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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. [ABSTRACT FROM AUTHOR]

Motorcycle food delivery riders (MFDRs) are at a higher risk of traffic accidents compared to regular motorcyclists. Their safety is a significant issue in many developing countries, particularly in Thailand, which has the highest rate of motorcyclist fatalities globally. This study aimed to determine the prevalence of traffic accidents and explore the association between risky riding behaviors, concerns for working conditions, and accidents among MFDRs. This cross-sectional study was conducted in Chiang Mai, Thailand. Data were collected from a total of 709 MFDRs through an online platform. Collected data included working conditions, risky riding behaviors, concerns about their work conditions, and experience with traffic accidents. In the past six months, the incidence of traffic accidents among MFDRs was 18.9 %., with 2.4 % experiencing hospitalized major injuries, 11.4 % having non-hospitalized major injuries, and 5.1 % having minor injuries. A total of 84.3 % of MFDRs exhibited at least one risky riding behavior, while 5.8 % engaged in ten or more such behaviors. Multivariable logistic regression analysis with adjustments for sex, age, and body mass index indicates that red-light running (adjusted odds ratio, aOR 2.78, 95 % CI 1.07 to 7.21) and being concerned for customer behaviors (aOR 2.66, 95 % CI 1.26 to 5.62) were significantly associated with major injuries. Additionally, factors such as sleeping less than 6 h per day (aOR 1.68, 95 % CI 1.10 to 2.56), riding the wrong way (aOR 2.49, 95 % CI 1.12 to 5.52), and having concern for customer behaviors (aOR 1.91, 95 % CI 1.03 to 3.55) were associated with total number of accidents. Red-light running and concerns for customer behavior in MFDRs are associated with a higher risk of traffic accidents with major injuries. Effective strategies and interventions should focus on strengthening the enforcement of traffic risk behaviors and encouraging food delivery companies to establish regulations relating to the workers’ concerns.

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. [ABSTRACT FROM AUTHOR]

It is necessary to analyze pedestrian behavior at crossings to improve their safety and mobility. Mid-block pedestrian crossings are structures that facilitate the mobility of pedestrians, safeguarding them from vehicular traffic; however, illegal crossing by pedestrians is an everyday occurrence and represents a risk to their safety. The purpose of this study is to evaluate the relationship between different human and road factors and the decision to illegally cross signalized mid-block crossings. Several human factors such as age, gender, waiting time in traffic light, use of the pushbutton and individual or group crossing, as well as road characteristics such as the length of traffic light phase, length of crossing, and vehicular volume were analyzed. To collect information about these variables this study recorded a one-hour video in six selected crosswalks within the Montes de Oca County in Costa Rica. A total of 1,707 crossings were recorded, 10.6% of which corresponded to instances of illegal crossing. After applying a logit model, this research found that traffic volume, pedestrian red-light time, waiting time, vehicle illegal crossing and group crossings reduced the probability of violations by pedestrians. On the other hand, minimum traffic light time and crossing length increased the possibility of pedestrian illegal crossings. This study concluded that the traffic light cycle is an important variable that must be rigorously analyzed to ensure pedestrian's compliance with traffic lights, which will improve the safety of the pedestrian mid-block crossings.

• As motorcyclists' intentions increase, their rate of running a red light increase. • As motorcyclists' willingness increase, their rate of running a red light increase. • The power of PWM in predicting the probability of RLR by riders is almost as same as TPB. 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. [ABSTRACT FROM AUTHOR]

The purpose of this article was to investigate driver's speed behaviors during the yellow-light period and how satisfied they were at the intersections under the Red-Light Cameras (RLC) project. The survey data was collected before the project implementation and 3 months after the project began. The data was analyzed by calculating the correlation between the distances from the intersection and the drivers’ speeds entering the intersections during the yellow signal. In addition, the satisfaction was evaluated with 400 samples based on the questionnaire conducted when the RLC project was ongoing. The results showed that drivers speeded up significantly during the yellow signal when they were approaching the stop line, both at the intersections under the project and at the other intersection. Besides, it was found that the speed during the yellow light reduced significantly at all distances, i.e, 14-40% (6.5-22 km/hr.), resulting in 20-meter reduction of the maximum length of movement during the yellow signal period, compared to an unchanged distance at the control intersection. The analytical results corresponded to the 56% reduction of red-light runners after RLC project began. As for the drivers’ satisfaction towards the RLC project, it was found that the majority (64%) showed positive satisfaction, and that 67% reported that the project resulted in speed reduction. In addition, some variables were found to significantly correlate with the attitudes towards RLC project. The study's findings can be used as references, and the approach can be applied in other locations to prevent traffic collisions at intersections.

