure to obey the traffic control signal. However, motorcycle riders and car drivers who are running through red lights could be influenced by human factors or road environment at intersection. RLR could be advertent or inadvertent behavior influenced by many factors. Little research study has been done to evaluate the contributing factors influencing the red-light violation behavior. This study aims to determine the factors influencing the red light running behavior including human characteristics, physical condition of intersection, traffic signal operation, and traffic condition. A total of 92 intersections were observed in Chiang Mai, Nakhon Ratchasima, and Chonburi, the major provinces in each region of Thailand. In addition, the socio-economic characteristics of red light runners were obtained from self-reported questionnaire survey. The Binary Logistic Regression and the Multiple Linear Regression models were used to determine the characteristics of red light runners and the factors influencing rates of red light running respectively. The results from this study can help to understand the characteristics of red light runners and factors affecting them to run red lights. For motorcycle riders and car drivers, age, gender, occupation, driving license, helmet/seatbelt use, and the probability to be penalized when running the red light significantly affect RLR behavior. In addition, the results indicated that vehicle travelling direction, time of day, existence of turning lane, number of lanes, lane width, intersection sight distance, type of traffic signal pole, type of traffic signal operation, length of yellow time interval, approaching speed, distance from intersection warning sign to stop line, and pavement roughness significantly affect RLR rates.

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 in Trinidad

Red Light Running (RLR) has been associated with road traffic collisions in many jurisdictions across the world. The study aimed at evaluating the factors related to red light running (RLR) in Trinidad and Tobago that could be incorporated into policies that would aim to reduce this tendency and its related costs. Using data collected from the Traffic Unit of Trinidad and Tobago and three major intersections in the country, analysis was done on the impact of time of the day, age of the driver, ethnicity, gender, type of the vehicle, location and day of the week which are related to RLR. The results showed that the highest number of RLR occurs during rush hours in the morning and also in the evenings. Also, Indo-Trinidadian and male drivers display a higher tendency towards RLR. It was also established that some junctions have higher RLR rates than others and that Fridays have more RLR cases than all the other days of the week. These findings are very similar to those made in other countries such as the USA. The study has resulted in specific findings that can be used to improve traffic safety policies in the country.

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.

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

Red Light Running Behavior of Electric Bicycles at Signalized Intersections in China

A comparative analysis was conducted on the red light running behavior of the riders of scooter-style electric bicycles (e-scooters), bicycle-style electric bicycles (e-bikes), and bicycles as they crossed signalized intersections. The crossing behaviors of 5,646 riders (1,568 e-bikes, 2,612 e-scooters, and 1,466 bicycles) were observed. The average red light running rates for e-bikes, e-scooters, and bicycles at the selected sites were 24.9%, 25.0%, and 18.3%, respectively. The rates for e-bikes and e-scooters were significantly higher than those for bicycles. The difference in the rates between e-bikes and e-scooters was not statistically significant. A binary logit model was developed to evaluate how various factors affected the red light running rates of two-wheeled vehicles at signalized intersections. Results of a comparison of traffic conflicts caused by various red light running two-wheeled vehicles indicated that bicycles were slightly more likely to be involved in traffic conflicts than e-bikes or e-scooters as they crossed intersections during a red pedestrian phase. Also, e-bikes were less likely than e-scooters and bicycles to be involved in traffic conflicts in the middle stage of a red pedestrian phase. Both e-scooters and e-bikes had higher crossing speeds than bicycles. Although e-scooters traveled at speeds significantly higher than e-bikes did when crossing intersections during green phases, the difference in crossing speeds between red light running e-bikes and red light running e-scooters was not statistically significant.

Turning off the cameras: Red light running characteristics and rates after photo enforcement legislation expired

In 2005 the Virginia legislature allowed the law permitting automated enforcement for red light running violations to expire. An opportunity presented itself to evaluate what would happen to red light running behavior at formerly enforced locations. Using intersections previously studied to document one city's deployment and use of photo enforcement (see Martinez and Porter, 2006), we mobilized multiple pre-expiration, immediate post-expiration, and one year post-expiration observations at camera-enforced intersections as well as two control groups consisting of same-city and a different city's non-camera locations. More than 2700 direct observations were made in these time periods, documenting the near-immediate increase in red light running at previously camera-enforced intersections. These intersections had a rate that nearly tripled immediately after the law expired, and more than quadrupled one year later. Further, within a year of the law's expiration, the low red light running rates at the previous-camera locations had recidivated to red light running rates of the control locations. Driver characteristics were not significant predictors of these rates once intersection group and traffic volume (and their interaction) were controlled, meaning red light running in this study was not linked to a particular driver type. Our results are important for scholars of intersection safety, as this is the first known peer-reviewed study documenting estimates of what could happen when automated enforcement is removed.

