The role of off-path glances: A quantitative analysis of rear-end conflicts involving Chinese professional truck drivers as the striking partners

Introduction: Rear-end crashes are one of the most frequent crash types in China, leading to significant economic and societal losses. The development of active safety systems – such as Automatic Emergency Braking System (AEBS) – could avoid or mitigate the consequences of these crashes in Chinese traffic situations. However, a clear understanding of the crash causation mechanisms is necessary for the design of these systems. Method: Manually coded variables were extracted from a naturalistic driving study conducted with commercial vehicles in Shanghai. Quantitative analyses of rear-end crashes and near crashes (CNC) were conducted to assess the prevalence, duration, and location of drivers’ off-path glances, the influence of lead vehicle brake lights on drivers’ last off-path glance, and driver brake onset, and the influence of off-path glances and kinematic criticality on drivers’ response to conflicts. Results: The results indicate that the Chinese truck drivers in our study rarely engage in distracting activities involving a phone or other handheld objects while driving. Instead, they direct their off-path glances mainly toward the mirrors, and the duration of off-path glances leading to critical situations are shorter compared to earlier analyses performed in Western countries. The drivers also often keep small margins. Conclusions: Overall, the combination of short time headway with off-path glances directed toward the mirror originates visual mismatches which, associated to a rapid change in the kinematic situation, cause the occurrence of rear-end CNC. When drivers look back toward the road after an off-path glance, a fast response seems to be triggered by lower values of looming compared to previous studies, possibly because of the short time headways. Practical Application: The results have practical implications for the development of driver models, for the design of active safety systems and automated driving, and for the design of campaigns promoting safe driving.     

Using SHRP2 naturalistic driving data to examine driver speeding behavior

Problem: Speeding-related crashes continue to be a serious problem in the United States. According to the National Highway Traffic Safety Administration, 26% of all fatal crashes in 2017 had speeding as a contributing factor. Method: Vehicle speed data recorded during the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study were analyzed to identify the frequency of speeding episodes. Up to 100 trips were sampled from 2,910 individual drivers aged 16–64. Vehicle speed data from individual trips were parsed into continuous speeding episodes (SEs) and Free-Flow Episodes (FFEs), which approximated opportunities to speed. Results & Discussion: Driving 10 mph above the posted speed limit (PSL) was common, and 99.8% of drivers had at least one occurrence SE within their trip sample, yielding an average of 2.75 SEs per trip (623,202 SEs in total). The analysis focused on a subset of higher-speed SEs in which the vehicle reached speeds of at least 15 mph above the PSL during the SE (71,113 SEs in total). Average maximum speeds for most higher-speed SEs ranged between 12 mph to 15 mph above the PSL, and most also lasted less than 2 min. Most drivers spent less than 5% of the FFE time speeding, and only a small number of drivers spent more than 10% of the time speeding. There was also a clear trend towards the younger group having higher overall percentages of SE time relative to FFE time. Practical Applications: The methods and measures developed in this study provide the foundation for future analyses to determine if there are different types of speeding that vary in terms of risky characteristics, and further, if certain drivers are more likely to engage in riskier speeding behavior. Identifying higher-risk speeders is an important step for developing countermeasures and strategies targeting drivers that are at greatest risk of speed-related crashes.     

Effects of environment, vehicle and driver characteristics on risky driving behavior at work zones

This study aims to analyze the effects of environment, vehicle and driver characteristics on the risky driving behavior at work zones. A decision tree is developed using the classification and regression tree (CART) algorithm to graphically display the relationship between the risky driving behavior and its influencing factors. This approach could avoid the inherent problems occurred in the conventional logistic regression models and further improve the model prediction accuracy. Based on the Michigan M-94/I-94/I-94BL/I-94BR highway work zone driving behavior data, the decision tree comprising 33 leaf nodes is built. Bad weather, poor road and light conditions, partial/no access control, no traffic control devices, turning left/right and driving in an old vehicle are found to be associated with the risky driving behavior at work zones. The middle-aged drivers, who are going straight ahead in their vehicles with medium service time and equipped with an airbag system, are more likely to take risky behavior at lower work zone speed limits. Further, the middle-aged male drivers engage in risky driving behavior more frequently than the middle-aged female drivers. The number of lanes exhibits opposing effects on risky behavior under different traveling conditions. More specifically, the risky driving behavior is associated with the single-lane road under bad light or weather conditions while drivers are more likely to engage in risky behavior on the multi-lane road under good light conditions.     