Red light running at signalised intersections is a growing road safety issue worldwide, leading to the rapid development of advanced intelligent transportation technologies and countermeasures. However, existing studies have yet to summarise and present the effect of these technology-based innovations in improving safety. This paper represents a comprehensive review of red-light running behaviour prediction methodologies and technology-based countermeasures. Specifically, the major focus of this study is to provide a comprehensive review on two streams of literature targeting red-light running and stop-and-go behaviour at signalised intersection – (1) studies focusing on modelling and predicting the red-light running and stop-and-go related driver behaviour and (2) studies focusing on the effectiveness of different technology-based countermeasures which combat such unsafe behaviour. The study provides a systematic guide to assist researchers and stakeholders in understanding how to best identify red-light running and stop-and-go associated driving behaviour and subsequently implement countermeasures to combat such risky behaviour and improve the associated safety.

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 2168driver 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, and7% of the drivers committed RLR violations. The results showed that drivers were morelikely to stop before the stop line through the yellow phase at locations with RLR cameras, green light flash devices, pavement markings, where pedestrianswere 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.

The purpose of this article was to investigate driver's speed behaviors during the yellow-light period and how satisfied they were at the intersections under the Red-Light Cameras (RLC) project. The survey data was collected before the project implementation and 3 months after the project began. The data was analyzed by calculating the correlation between the distances from the intersection and the drivers' speeds entering the intersections during the yellow signal. In addition, the satisfaction was evaluated with 400 samples based on the questionnaire conducted when the RLC project was ongoing. The results showed that drivers speeded up significantly during the yellow signal when they were approaching the stop line, both at the intersections under the project and at the other intersection. Besides, it was found that the speed during the yellow light reduced significantly at all distances, i.e, 14-40% (6.5-22 km/hr.), resulting in 20-meter reduction of the maximum length of movement during the yellow signal period, compared to an unchanged distance at the control intersection. The analytical results corresponded to the 56% reduction of red-light runners after RLC project began. As for the drivers' satisfaction towards the RLC project, it was found that the majority (64%) showed positive satisfaction, and that 67% reported that the project resulted in speed reduction. In addition, some variables were found to significantly correlate with the attitudes towards RLC project. The study's findings can be used as references, and the approach can be applied in other locations to prevent traffic collisions at intersections. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dynamic All-Red Extension (DARE) has recently attracted research interest as a non-traditional intersection collision avoidance method. We propose a probabilistic model to predict red light running (RLR) hazard for dynamic all-red extension system. The RLR hazard, quantified by a predicted encroachment time, has contributory factors including the speed, distance and car-following status of the violator and the empirical distribution of the entry time of conflict traffic. An offline data analysis procedure is developed to set the parameters for RLR hazard prediction. Online-wise, a two-dimensional normal model is developed to predict the vehicle’s stop-go maneuver based on speeds at advanced detectors. Additionally, unlike most prediction models which are designed to minimize mean errors, our model identifies two types of errors, namely the false alarm and missed report. The capability of distinguishing these two types of errors is crucial to the effectiveness of dynamic systems. To quantify the trade-off between these two types of errors in the system design, a system operating characteristics (SOC) function is then defined. Performance of the proposed model and its prediction algorithm is evaluated using data collected from a field intersection. At a false alarm rate of 5%, the algorithm reach a correct detection rate of over 65% to over 90% for various legs of the test intersection. Performance evaluation results showed that the proposed models and algorithms within the DARE framework can effectively detect the RLR hazards.

