Red Light Running Research Articles

Robust real-time traffic light detector on small-form platform for autonomous vehicles

Timely and accurate detection and recognition of traffic lights are critical for Autonomous Vehicles (AVs) to avoid crashes due to red light running. This paper integrates a new robust machine learning based solution by combining a Convolutional Neural Network (CNN) with computer vision techniques to achieve a real-time traffic light detector. The proposed detection and recognition algorithm is capable of recognizing traffic lights on low-power small-form platforms, which are lightweight, portable, and can be mounted on AVs in daylight scenarios. The LISA open-source dataset is utilized with augmentation methods to increase the accuracy of the solution. The proposed approach achieves 93.42% of accuracy at a speed of 30.01 Frames Per Second (FPS) on an NVIDIA Jetson Xavier platform without using hardware accelerators such as FPGA. This solution is expected to promote the quicker adoption and wider deployment of AVs by increasing the chances of avoiding crashes and ultimately saving lives.

Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach

To effectively reduce the number of red-light violations and crashes, it is crucial to explore RLR behavior at local intersections, understand the contributing factors, and identify the riskiest intersections by estimating RLR frequency. In this study, a finite mixture modeling method was utilized to understand the contributing factors to RLR behavior and estimate this violating behavior. To develop the RLR estimation models, performance metrics and signal phasing data were collected from the Automated Traffic Signal Performance Measures (ATSPMs) system in two jurisdictions in Arizona: Pima County and the Town of Marana. The results from calibrated models showed that an increase in traffic flow, intersection delay, number of approach lanes, and split failure is associated with an increase in the likelihood of observing red-light violations. In addition, it was found that an increase in cycle length is associated with a decrease in the likelihood of observing the red-light violation. The results of comparing the proposed RLR estimation method with several conventional methods, the Poisson Generalized Linear Model (PGLM), Zero-inflated Poisson Regression Model (ZIPM), and Zero-inflated Negative Binomial Regression Model (ZINB) showed the proposed method outperforms all the models in terms of both model fit and accuracy. The application of the proposed method could be used to analyze the intersections with the highest number of red-light violations. Furthermore, the presented transferability results can be advantageous to transportation agencies within Arizona and urban areas with similar characteristics by providing insight into which model specifications may provide the best RLR estimation accuracy.

Analysis of the Contributory Factors to Accidents at Signalized Intersections using Generalized Estimating Equation with Negative Binomial Distribution

Ensuring safety on roadways and controlling conflicting and merging streams of traffic at signalized intersections has become a major concern for transportation engineers and road users globally and locally. Many factors contribute to accident occurrence at road segments and intersections with driver behavior being the major contributor.  Other factors include those related to the vehicle and the road. The primary aim of this study is to understand the impact of different factors causing traffic accidents at signalized intersections.  It also include developing a descriptive study of traffic accident data to show the variation with different factors at both intersections and roadway segments. To achieve this objectives, 22 signalized intersections and all roadways in Duhok city/Iraq were selected.  Assessing the effects of geometric characteristics and driver behavior on safety performance of the sites have been achieved through observations of trends from graphs and use of appropriate advanced statistical analysis such as Generalized Estimating Equation (GEE) with NB distribution and log link function.  The results show that approach width, driver inattention, red light running and surface moisture condition are the significant contributors to accident occurrence. The study results highlight the importance of driver education, police enforcement and appropriate geometric design of intersections to the reduction of accident occurrence. &nbsp

Dynamic dilemma zone protection system for high-speed signalized intersections: A comprehensive safety -operational assessment