Effect prediction of time-gaps for adaptive cruise control (ACC) and in-vehicle tasks on bus driver performance

Researchers have devoted a great deal of attention to the effects of driver assist systems on driver performance. This article describes a modeling approach to simulate the effects of time-gaps for adaptive cruise control (ACC) and manual in-vehicle tasks on bus-driver performance. A concept model was built with the knowledge of modularization, parameterization, and parallel processing. By running the model, the predictions for the effects of five levels of time-gaps and two types of in-vehicle tasks were collected in three measures: (1) mean gap, (2) minimum gap, and (3) collision rate. The model performed well in prediction, especially when driving with in-vehicle tasks. Predictions from the model were validated by the experiment with a verified fixed-base bus-driving simulator, used in the authors’ previous studies. Throughout the modeling approach, this research provides a theoretical and accurate way to assess effects of time-gaps and vehicle-equipped interfaces. In follow-up research, the authors will apply this approach to evaluate other driving assist systems (e.g. collision warning systems and navigation systems) to create a customized software kit.     

Modeling drivers’ reaction when being tailgated: A Random Forests Method

Background: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles’ reaction using existing naturalistic driving data. Method: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle’s reaction based on corresponding factors including driver, vehicle, and environmental variables. Results: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers’ reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.     

Assessing the safety criticality of driver behavior toward cyclists at intersections

Abstract

Objective: Detailed analyses of car-to-cyclist accidents show that drivers intending to turn right at T-junctions collide more often with cyclists crossing from the right side on the bicycle lane than drivers intending to turn left. This fact has led to numerous studies examining the behavior of drivers turning left and right. However, the most essential question still has not been sufficiently answered: is the behavior of drivers intending to turn right generally more safety critical than the behavior of those intending to turn left? The purpose of this article is to provide a method that allows to determine whether a driver’s behavior toward cyclists can retrospectively be assessed as critical or non-critical.

Methods: Several theoretical considerations enriched by findings of experimental studies were employed to devise a multi-measure method. This method was applied to a dataset containing real-world approaching behavior of 48 drivers turning right and left at four T-junctions with different sight obstructions. For each driver a behavior-specific criticality was defined based on both, their driving and gaze behavior. Moreover, based on the behavior-specific criticality of each driver, the required field of view to see a cyclist from the right was defined and was set into relation with the available field of view of the T-junction.

Results: The results show that only a small portion of the drivers within the dataset would have posed an actual risk to cyclists crossing from the right side. Those situations with a higher safety criticality did not only arise when drivers intended to turn right, but also left.

Conclusion: Therefore, the analysis can only provide an explanation for the higher proportion of accidents between drivers turning right and cyclists crossing from the right side in certain situations. Further research, for example analyses of exposure data regarding the frequency of turning manoeuvers at T-junctions, is needed in order to explain the higher proportion of accidents between drivers turning right and cyclists crossing from the right side.     

Enhancing older driver safety: A driving survey and evaluation of the CarFit program

Objective

To evaluate CarFit, an educational program designed to promote optimal alignment of driver with vehicle.

Methods

A driving activity survey was sent to 727 randomly selected participants living in retirement communities. Drivers (n = 195) were assigned randomly to CarFit intervention (n = 83, M age = 78.1) or Comparison (n = 112, M age = 79.6) groups. After 6 months, participants completed a post-test of driving activity and CarFit recommendations.

Results

Nonconsenting drivers were older and participated in fewer driving activities. CarFit participation was moderate (71%) with 86% of the participants receiving recommendations. 60% followed the recommendations at the 6-month re-evaluation). The CarFit (67.6%) and Comparison (59.3%) groups reported at least one type of self-regulation of driving activity at baseline. There was no significant change in the driving behaviors at the six-month follow-up.