The aim of the present study was to investigate the relationship between bicycle helmet use and safety behavior at signalized intersections. Two hypotheses were investigated: The first states that bicycle helmet use leads to risker behavior because of the increased sense of protection (risk compensation), the other states that helmeted cyclists have a general inclination toward safer behavior (safety package) and that helmet use is one of several behaviors for improving safety. Based on video recordings of 1031 cyclists at 12 signalized intersections in Denmark, two indicators of risky behavior were compared between helmeted and unhelmeted cyclists: Speed and time after the onset of yellow at which the cyclists crossed the stop line. Linear regression models were developed with gender, type of bicycle, and intersection characteristics as predictor variables, in addition to helmet use. Helmeted and unhelmeted cyclists do not differ in how many seconds after the onset of yellow they cross the stopping line. This is consistent with the absence of both risk compensation and safety package, alternatively with a general inclination of helmeted cyclists toward safer behavior which is about offset by risk compensation. Helmeted cyclists had higher speed on average, which indicates that risk compensation may occur. However, the higher speed may also be due to the generally better fitness of helmeted cyclists which is likely to result from larger cycling volumes. Moreover, the effect of helmet use on speed may be overestimated because of a lack of control for potential confounding variables. The results show further that, regardless of helmet use, before-red (lights on a separate bicycle signal shift to red before the main signal) is related to later crossings of the stop lane after the onset of yellow and that cyclists stop earlier on average at intersections with right-turn signals. The results do not provide support for the position that promoting or mandating bicycle helmet use will have adverse safety effects because of more risky behavior among helmeted cyclists. [ABSTRACT FROM AUTHOR]

Red-Light-Running (RLR) is the leading cause of urban crashes. This report presents the findings from an investigation of signal control-related countermeasures to reduce the occurrences of RLR and related collisions. The objectives of this study were to identify and evaluate potential modifications to signal timing schemes in reducing the frequency of RLR and to develop online adaptive RLR collision avoidance algorithms to reduce the possibility of RLR collisions. High quality, second-by-second vehicle count and signal timing and phasing data were collected at arterial intersections. A signal-cycle-based data analysis was performed to determine RLR contributing factors that are statistic significant, having substantial impact on RLR and controllable via signal timing modifications. These findings were then applied to develop signal timing schemes that aim to increase the probability of stopping and online signal timing adaptation strategies to avoid potential collision when RLR occurs. We demonstrate the effectiveness of signal timing modifications in reducing RLR while maintaining intersections’ level of service, under both microscopic simulation and macroscopic simulation environments. In addition, an investigation of drivers’ decision making at signalized intersections using real world data and a probabilistic RLR prediction model are presented.

• Eye movements are recorded during simulated red-light-running driving. • Collision avoidance process was divided into detection stage and reaction stage. • Effects of warning timings and warning content on visual performances were conducted. Intersections have been recognized as hazard locations with lots of visual information that drivers need to process. Although the collision avoidance systems (CASs) have been proved to effectively reduce the crash rate and much research on the effectiveness of CASs has been conducted with regard to the driving behaviors, drivers' visual performances under the effects of different collision avoidance warning conditions that were closely related to the effectiveness of CAS have been neglected. In this study, a driving simulator experiment was conducted to evaluate the relationships among drivers' visual performances, drivers' different warning conditions (warning timings × warning content) and driver's gender when they crossed the intersections involved with red-light running (RLR) vehicles. The experimental results showed that warning timings had significant effects on the detection stage and reaction stage. Specifically, drivers could detect the conflicting RLR vehicle most quickly in the warning timings of 4.5 s ahead of a collision. When the warning was released earlier than 5.0 s ahead of a collision, driver tended to take brake action earlier than paying a fixation on a conflicting RLR vehicle. Warning content only had significant effects on drivers' detection stage. Compared to the non-directional warning, the specific directional information could shorten the time spent in detecting the conflicting RLR vehicle. Besides, directional information could increase drivers' average blink duration during the process of collision avoidance. Additionally, the results showed that female drivers were more likely to be involved with RLR collisions, and male drivers could detect the conflicting RLR vehicle more quickly than female drivers. Also, it had been found that later warning timings tended to increase female drivers' blink rate, and non-directional warning tended to increase female drivers' blink rate. These findings could direct warning condition design to improve the effectiveness of collision avoidance systems. [ABSTRACT FROM AUTHOR]