Dynamic dilemma zone protection (DZP) system with continuous vehicle tracking via radar sensors has been implemented at multiple locations in Alabama to promote the safety and operational efficiency of high-speed signalized intersections. The present study discusses the main features of the DZP system, and its comprehensive assessment in three important aspects: changes in (1) vehicle arrival distributions in different signal intervals (green, yellow, and red), (2) dilemma zone length and location, and (3) dilemma zone conflicts before and after the DZP system implementation. For the system assessment, an extensive amount of data collection and analysis efforts were performed with radar sensor-based vehicle attribute data and high-resolution signal controller data collected from seven intersection approaches where a DZP system has been recently implemented. The analysis results showed that vehicle arrivals on green intervals were significantly increased after the DZP system implementation, while vehicle arrivals on the yellow and red intervals were both reduced significantly despite no change in total vehicle arrivals before and after the system implementation. The chance of dilemma zone conflicts (i.e., red-light running and abrupt stops) was also significantly reduced after the DZP system implementation. Results also showed that the dilemma zone length became shorter, and its location was closer to the intersection stop bar after its implementation. Thus, the present study claims that the DZP system promotes intersection operational efficiency by effectively utilizing available green time as well as improving intersection safety by reducing the number of vehicles trapped within the dilemma zone.

Running red lights: Risk recognition versus follow-up implementation in the case of Germany's pandemic preparation.

The discrepancy between formal arrangements to ensure health security, as assessed in the Global Health Security Index, and COVID-19 outcomes points to a broader problem where formal risk recognition is de-coupled from potentially resource-intensive follow-up policy implementation. Germany is an extreme example of this. Pre-COVID-19, Germany's Federal Office of Civilian Protection conducted two pandemic preparation exercises based on scenarios which closely mirrored the current COVID-19 pandemic: (a) a multi-jurisdictional, multi-agency crisis management exercise assuming a global influenza pandemic and (b) a joint federal and expert-agency based risk-analysis assuming the outbreak of a modified severe acute respiratory symptom virus. While informing legal and institutional reforms, key recommendations on storing personal protective equipment (PPE) and disinfectants for front-line staff were subsequently ignored. PPE shortages initially put staff at risk, led to export restrictions on PPE, and later on hampered the country's ability to address a second wave of the pandemic. This short paper calls for a fuller exploration of factors which hinder ''implementation post-cognition.''.

IoT-Based System for Automated Accident Detection and Rescue

One of the most common causes of death is auto accidents. The worst thing that may happen to a road user is a traffic accident, despite the fact that they happen regularly. The worst part is that we fail to learn from our on-the-road errors. Most people who use roads regularly are extremely familiar with the fundamental guidelines and safety procedures that should be followed, but it is only their own negligence that results in accidents and wrecks. Accidents and crashes are primarily caused by human error. Here are some examples of normal human actions that result in accidents. 1. Driving too fast; 2. Driving while intoxicated; 3. Distracting the driver; 4. Running red lights; 5. Avoid utilizing safety equipment, such as seatbelts and helmets; 6. Driving erratically and overtaking improperly in order to save lives in a traffic collision, we’re going to construct an Arduino-based car accident alert system that combines GPS, GSM, and an accelerometer. If the accelerometer detects an abrupt shift in the vehicle’s axis, the GSM module alerts you and communicates the location of the accident to your cell phone. The GPS module’s latitude and longitude are utilized to pinpoint the accident’s location, which is provided as a Google Map link. The message also contains the vehicle’s speed in knots.

Evolutionary game analysis of multi-actor pedestrian crossing under peak hour considering the role of government incentives

Pedestrian traffic violations at intersections are a major cause of pedestrian accidents, especially red-light running, and they are repeatedly prohibited in China. This paper uses evolutionary game theory to investigate the complex interaction mechanism between the behavior of government, pedestrians, and drivers at intersections when pedestrians are preparing to cross a crosswalk at a red light. Firstly, the impact of the illegal pedestrian crossing on motor vehicle traffic flow during rush hour is considered, and the impact of government giving certain incentives to drivers and pedestrians who obey the rules on their behavioral decisions is studied; secondly, ideal events are defined and the impact of key factors on the dynamic evolutionary process is analyzed. This paper finds that drivers’ behavioral decisions evolve to gradually stabilize in obeying traffic rules, while government and pedestrians’ behavioral decisions are in cyclical fluctuations. The main factors affecting desirable events in terms of priority are: firstly, pedestrian waiting time, collision probability between violating pedestrians and non-violating drivers, damage to pedestrians in traffic accidents; then fines for violating pedestrians; and finally, other factors. The results of the study can provide recommendations for the government to improve the transportation environment.