Analysis and Modelling of Effects of Traffic Light Operations Variability to Violation Rates at Junction

Previous research, based on potential conflicts analysis, has provided a quantitative evaluation of ‘proneness’ to red-light running (RLR) behaviour at urban signalised intersections (Giuffrè & Rinelli, 2006) varying geometric, traffic flow and driver characteristics. Recent study (Bell, Galatioto, Giuffrè, & Tesoriere, 2012) demonstrated the potential to use micro-simulation models to evaluate the ‘proneness’ to RLR behaviour at urban signalised intersections in Italian cities, varying flow characteristics and stop line distances. The micro-simulation, although at its early stages of development, has shown promise in its ability to model unintentional RLR behaviour and to evaluate alternative junction designs on proneness. In order to make more robust the new modelling framework, the need to demonstrate the transferability of the modelling approach has been addressed in this paper by using a four arms junction in Enna (Italy), where an extensive period of continuous video recording has been carried out. Moreover, in collaboration with the local Police, different cycle and green time settings have been implemented in order to measure the effects on the RLR rates. Then the measured RLR rates have been evaluated and compared to the modelling results as a validation exercise. In this way the prediction capability of the proposed potential conflict model has been extended and improved.

Field Testing of a Proposed Pavement Marking as a Red Light Running Countermeasure

When a motorist approaches a signalized intersection during the signal change period (changing from green to yellow), he/she is required to make a stop/go decision. A proposed pavement marking with a word message of SIGNAL AHEAD was proposed to be positioned at the stop sight distance from the intersection stop-bar to assist the driver in making a proper stop/go decision without running a red light or making an abrupt stop. This paper examined effectiveness of the marking as a red light running violation countermeasure through a field study. Red light running behaviors were recorded at a test intersection (with marking) and a control intersection (without marking) as a comparison. A reduction in the red light running rate was observed after the marking was applied at the test intersection, while the red light running rate did not shift significantly at the control intersection, leading to the conclusion that the marking has a good potential as a red light running countermeasure.

Impact of “Signal Ahead” pavement marking on driver behavior at signalized intersections

When drivers are approaching a signalized intersection at the onset of yellow signal, they may hesitate to decide whether to stop or cross the intersection due to the dilemma zone. As defined in the Traffic Engineering Handbook [Institute of Transportation Engineers (ITE). (1999). Traffic engineering handbook, Washington, DC: Institute of Transportation Engineers], the dilemma zone for an intersection is a specific road segment, prior to the intersection in which a vehicle approaching the intersection during the yellow phase can neither safely clear the intersection, nor stop comfortably at the stop line. In this study, a pavement marking countermeasure is proposed to reduce the dilemma zone and improve traffic safety at signalized intersections. A pavement marking with word message ‘Signal Ahead’ that is placed on the pavement upstream of a signalized intersection may have a potential to assist drivers in stop/go decisions and improve driver performance during the signal change. This paper presents a simulator-based study that was designed to test the impact of the pavement marking countermeasure on driving behaviors, including driver’s stop/go decision, red-light running violation, brake response time, and deceleration rate of the stopping vehicle. The experiment results indicated that the marking had positive effects on drivers’ behaviors at signalized intersections. It was found that the marking can reduce the probabilities of both conservative-stop and risky-go decisions, contribute to a lower red-light running rate, and result in a lower deceleration rate for stopping drivers at higher speed limit intersections. These findings suggest that the marking countermeasure may have a potential to reduce the probabilities of both rear-end and angle crashes.

Larger size vehicles (LSVs) contribution to red light running, based on a driving simulator experiment

Larger size vehicles (LSVs), comprising semis and six-wheelers drive higher and wider than passenger cars which could affect the visibility of traffic lights for the following passenger car driver at signalized intersections. This paper investigates the contribution of LSVs to red light running resulting from vertical visibility blockage based on a driving simulator experiment. Three sub-scenarios were developed in the driving simulator. The first sub-scenario served as a base sub-scenario, where the simulator car follows a passenger car. The second sub-scenario served as the test sub-scenario, where the simulator car tracks an LSV. The results obtained by comparing the sub-scenarios confirmed that LSVs generate higher red light running rate at signalized intersections due to vertical visibility blockage. The third sub-scenario (or suggested solution sub-scenario) tested a suggested solution by the Federal Highway Administration for the vertical visibility blockage issue, which consists of installing an additional signal pole on the right side of the road. The comparison between the test sub-scenario and the suggested solution sub-scenario demonstrated that the addition of a signal pole on the right side of the road decreases red light running resulting from vertical visibility blockage significantly.

Predicting Red-Light Running Behavior: A Traffic Safety Study in Three Urban Settings

Red-light running is a frequent and highly dangerous driving act. Reducing this risky behavior depends on understanding its prevalence, as well as the drivers involved. With these aims, the present study provided 5,112 observations of drivers entering six traffic-controlled intersections in three cities. Overall, 35.2% of observed light cycles had at least one red-light runner prior to the onset of opposing traffic. This rate represented approximately 10 violators per observation hour. City differences emerged as important for predicting red-light runners. Higher red-light running rates were observed in cities with larger intersections and higher traffic volumes. Time of day, although limited to the interval from 3 p.m. to 6 p.m. when observations occurred, also predicted red-light running. Red-light violators were more prevalent earlier in this period. Further, after controlling for city and time differences, two driver factors were important: safety belt use and ethnicity. Unbuckled and non-Caucasian drivers were more likely to run red lights. The importance of these findings in understanding red-light running and helping communities improve their intersection safety is discussed.