Abstract Electric bicycle (EB) has been one popular traffic tool in urban traffic system, but it has some disorder motion behaviors at a signalized intersection. In this paper, we propose a strategy to control each EB’s motion at a signalized intersection, and design some rules to explore each EB’s motion and the impacts of the proposed strategy on each EB’s motion at the signalized intersection. The numerical results indicate that the proposed strategy can enhance the operational efficiency at the signalized intersection. The results can help administrator to better manage the EB’s motion at the signalized intersection. Highlights • A strategy for EB flow is proposed. • EB’s motion rules under the proposed strategy are designed. • The effects of the proposed strategy on each EB’s motion studied. [ABSTRACT FROM AUTHOR]

Recently, electric bicycle (EB) has been an important traffic tool in China. However, EB has some abnormal behaviors (e.g., retrograde, red-light running) in urban traffic system due to its own merits. Little effort has been made to explore the EB’s abnormal behaviors. In this paper, we propose a cellular automaton (CA) model to explore the effects of the EB’s abnormal behaviors on the operational efficiency at a signalized intersection. The numerical results indicate that the EB’s abnormal behaviors have prominent negative impacts on the operational efficiency at a signalized intersection (i.e., the two behaviors will reduce the operational efficiency) and that the impacts are dependent on the proportion of the EBs with the two abnormal behaviors. The results can help us better manage the EB’s behaviors (especially at a signalized intersection). [ABSTRACT FROM AUTHOR]

• We offer a method for estimating enforcement coverage across populations. • Automatic (as an objective benchmark) and manual ticketing were compared. • Data included tickets issued to Israelis for speeding and running red lights. • Manual under-enforcement was found for females and drivers with previous tickets. • Manual over-enforcement was found for younger drivers and truck drivers. Over the years empirical evidence has shown that traffic enforcement reduces traffic violations, crashes, and casualties. However, less attention has been paid to enforcement coverage across different populations and driver characteristics. The current study develops and explores a method for estimating police enforcement coverage, by comparing the share of drivers across several characteristics who received tickets from automatic speed and red-light cameras – as an objective estimate of offenses committed – to the share of drivers who received tickets through manual police enforcement. Using data from all speeding and red-light tickets issued to Israelis over a period of one and a half years, we found under-enforcement by police officers for female drivers, two-wheeled vehicle drivers (for speeding), and drivers with previous tickets. We found over-enforcement for younger drivers, truck drivers, and two-wheeled vehicle drivers (for red-light offenses). The findings suggest that the method developed in the research is able to identify groups of drivers who are over- or under-enforced. Police authorities can use this information to create evidence-based enforcement policies. [ABSTRACT FROM AUTHOR]

In this paper, we present a Smart In-Vehicle Decision Support System (SIV-DSS) to help making better stop/go decisions in the indecision zone as a vehicle is approaching a signalized intersection. Supported by the Vehicle-to-Infrastructure (V2I) communications, the system integrates and utilizes the information from both vehicle and intersection. The effective decision support models of SIV-DSS are realized with the probabilistic sequential decision making process with the capability of combining a variety of advantages gained from a set of decision rules, where each decision rule is responsible to specific situations for making right decisions even without complete information. The decision rules are either extracted from the existing parametric models of the indecision zone problem, or designed as novel ones based on physical models utilizing the integrated information containing the key inputs from vehicle motion, vehicle-driver characteristics, intersection geometry and topology, signal phase and timings, and the definitions of red-light running (RLR). In SIV-DSS, the generality is reached through physical models utilizing a large number of accurate physical parameters, and the heterogeneity is treated by including a few behavioral parameters in driver characteristics. The performance of SIV-DSS is evaluated with systematic simulation experiments. The results show that the system can not only ensure traffic safety by greatly reducing the RLR probability, but also improve mobility by significantly reducing unnecessary stops at the intersection. Finally, we briefly discuss some relevant aspects and implications for SIV-DSS in practical implementations. [ABSTRACT FROM AUTHOR]