Hardware-in-the-Loop Simulation for Assessing Red-Light Violation Warning Application With Semi-Actuated Signal Control in a Connected Vehicle Environment

Understanding the safety and mobility impacts of connected vehicle (CV) applications is critical for ensuring effective implementations of these applications. This study provides an assessment of the safety and mobility impacts of the red-light violation warning (RLVW), a CV-based application at signalized intersections, under semi-actuated signal control utilizing a hardware-in-the-loop simulation environment. With actuated traffic signal operations, there is uncertainty in the end-of-green information provided to the vehicles using CV messages. The RLVW algorithm lacks the input information about when exactly the phase is going to be terminated since this termination occurs when a gap of a particular length is encountered at the detector. This study investigates a method recently proposed in a national effort to provide an assured green period (AGP) to grant definitive times for when the green interval will end. AGP mitigates the uncertainties related to termination of the green signal associated with actuated signal control and is expected to improve the performance of the CV RLVW application. Results showed that the safety benefits of RLVW without the use of AGP were limited. On the other hand, by introducing AGP with 100% RLVW, the number of red-light running events at signalized intersections was reduced by approximately 92%. However, the application of the AGP, as applied and assessed in this paper, can increase the number of stops and approach delay. This issue will need to be further investigated to determine the optimal setting of the AGP considering both mobility and safety impacts.

Delivering hot food on motorcycles: A mixed method study of the impact of business model on rider behaviour and safety

In Great Britain, motorcyclists have the highest fatality rate per billion passenger miles of all road users, and people who work on motorcyclists have a much greater risk of injury compared to other motorcyclists. Many hot meal delivery services are provided by people using motorcycles. Work can be accessed by an app or by being employed by a restaurant. There is a need to understand whether the business model under which riders work influences their experience of risk or whether this is just related to the risks of delivering food by motorbike per se. Interviews were conducted with six gig riders and 14 employed riders and an online survey was completed by 164 riders working via apps and 155 employed by restaurants. Gig workers were significantly more likely to agree that that their phone was a distraction and that they violated traffic laws related to speeding, red light running and, unsurprisingly, they had more points on their licence compared to employed riders. Gig riders were also more likely to be incentivised to ride in dangerous conditions and carry unstable loads. Gig workers were more likely to report being involved in collisions where their vehicle was damaged and where someone was injured. These findings were also evident in interview narratives. More needs to be done to make gig companies embed practices that do not increase risks for delivery riders.

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.

Exploration of motorcyclists’ red-light running and helmet use at signalized intersections in Ghana

Objective There is a dearth of empirical studies on motorcyclists’ red-light running and helmet use at signalized intersections in low and middle-income countries like Ghana, Nigeria and Malaysia. This study seeks to fill the gap by looking at red-light running and helmet use at signalized intersections in the Cape Coast metropolis, Ghana. The study also identified potential areas of intervention to reduce the dangers posed by motorcyclists’ red-light running in the Cape Coast Metropolis without the use of a helmet. Method A naturalistic exploratory un-obstructive observational approach was used in assessing this phenomenon. The relationship between motorcyclists’ behaviors and motorcyclists’ observed demographic characteristics, the locality of the intersection, time of the week and presence of pillion passengers were analyzed. Chi-Square test of independence was used to establish the statistically significant relationships between dependent and independent variables. Results In all, 2,225 motorcyclists and 744 pillion passengers were observed. The results revealed that 33.1% of the motorcyclists ran a red light with 45.4% not using a helmet. Red-light running at signalized intersections was significantly linked to the locality of the intersection, time of the week, and helmet use. The helmet use was low and significantly associated with the presence of a pillion passenger and whether the pillion passenger used a helmet or not. Conclusion Red-light running is influenced by locality of intersection, time of the week and helmet use. Efforts to reduce red-light running and improve helmet use should involve road safety education, awareness creation, and enforcement of traffic laws by the officials of the National Road Safety Authority and Motor Transport and Traffic Department of the Ghana Police Service. City managers in other low and middle-income countries can use the findings in the study to inform policy.

