Driver Training Programs Research Articles

Risk Assessment for the Development of Emergency Scenarios for Tram Driver Training

(1) Background: This article presents research results from a project to develop a simulator and training program for tram drivers based on virtual reality tools. This article aims to present the research results on the risk assessment of adverse events to develop training scenarios for tram drivers. (2) Methods: The research methodology involved four steps, including the identification of adverse events based on interviews with experts, estimation of risk parameters, risk assessment using fuzzy logic, and risk evaluation to identify events recommended for the training program. (3) Results: Thirteen adverse events related to tram driving were identified and ranked according to the proposed classification. A risk assessment was then performed for the selected group of events using fuzzy logic models. The results made it possible to recommend situations that should be mapped in the virtual world as part of a tram driver training program. (4) Conclusions: The results made it possible to distinguish a group of events that should constitute the training area dedicated to tram drivers. Including the developed emergency scenarios in the training program allows employees to examine their reactions to stressful or dangerous situations and better prepare themselves for future duties.

Impacts of brightness contrast, road environment complexity, travel direction and judgement type on speed perception errors among older adult pedestrians' road-crossing decision-making.

This study aimed to explore how various factors affect older people's vehicle speed perception to enhance their road safety as pedestrians, focusing on the impact of their cognitive and perceptual abilities on road-crossing decisions. The study evaluated the effects of brightness contrast (high, medium and low), road complexity (high and low) and vehicle travel direction (same and opposite) on speed perception errors in simulated traffic settings. It involved 38 older participants who estimated the speed of a comparison vehicle under two judgement conditions. Findings showed a consistent underestimation of speed in all conditions. A repeated-measure ANOVA revealed that speed perception errors were significantly higher with low brightness contrast, in simpler road environments, with vehicles travelling in the same direction, and when using absolute judgements. These results have practical importance for public safety initiatives, traffic regulation and road design catering to older adults' perceptual needs. They also provide valuable insights for driver training programs for older adults, aimed at enhancing their understanding and management of perceptual biases.

Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications.

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.

How are sociodemographic differences contributing to red light violation behavior? the underlying role of gender, age, driving experience, and income

ABSTRACT This paper presents the results of the first driver behavior questionnaire (DBQ) study on analyzing Indian drivers’ aberrant behavior for red light violations (RLVs) at signalized junctions. An ordered probit model is developed using the drivers’ socio-demographic variables. The model results indicate that age has the strongest influence on drivers’ RLV behavior, followed by driving experience and level of education. The level of perceived RLV appears to increase with higher driving frequency and two-wheeler ownership. This research is also the first to analyze the aberrant behavior differences among male and female drivers concerning their demographics and driving experiences. It is unique to note that at a certain combination of various demographics (e.g., driving experience, education level, and daily trip frequency), the men and women are becoming equal in their aberrant behavior while committing RLVs. This finding is an interesting input for developing driver training and education programs.

ВЛИЯНИЕ ВОДИТЕЛЬСКОГО СТАЖА НА АВАРИЙНОСТЬ

The analysis of traffic accidents and driver training in different countries is considered. The main causes of traffic accidents are analyzed and governments’ measures to improve road safety are considered. Various driver training programs in different countries are considered and their effectiveness is revealed

Driving behavior assessment: A practical study and technique for detecting a driver's condition and driving style

New research confirms that driving mistakes account for almost 75 % of all traffic accidents. Recently, many researchers have studied the recognition of driving behavior, including aggressive, fatigued or drowsy driving. The principal aim of this work is to present a combination approach for evaluating driver behavior, focuses on the analysis of numerous driving-related parameters and the detection of driver fatigue and drowsiness. Experimental research is carried out using a monitoring unit which can be mounted in any vehicle fitted with CAN (Control Area Network) technology, and which monitors current driving actions. This work combines the analysis of driving parameters such as engine speed, vehicle speed, accelerator pedal position, steering wheel angle, engine noise intensity, fuel consumption and, finally, the quantity of exhaust gases generated to define whether driving is normal or aggressive. Analysis of the driver's face enables us to detect fatigue and know whether the driver is asleep on the basis of facial features. This proposed detection method achieved an average accuracy of 99.10 %. The information gathered for each driving style is recorded, in order to build up our own data and analyze it carefully. In-depth study of these parameters reveals the boundaries that define driving status and style. Experimental and simulation results confirm that the proposed system can effectively detect driving states and driver reactions, with the aim of guaranteeing a sufficient and acceptable level of safety. This study can be used to improve policies and design more robust driver training and education programs.