The collision avoidance warning system is an emerging technology designed to assist drivers in avoiding red-light running (RLR) collisions at intersections. The aim of this paper is to evaluate the effect of auditory warning information on collision avoidance behaviors in the RLR pre-crash scenarios and further to examine the casual relationships among the relevant factors. A driving-simulator-based experiment was designed and conducted with 50 participants. The data from the experiments were analyzed by approaches of ANOVA and structural equation modeling (SEM). The collisions avoidance related variables were measured in terms of brake reaction time (BRT), maximum deceleration and lane deviation in this study. It was found that the collision avoidance warning system can result in smaller collision rates compared to the without-warning condition and lead to shorter reaction times, larger maximum deceleration and less lane deviation. Furthermore, the SEM analysis illustrate that the audio warning information in fact has both direct and indirect effect on occurrence of collisions, and the indirect effect plays a more important role on collision avoidance than the direct effect. Essentially, the auditory warning information can assist drivers in detecting the RLR vehicles in a timely manner, thus providing drivers more adequate time and space to decelerate to avoid collisions with the conflicting vehicles.

Pedestrians’ Red-light running behavior is one of the most critical factors for pedestrian involved traffic crashes at intersections in China. The primary objective of this study is to explore how various factors affect pedestrians’ red-light running behaviors at intersection areas, using the data collected from Hefei, China. A questionnaire was well designed aiming at collecting pedestrians’ socio-economic characteristics, trip related features, and attribute variables in different crossing facilities. Based on 631 valid samples, a binomial logistic model was established to evaluate the impacts of contributing factors on pedestrians’ red-light running behavior. The modeling results show that four variables significantly affect the probability of pedestrians’ red-light running behavior, which are the trip purpose, time period in one day, pedestrian’s attitude towards whether to run a red light when in hurry, and pedestrian’s attitude towards whether quality of road facility affects crossing behavior. With those variables, the probability of pedestrians’ red-light running behavior at intersections could be predicted. Findings of this study can help understand why pedestrians in China run red-lights and identify which pedestrian groups and intersections are more likely to have such behaviors. This study can also help propose countermeasures more efficiently to reduce pedestrian-related crashes at intersections in China. [ABSTRACT FROM AUTHOR]

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 ( T T I ), distance to intersection ( D T I ), 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. T T I , D T I , the required deceleration parameter ( R D P ), 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 2–6 m contributed to the best model performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There is usually a high red-light running (RLR) collision rate at signalized intersections because of the unpredictability of dangerous RLR vehicles traversing intersections illegally. The RLR collision warning systems can help drivers be aware of the red-light running (RLR) vehicles and take collision avoidance actions timely. The objective of this paper is to evaluate the effects of auditory warning information on brake response time (RT) to red-light running vehicles during the collision avoidance process based on a driving simulator experiment. Warning status (e.g., warning vs. no warning), warning lead time (3 s vs. 5 s) and warning content (directional information vs. undirectional information) were considered to examine the effectiveness of the RLR collision warning system under different conditions. The experiment results showed that the auditory warning information can effectively reduce brake RT and collision occurrence rate and RT is the most influential variable on the collision occurrence. Although early warning is more effective than late warning in reducing brake RT and collision rate, the late warning can still lower the collision rate by 37%. However, the directional warning information did not have apparent advantages over the undirectional warning information. The findings of this study are helpful for the designers of RLR collision warning systems to enhance the effectiveness of the systems’ application on traffic safety. [ABSTRACT FROM AUTHOR]

A study to analyze the speed profile of individual vehicles and the drivers' decisions to stop or go at the onset of the yellow signal was performed. Events were recorded continuously from 480 feet (146 m) until the stop bar using video cameras. Based on the time to stop line data at the yellow onset, it was observed, that in addition to the group of "normal drivers" who stop or cross the intersection in a reasonable time after the yellow onset, there are two groups of drivers: aggressive drivers who decide to accelerate even though the yellow phase had ended and conservative drivers stopped although they could have passed the intersection during the 3.0 seconds assigned for the yellow period. It is observed that most of the vehicles that stopped took 5.0 seconds or more to reach the intersection and most of the passing vehicles took 4.0 seconds or less to cross the intersection. For passing vehicles at the intersection, the results of the yellow and red entry time of 3.00 and 2.46 seconds, respectively, show a high degree of driver aggressiveness at the intersection. A dilemma zone (DZ) model calibrated from the data showed values of driver's perception-reaction time, and maximum acceleration and deceleration rates consistent with the theoretical values recommended for the design of a signal timing plan. The proposed technique can be extended to other intersections with similar characteristics using the parameters suggested in this study. A practical manner to avoid involuntary RLR events is proposed. [ABSTRACT FROM AUTHOR]