Automatic detection system of speed violations in a traffic based on deep learning technique

With the growth in vehicular traffic, there is a greater risk of road accidents. Over speeding, intoxicated driving, driver distractions, red-light runners, ignoring safety equipment such as seat belts and helmets, non-adherence to lane driving, and improper overtaking are the leading causes of accidents. Speed violation, in particular, has a significant influence on today’s transportation. Also, detecting this speed violation and punishing this violator are more time-consuming tasks. For that reason, a novel automatic speed violation detection in traffic based on Deep learning is proposed in this paper. This proposed method is separated into two working modules: object detection and license plate recognition. The object detection module uses the most efficient PP YOLO neural networks. It utilizes open ALPR (Automatic License Plate Recognition) for the vehicle’s number plate identification, which passes the traffic above maximum speed. With the number plate details, the authorities can take action against the rule violator with less time and effort. The simulation results show that the proposed automatic speed violation detection system also has an accuracy rate of 98.8% for speed violation detection and 99.3% for license plate number identification, demonstrating that the approach described in this work has a higher performance in terms of accuracy. Furthermore, the proposed technique was compared to recent existing results.

The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections

Introduction: With a significant increase in accidents involving cyclists, more attention has been paid to cycling safety. Previous studies on traffic accident revealed that red-light violations of non-motorized vehicles have become the leading cause of crashes at signalized intersections. The objective of this study is to investigate the impact of non-motorized traffic enforcement cameras (NTECs) on the red-light running behavior of cyclists, including ordinary e-bike riders, delivery e-bike riders, and bicyclists. Method: An observational study of 5,217 cyclists was conducted at six primary intersections in the downtown areas of Nanjing, China. A random parameter logit model was used to explore the safety effect of the NTECs and other factors related to red-light violation behavior. Results: The results indicate higher reductions in red-light violations at intersections with the NTECs compared than at the non-adjacent intersections without the NTECs. Furthermore, the NTECs demonstrated a beneficial but smaller impact on the reduction of violations at adjacent intersections. Another primary finding was that the effects of the NTECs varied among three types of cyclists (ordinary e-bike riders, delivery e-bike riders, and bicyclists). Conclusions: The NTECs were found to be most effective in the case of delivery e-bike riders, followed by ordinary e-bike riders and bicyclists. In addition, the factors associated with the red-light violation behaviors of these three groups were also found to be different. In general, group size, maximum waiting time, waiting position, and visual search were significantly related to the probability of red-light violations in all three groups. Practical Applications: Based on these findings, this study provides some feasible suggestions for improving the effect of the NTECs and for the future extension of the NTECs installation, such as the randomization of the enforcement and publicity campaigns.

Risky business: Comparing the riding behaviours of food delivery and private bicycle riders

The growth in the gig economy and a preference for home delivery of meals due to COVID-19 have led to huge growth in the food delivery business internationally and consequent road safety concerns. There is increasing evidence that delivery riding is an occupation with significant road safety risks because work pressures encourage risky behaviours. However, there is little or no research that directly compares delivery and private riders. Thus, the aim of this study was to examine the impact of riding for work by comparing the observable riding behaviours of food delivery and private bicycle riders. Specifically, this investigation used decision trees to analyse the prevalence and patterns of risky riding behaviours of 2274 bicycle food delivery riders (BFDRs) and 1127 private bicycle riders observed in the inner suburbs of Brisbane, Australia. The results showed that helmet use was higher for BFDRs than private riders (99.8% versus 93.4%) but varied by company and for some companies, female BFDRs had lower wearing rates. Male BFDRs on electric bikes were more likely to wear helmets than those on standard bikes (99.7% versus 94.9%). Using a handheld mobile phone or having a mobile phone in a cradle was less common for one company (0.6%) than for the others (3.0%) or among private riders (1.8%). Among riders from the Other Companies, using a handheld mobile phone was more common on standard bikes and differed by time of day. Female BFDRs were more likely to be observed using handheld mobile phones. Overall, 24.0% of riders facing a red traffic or pedestrian signal (“red light”) did not stop. This was more common among riders who rode on the footpath (Australian term for sidewalk), and particularly those who moved between the footpath and the road on electric bikes (49.5%) and among those who rode in the wrong direction in the traffic lane (55.0%). Whether the rider was a BFDR or private rider had little influence on red light running. The results suggest that BFDRs are not more likely to perform the risky behaviours examined, but that other factors such as bicycle type, gender, time of day and infrastructure appear to be more important determinants. However, the differences among companies suggest that organisational factors deserve further investigation.