Conclusion

CarFit was able to detect addressable opportunities that may contribute to the safety of older drivers.

Impact on industry

CarFit recommendations may need stronger reinforcement in order to be enacted by a participant.     

Associations between driver training, determinants of risky driving behaviour and crash involvement

The core aim of the study is to examine associations between formal and informal practical driver training as well as driving experience on the one hand and young drivers’ safety attitudes, self-assessment of driving ability and self-reported driver behaviour on the other hand. An additional aim is to examine the associations between attitudes, self-assessment and behaviour on the one hand and crash involvement on the other hand. The results are based on a self-completion questionnaire survey conducted among a representative sample of Norwegian drivers aged 18–20 years (n = 1419). The results showed that there were small yet significant associations between driver training, on the one hand and traffic safety attitudes and risky driving behaviour on the other hand. The amount of formal driver training was negatively associated with the respondents’ evaluation of their driving skills; although the amount of lay instruction was positively associated with such self-evaluation. The results also showed that attitudes as well as self-assessment of driving ability were significantly associated with self-reported risk behaviour. This was especially true for attitudes related to rule violations. There was a strong association between crash involvement and exposure (measured as months holding a licence). Young novice drivers’ crash involvement seems stronger associated with driving skills (manifested as self-assessment of driving ability) than safety attitudes and self-reported driver behaviour. The consequences of the results for driver training and accident prevention are discussed.     

Examining the relationship between driver distraction and driving errors: A discussion of theory, studies and methods

Over the past two decades the concept of driver distraction has been the focus of intense research attention. One aspect of distraction for which there has been limited systematic research, however, is its role in driver error causation. This article presents a review of the distraction literature with a view to elucidating what is currently known about the types of driving errors that distraction contributes to and the mechanisms by which distraction induces these errors. The review revealed a number of fundamental gaps in our knowledge, including the number and nature of errors made by drivers when distracted; the mechanisms by which distraction causes errors; whether and how distraction disrupts drivers’ ability to recover from errors; and how system-wide factors moderate the relationship between distraction and error. In closing, we attempt to identify the most appropriate theoretical and methodological approach to drive the integrated study of distraction and error forward. We conclude that it is only through the adoption of a systems approach that integrated countermeasures can be proposed and implemented to mitigate driver errors caused by distraction.     

Older driver training programs: A systematic review of evidence aimed at improving behind-the-wheel performance

IntroductionAge- and health-related changes, alongside declines in driving confidence and on-road exposure, have been implicated in crashes involving older drivers. Interventions aimed at improving behind-the-wheel behavior are diverse and their associated impact remains unclear. This systematic review examined evidence on older driver training with respect to (1) road safety knowledge; (2) self-perceived changes in driving abilities; and (3) behind-the-wheel performance. Method Nine databases were searched for English-language articles describing randomized controlled trials (RCTs) and non-RCTs of driver training interventions aimed at those aged 55+ who did not have medical or other impairments that precluded licensure. Quality appraisals were conducted using Cochrane’s Risk of Bias Tool (RoB) and Risk Of Bias In Non-randomized Studies – of Interventions tool (ROBINS – I). [PROSPERO; registration no. CRD42018087366]. Results Twenty-five RCTs and eight non-RCTs met the inclusion criteria. Interventions varied in their design and delivery where classroom-based education, or a combination of classroom-based education with on-road feedback, improved road safety knowledge. Training tailored to individual participants was found to improve self-perceived and behind-the-wheel outcomes, including crashes. Conclusions Interventions comprised of tailored training can improve knowledge of road safety, changes to self-perception of driving abilities, and improved behind-the-wheel performance of older drivers. Future research should compare modes of training delivery for this driver population to determine the optimal combinations of off- and/or on-road training. Practical applications Training programs aimed at older drivers should be supported by theory and research evidence. By conducting comparative trials with a sufficient sample size alongside well-defined outcomes that are designed in accordance with reporting guidelines, the most effective approaches for training older drivers will be identified.     