This paper presents a hazard-based duration approach to investigate riders’ waiting times, violation hazards, associated risk factors, and their differences between cyclists and electric bike riders at signalized intersections. A total of 2322 two-wheeled riders approaching the intersections during red light periods were observed in Beijing, China. The data were classified into censored and uncensored data to distinguish between safe crossing and red-light running behavior. The results indicated that the red-light crossing behavior of most riders was dependent on waiting time. They were inclined to terminate waiting behavior and run against the traffic light with the increase of waiting duration. Over half of the observed riders cannot endure 49 s or longer. 25% of the riders can endure 97 s or longer. Rider type, gender, waiting position, conformity tendency and crossing traffic volume were identified to have significant effects on riders’ waiting times and violation hazards. Electric bike riders were found to be more sensitive to the external risk factors such as other riders’ crossing behavior and crossing traffic volume than cyclists. Moreover, unobserved heterogeneity was examined in the proposed models. The finding of this paper can explain when and why cyclists and electric bike riders run against the red light at intersections. The results of this paper are useful for traffic design and management agencies to implement strategies to enhance the safety of riders. [ABSTRACT FROM AUTHOR]

The object of this paper is to analyze and to suggest improvements on intersection management systems using existing equipment currently available in street intersections. In order to do this, we designed an intersection management system capable of detecting traffic violations as well as vehicle collisions using high-speed cameras. We propose to implement the above mentioned system through design and simulation in the LabVIEW programming environment. LabVIEW offers all the instrumentation necessary to accurately simulate the phenomena that occur in modern-day intersections, making it possible to further develop the existing intersection technology. It also makes it possible to determine the requirements and limitations of the existing equipment and to find the best solutions for every scenario. [ABSTRACT FROM AUTHOR]

This paper validates traffic safety climate attitudes based on a representative sample of road users of all travel modes. We use the German version of the Traffic Climate Scale (TCS) which was applied in a large-scale road safety survey in 2010. A total of 1680 people were surveyed. The sample is representative for socio-demographic characteristics and travel mode choice in Germany. Factor analysis reveals a three-factor structure of traffic safety climate with the factor ‘External affective demands’ describing emotional engagement in traffic, the factor ‘Internal requirements’ representing individual skills and abilities to successfully participate in traffic, and the factor ‘Functionality’ describing requirements for a functional traffic system. The less emotionally demanding and the more functional traffic is perceived to be, the safer people feel in traffic. External affective demands are consistently related to the perception of others’ driving/riding style but not to one’s own, whereas internal requirements are consistently related to one’s own driving/riding style but not to the perception of others. There is no relation between traffic safety climate and accidents or near accidents. Contrary to our expectations, a positive traffic safety climate is associated with more secondary tasks while driving and traffic violations. Behavioural control beliefs may mediate the traffic climate–traffic behaviour relationship. The results are discussed with reference to attitude research and the theory of planned behaviour in particular. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: About 20years ago, concern was raised about the dangers that children face when driven by drinking drivers in the United States. During the last decade, the pace of research on this topic subsided. Yet in 2010, every day three children younger than age 15 were killed, and 469 were injured in motor-vehicle crashes. Method: The aim of this effort is to describe the status of the problem in the United States and suggest lines of research. From the Fatality Analysis Reporting System (FARS), we selected crashes in which a driver aged 21 or older was driving at least one child younger than age 15. We identified crashes that occurred at different times of the day in which the driver was speeding, ran a red light, or was alcohol positive. We described the drivers' demographics and examined how they relate to the different crash types. Results: We found that, although driving a child seems to protect against the studied forms of risky driving, such protection varies sharply depending upon the drivers' and children's demographics and the crash type. There is no clear reason to explain the drivers' decision to endanger the children that they drive. The percent of children killed in speeding-related and red-light running motor-vehicle crashes has remained relatively stable during the last decade. Future research must (a) examine the effectiveness of current child endangerment laws; (b) examine crashes other than fatal; and (c) be more targeted, looking at specific drivers' age and gender, specific children's ages, the time of the crash, and the type of crash. Practical applications: Significant attention needs to be given towards improving state laws on child endangerment. Policymakers’ reaction to this problem is tentative because of our limited understanding of the problem; therefore, further research is needed. With unfocused countermeasures and prevention efforts, we have been restricted in our ability to evaluate these responses. The findings of this report should be informative to policy makers. [Copyright &y& Elsevier]