Willingness and intention to run a red light among motorcyclists

Introduction: An important issue affecting the safety of riders is running a red light. Many factors can affect this risky behavior including demographic, safety, and meteorological factors. Method: Using two models, the Prototype Willingness Model (PWM) and the Theory of Planned Behavior (TPB), this study examines this behavior. In this study, 853 participants completed an online questionnaire. Results: The results indicated that older people and those who were married ran the red light less frequently compared with other riders. Additionally, people who rode motorcycles for more hours in a day had greater intentions and willingness to run the red light. People who had prior risky experiences while riding in the past reported running red lights more frequently. When comparing the two models, the PWM predicted a greater level of variance in the red-light running behavior when compared with the TPB. Among the components of the TPB model, the attitude factor was the strongest predictor. Prototype similarity was the strongest predictor of red-light running among the components of the PWM. Finally, it was observed that using a road safety educational (RSE) solution can effectively reduce the rate of red light traffic running among motorcyclists.

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.

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.

Examining unobserved factors associated with red light running in Vietnam: A latent class model analysis

Abstract Red-light running (RLR) is a crucial violation that causes traffic accidents and injuries. Understanding factors that affect RLR is very significant to reduce the potential of this violation. Current studies have paid considerable attention to the observable factors, but not to unobservable factors. This study aims to examine the effects of observable and unobservable factors on RLR. This study uses a latent class model (LCM) to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes. This study incorporates psychological determinants into the LCM to account for unobservable factors. The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators. Such a study has not yet been conducted in the existing literature. In addition, a comprehensive comparison of the LCM and a traditional ordered probit model was conducted. Overall, the results suggest that the LCM is superior to the model that does not consider latent classes. Our estimation results are in alignment with previous studies on RLR: males, younger drivers/riders, less educated road users and motorcyclists are more likely to run red lights. An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators, the absence of traffic police, and long waiting times—increase the possibility of violations. Based on these results, we provide suggestions to policymakers and traffic engineers: the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.

The effect of risk perception and other psychological factors on mobile phone use while crossing the street among pedestrians

Pedestrians may be the most vulnerable group among road users, and mobile phone use while crossing the street is ubiquitous worldwide in this information era. However, previous studies have found that such distracting behaviors may increase the risk of injury and death. The present study primarily aimed to explore the effect of reinforcement sensitivity theory components (i.e., Behavioral Inhibition System (BIS), Behavioral Approach System (BAS)), risk perception, attitudes towards red light running, and fear of missing out (FoMO) on mobile phone use while crossing the street among pedestrians. Risk perception was measured in three ways (i.e., assessing the probability of a negative outcome (RP-Pro), judging the severity of the consequence (RP-Se), and evaluating the general riskiness of the behavior (RP-Ri)). An online questionnaire survey was conducted, and only valid responses (N = 425) were used for subsequent data analyses. The results indicated significant differences in the responses across the risk perception questions with different focuses. Participants who reported engaging in more distracted street-crossing (i.e., high-risk takers) perceived a significantly lower risk, and this difference did not depend on the focus of risk perception. Three path analysis models with differential risk perception constructs (RP-Pro, RP-Se, and RP-Ri) were developed to examine the relationship between risk perception and distracted street-crossing. The results suggest that the relationship between these two variables does not depend on the focus of the risk perception questions. Moreover, FoMO was a predictor of mobile phone use while crossing the street, while attitudes had both direct and indirect effects on behavior. BIS and BAS had the lowest total effect on mobile phone use among pedestrians. In particular, a direct association between BAS and distracted street-crossing was found only in the model in which risk perception was measured by judging the severity of crashes caused by mobile phone use distraction. This study may be meaningful for understanding the associations between psychological factors and mobile phone use among pedestrians. The implications of the findings for the development of safety interventions are discussed in this study.

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.