Market basket analysis of safety at active highway-railroad grade crossings

IntroductionData from the Federal Railroad Administration show high numbers of incidents at the approximately 210,446 highway-railroad grade crossings across the United States. One cause for this unsettling trend is the problem of drivers stopping within the dynamic envelope zone (DEZ) of the train while in queue. This research seeks to study DEZ stopping behavior at highway-railroad grade crossings by evaluating regulatory signage and further analyze variables that may affect this behavior. Method: A comparative safety analysis is undertaken to evaluate the effectiveness of the standard “Do Not Stop on Tracks” (R8-8) sign by using percentage change calculations and chi-squared tests. The study then conducts a market basket analysis (MBA) to extrapolate on these results and to identify underlying factors associated with observed driver behavior using variables influenced by visibility, perception, and maneuverability. Results: Rather low reductions in safety violations resulted from the R8-8 installation, including a 2.2% reduction in DEZ stopping behavior and only a slight 3.7% increase in compliance. The results of the MBA identified associations that affirmed assumptions about driver behavior, while other associations were not as direct but altogether helped broaden the understanding of interactions at grade crossings. Conclusions: This study concluded that the R8-8 had a positive but minimal effect on driver behavior at the grade crossings. The MBA successfully demonstrated its value by providing further insight on the safety analysis and by increasing the number of variables that can be analyzed simultaneously. The methodology offers the scientific community an innovative approach to analyzing driver behavior. Practical Applications: The results identified important variables for developing preventive measures, which will ultimately help reduce safety violations at grade crossings. The MBA can provide practical insight for railroad safety professionals and transportation engineers when determining problematic intersections and can be used to improve the education on grade crossing interactions.     

Safety and associated parameters influencing performance of rail road grade crossings: A critical review of state of the art

Introduction: Railroad grade crossings (RRGCs) have emerged as critical transportation infrastructures from the point of safety and operational aspects because two modes of transportation intermingle at the intersecting zone; the understanding of safety and traffic operation at RRGC is of prime concern while planning and designing this transportation facility. Method: In this context, this work tries to comprehend RRGC performance-related parameters from published literature and figure out critical gaps. An international synthesis on the identified potential parameters influencing the RRGC performance (i.e., safety, driver behavior, and operational impact) was carried out by critically reviewing the articles published worldwide. Furthermore, key findings, used variables, analysis methods, research gaps, and recommendations were studied. Results: The review revealed that many researchers had explored the driver behavior and safety aspect based on past crash data and violations prevailing at RRGC. However, little research has been done to evaluate the effect of highways' operational characteristics on the performance of RRGC. Moreover, limited investigation has been carried out to understand the dilemma of drivers and the proactive safety evaluation of pedestrians and non-motorized vehicles at RRGC. A total of seven critical research gaps concerning parameters are recognized, facilitating a clear agenda for further research pertaining to RRGC performance.     

A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

Objective: This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.

Method: Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.

Results: Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.

Conclusions: This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.     

A challenge to the assumed generalizability of prediction and countermeasure for risky driving: different factors predict different risky driving behaviors

In road safety, it may be debated whether all risky behaviors are sufficiently similar to be explained by similar factors. The often assumed generalizability of the factors that influence risky driving behaviors has been inadequately tested. Study 1 (N=116) examined the role of demographic, personality and attitudinal factors in the prediction of a range of risky driving behaviors, for young drivers. Results illustrated that different driving behaviors were predicted by different factors (e.g., speeding was predicted by authority--rebellion, while drink driving was predicted by sensation seeking and optimism bias). Study 2 (N=127) examined the generalizability of these results to the general driving population. Study 1 results did not generalize. Predictive factors remained behavior-specific, but different predictor-behavior relationships were observed in the community sample. Overall, results suggest that future research and practice should focus on a multi-factor framework for specific risky driving behaviors, rather than assuming generalizability across behaviors and driving populations.     

Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

Abstract

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human–machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle.

Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed.

The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary.

The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10?min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object.

Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05).

Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.     

The role of self-regulation in the context of driver distraction: A simulator study

Objective: There is considerable evidence for the negative effects of driver distraction on road safety. In many experimental studies, drivers have been primarily viewed as passive receivers of distraction. Thus, there is a lack of research on the mediating role of their self-regulatory behavior. The aim of the current study was to compare drivers' performance when engaged in a system-paced secondary task with a self-paced version of this task and how both differed from baseline driving performance without distraction.

Methods: Thirty-nine participants drove in a simulator while performing a secondary visual–manual task. One group of drivers had to work on this task in predefined situations under time pressure, whereas the other group was free to decide when to work on the secondary task (self-regulation group). Drivers' performance (e.g., lateral and longitudinal control, brake reaction times) was also compared with a baseline condition without any secondary task.

Results: For the system-paced secondary task, distraction was associated with high decrements in driving performance (especially in keeping the lateral position). No effects were found for the number of collisions, probably because of the lower driving speeds while distracted (compensatory behavior). For the self-regulation group, only small impairments in driving performance were found. Drivers engaged less in the secondary task during foreseeable demanding or critical driving situations.

Conclusions: Overall, drivers in the self-regulation group were able to anticipate the demands of different traffic situations and to adapt their engagement in the secondary task, so that only small impairments in driving performance occurred. Because in real traffic drivers are mostly free to decide when to engage in secondary tasks, it can be concluded that self-regulation should be considered in driver distraction research to ensure ecological validity.     

A national evaluation of the nighttime and passenger restriction components of graduated driver licensing

Introduction

The high crash rate of youthful novice drivers has been recognized for half a century. Over the last decade, graduated driver licensing (GDL) systems, which extend the period of supervised driving and limit the novice's exposure to higher-risk conditions (such as nighttime driving), have effectively reduced crash involvements of novice drivers.

Method

This study used data from the Fatality Analysis Reporting System (FARS) and the implementation dates of GDL laws in a state-by-year panel study to evaluate the effectiveness of two key elements of GDL laws: nighttime restrictions and passenger limitations.

Results

Nighttime restrictions were found to reduce 16- and 17-year-old driver involvements in nighttime fatal crashes by an estimated 10% and 16- and 17-year-old drinking drivers in nighttime fatal crashes by 13%. Passenger restrictions were found to reduce 16- and 17-year-old driver involvements in fatal crashes with teen passengers by an estimated 9%.

Conclusions

These results confirm the effectiveness of these provisions in GDL systems. Impact on Public Health. States without the nighttime or passenger restrictions in their GDL law should strongly consider adopting them.

Impact on Industry

The results of this study indicate that nighttime restrictions and passenger limitations are very important components of any GDL law.     

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Introduction: With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted. Method: Utilizing DI-related crash data collected from North Carolina for the period 2013–2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset. Results: The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

A study of stopping propensity at matured red light camera T-intersections

PROBLEM: This paper evaluates the stopping propensity of drivers at matured in-service red light camera (RLC) signalized T-intersections after a number of years of camera operation. METHOD: A special-purpose data logger working in conjunction with loop sensors was used to gather traffic parameters, vehicle stopping/crossing movements, and signal phases at camera (treatment) and noncamera (comparison) approaches. Logistic modeling was employed to determine the stopping/crossing decisions of civilian vehicle drivers in response to the onset of yellow (amber) with traffic and the associated situational and behavioral variables, including their interactions. SUMMARY: The propensity to stop at camera approaches was about 17 times more than at noncamera approaches. This finding strongly affirmed the positive effect of RLC in encouraging drivers to stop. The likelihood to stop was about 6.5 times higher for an approach that faces an opposing right-turn conflict immediately after the onset of red. Higher stopping propensity was also more prevalent during weekdays and in periods of low traffic volumes. IMPACT ON INDUSTRY: The findings provided an objective measure on the effectiveness of RLC that would be useful to evaluate intersections for camera installation as well as on the implementation and operational aspects of RLC program.