Highlights: [•] This study evaluates the impact of Winnipeg's photo enforcement safety program on speeding and red-light running. [•] ARIMA time series analyses are used to analyze monthly crash counts from 1994 to 2008. [•] A quasi-experimental intersection camera experiment was conducted using observational roadside data. [•] Overall, Winnipeg's photo enforcement safety program had a positive net effect on traffic safety. [•] The protective effect of photo enforcement is not equally pronounced across different conditions. [Copyright &y& Elsevier]

Abstract: Automated enforcement red-light cameras (RLC) have been widely adopted by municipalities around the world as a measure of curbing red-light running (RLR) at signalized intersections and reducing the cost of law enforcement. While a consensus has not yet been reached about whether RLC in general can benefit intersection safety by reducing RLR and crashes, recent debates revolve around using RLC as a revenue generator. Some of the political backlash of RLC is the perception that they are installed primarily to fulfill revenue guarantees and sustain the RLC program. Some municipalities have been charged with changing the signal phasing to trap more red-light runners and increase the revenue from RLC programs. This paper focuses on a number of engineering strategies, mainly related to signal timing that may be used by municipalities to achieve their financial goals. The negative impacts of implementing these measures on the safety and efficiency of intersection operations and public support on RLC programs are also discussed. These strategies are also revealed to increase transparency of the divergent motivations of RLC vendors, municipalities, policy makers and safety advocates. [Copyright &y& Elsevier]

Abstract: Electric bikes and regular bicycles play an important role in the urban transportation system of China. Red-light running is a type of highly dangerous behavior of two-wheeled riders. The main purpose of this study was to investigate the rate, associated factors, and behavior characteristics of two-wheelers’ red-light running in China. A field observational study was conducted using two synchronized video cameras at three signalized intersections in Beijing. A total of 451 two-wheelers facing a red light (222 e-bike riders and 229 cyclists) were observed and analyzed. The results showed that 56% of the two-wheelers crossed the intersection against a red light. Age was found to be a significant variable for predicting red-light runners, with the young and middle-aged riders being more likely than the old ones to run against a red light. The logistic regression analysis also indicated that the probability of a rider running a red light was higher when she or he was alone, when there were fewer riders waiting, and when there were riders already crossing on red. Further analysis of crossing behavior revealed that the majority of red-light running occurred in the early and late stages of a red-light cycle. Two-wheelers’ crossing behavior was categorized into three distinct types: law-obeying (44%), risk-taking (31%) and opportunistic (25%). Males were more likely to act in a risk-taking manner than females, and so were the young and middle-aged riders than the old ones. These findings provide valuable insights in understanding two-wheelers’ red-light running behaviors, and their implications in improving road safety were discussed. [Copyright &y& Elsevier]

Abstract: When the driver encounters a signal change from green to yellow, he is required to make a stop or go decision based on his speed and the distance to the stop bar making the wrong decision will lead to a red-light running violation or an abrupt stop at the intersection. In this study, a field data collection was conducted at a high-speed signalized intersection, where a video-based system with three cameras was used to record the drivers’ behavior related to the onset of yellow. Observed data include drivers’ stop/go decisions, red-light running violation, lane position in the highway, positions (leading/following) in the traffic flow, vehicle type, and vehicles’ yellow-onset speeds and distances from the intersection. Further, classification tree models were applied to analyze how the probabilities of a stop or go decision and of red-light running are associated with the traffic parameters. The data analysis indicated that vehicle''s distance from the intersection at the onset of yellow, operating speed, and position in the traffic flow are the most important predictors for both the stop/go decision and red-light running violation. This study illustrates that the tree models are helpful to recognize and predict how drivers make stop/go decisions and partake in red-light running violations corresponding to the traffic parameters. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

The traditional way to evaluate pedestrian safety is a reactive approach using the data at an aggregate level. The objective of this study is to develop real-time safety models for pedestrian red-light running using the signal cycle level traffic data. Traffic data for 464 signal cycles during 16 h were collected at eight crosswalks on two intersections in the city of Nanjing, China. Various real-time safety models of pedestrian red-light running were developed based on the different combination of explanatory variables using the Bayesian Poisson-lognormal (PLN) model. The Bayesian estimation approach based on Markov chain Monte Carlo simulation is utilized for the real-time safety models estimates. The models' comparison results show that the model incorporated exposure, pedestrians' characteristics and crossing maneuver, and traffic control and crosswalk design outperforms the model incorporated exposure and the model incorporated exposure, pedestrians' characteristics, and crossing maneuver. The result indicates that including more variables in the real-time safety model could improve the model fit. The model estimation results show that pedestrian volume, ratio of males, ratio of pedestrians on phone talking, pedestrian waiting time, green ratio, signal type, and length of crosswalk are statistically significantly associated with the pedestrians' red-light running. The findings from this study could be useful in real-time pedestrian safety evaluation as well as in crosswalk design and pedestrian signal optimization. [ABSTRACT FROM AUTHOR]

• The theory of planned behavior was used to investigate the behavior of delivery riders running red lights. • The structural equation model was used to predict delivery riders running red lights. • Attitude, conformity tendency and the traffic environment were significant predictors in the modified TPB model. • The results of multiple group analysis show that differences between the means of psychological variables of different population groups. • Some intervention measures are obtained by analyzing the influencing factors of running red lights. Delivery riders, an occupation that has emerged from China's booming E-commerce industry, have attracted widespread attention due to their red-light running (RLR) and high accident rates. This study aimed to utilize the theory of planned behavior (TPB) to investigate the psychological characteristics of delivery riders' RLR intentions. A survey questionnaire was designed to collect data, including information regarding the extended variables, the basic components of the TPB and demographic characteristics. The survey was conducted in Xi'an, and 228 complete questionnaires were collected. Structural equation modeling was used to examine the data, and a multiple group analysis of the demographic variables was conducted. The results showed that the expanded TPB model had a better model fit and higher variance explanation than the original TPB model. Extended constructs, i.e., conformity tendency (CT) and the traffic environment (TE), were significant predictors, and attitude was the strongest predictor of all the examined variables related to RLR intentions. Finally, the path parameters of the expended TPB model were adapted for different demographic groups, and some differential effects were also found. These results could provide a basis for the design of intervention measures and safety education schemes by delivery platforms and traffic management departments to reduce RLR behavior among delivery riders. [ABSTRACT FROM AUTHOR]

In this paper, a collision avoidance system is presented to detect red light running and warn nearby vehicles and pedestrians in real time in order to prevent possible accidents. No complex infrastructure-based solution such as those based on radars or cameras is here required. Instead, a new solution based on smartphones carried by drivers and pedestrians is proposed so that it is the device inside the vehicle violating a traffic light, the one that self-reports the offence in order to generate alerts and warn nearby vehicles and pedestrians to prevent accidents. The proposal could also be used by road authorities to collect data on traffic lights that are most frequently violated in order to define an action plan to investigate causes and look for solutions. It includes a classifier for learning and estimating driver behaviour based on collected data, which is used to predict whether he/she is about to run a red light or detect whether that has already happened. In the first case, the system broadcasts warnings directly to close vehicles and pedestrians through Wi-Fi, while in the second case, the proposal warns vehicles and pedestrians in the neighbourhood through a server. The solution also includes a prioritization system based on changing traffic lights at intersections according to the needs and characteristics of the traffic at all times, giving the top priority to emergency vehicles. Furthermore, the proposal involves the use of cryptographic schemes to protect authenticity and integrity of messages sent from traffic lights, smartphones and servers, and privacy and anonymity to promote the use of the system. A beta version with some parts of the proposal has been implemented and the obtained results are promising. [ABSTRACT FROM AUTHOR]