Analysis of factors influencing crash injury severities at highway–rail grade crossings accommodating for unobserved heterogeneity

Introduction: This research aims to identify and understand the risk factors associated with injury severities in accidents occurring at highway–rail grade crossings in the context of the developing country, Thailand.Method: The mixed logit model was employed by analyzing crash data over 10 years, from 2012 to 2022.Results: The analysis revealed a number of significant factors associated with severe or fatal crashes. These included accidents that occurred between midnight and 6 a.m., collisions involving pickup cars or heavy trucks, roads with a “no overtaking zone” sign, intersections classified as Type B1 (defined by the presence of only warning posts and horizontal crossing barriers), and intersections without adequate traffic control devices. In contrast, hazard markers on pavements and traverse rumble strips were positively correlated with property damage only crashes, where no injuries were reported.Discussion: This study provides insights into contributory factors to accidents at highway–rail grade crossing. Based on these key findings, the study recommends increasing nighttime visibility at railway grade crossings, developing targeted education and training programs for pickup car and truck drivers, installing hazard markers and traverse rumble strips, and considering physical barriers, such as bollards or delineators, to discourage overtaking maneuvers near the railway crossing.

Preparing drivers for the future: Evaluating the effects of training on drivers’ performance in an autonomous vehicle landscape

The rapid development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technology has raised concerns about the ability of ordinary drivers to safely interact with these systems without formal training or practice sessions. This study investigates the impact of training on drivers' ability to interact with ADAS functions, aiming to enhance road safety and prepare drivers for future autonomous driving. To do so the study compared novice and experienced drivers' performance across various driving training sessions separated by different time intervals. Using a simulation-based driving scenario, a diverse group of participants with different ages, genders, driving experience, and ADAS usage frequencies was recruited. Participants were instructed to drive a virtual subject vehicle (SV) multiple times with different time intervals and various ADAS functions being activated and/or deactivated. Data on steering wheel input, acceleration, braking, event triggering time, response time, and vehicle lateral position were collected and analysed. Participants' performance, measured in terms of accuracy, reaction time in interacting with ADAS functions, and vehicle control when they are interacting with those functions, was assessed after each training session. Results revealed significant improvements in accuracy, reaction time, and vehicle control for both novice and experienced drivers' performance following training. The results highlight the importance of appropriate training for drivers to safely operate ADAS and autonomous functions, providing valuable insights for policymakers, vehicle manufacturers, and driver education institutions. Furthermore, the findings emphasise the need for further investigations on the effectiveness of various training modalities and the potential benefits of integrating real-time feedback systems or Artificial Intelligence (AI)-assisted coaching. This study has important ramifications for the future design and implementation of driver training programs, as well as the development of policies and regulations to promote the safe use of ADAS and AV technology in the future.

Learning to drive with neurological conditions: profile of users of an adapted driver training program and cognitive factors associated with success

Purpose To describe the sociodemographic and cognitive profile of participants enrolled in an adapted driving program for individuals with neurological conditions, to explore the association between cognitive functioning and driving program outcome, and to describe driving habits after program completion. Methods This study combined retrospective chart review and cross-sectional data collection. Results The sample included 71 participants with neurological disorders (aged 15–56 years, M = 22.2 ± 8.6; 39% women). Driving program was either successful (47%), failed (7%), discontinued (34%), or ongoing (13%). Among 35 participants with complete neuropsychological and driving program outcome data, those who successfully completed the program showed better attention functioning, and better performance relative to global functioning for attention, executive functions, and working memory, compared to those who discontinued/failed the program. Among 21 participants who completed a telephone questionnaire on average 3.7 years after program enrollment, 67% obtained their driver’s license and drove regularly. Participants reported high levels of satisfaction with the program. Conclusion These results suggest that approximately half of the persons enrolled in a driver training program designed for learners with neurological conditions, obtain a driver’s license; and that attention, and to a lesser extent executive functioning and working memory, are related to driving program success. IMPLICATIONS FOR REHABILITATION In individual with neurological conditions, learning how to drive can be challenging. An adapted driver training program, involving collaboration between driving instructors and healthcare professionals, simplification of theoretical learning, and increasing driving practice opportunities, can be effective, both in terms of licensing success and client satisfaction. Conducting a pre-driving program neuropsychological assessment, with identification of cognitive strengths and weaknesses, can provide valuable information for clinicians and driving instructors for optimizing training and predicting outcome. Better performance in attention, and better relative to global cognitive functioning in attention, executive functions, and working memory, are related to higher success rate of an adapted driving program.

Mortality Rate and Years of Life Lost Due to Road Traffic Accidents in Fars Province, 2004-2019.

Traffic accidents are one of the most critical health problems and the ninth leading cause of death globally. We aimed to determine the Mortality rate and the number of Years of Life Lost (YLL) due to road traffic accidents. In this retrospective cohort study, mortality rate and YLL due to road traffic accidents were examined in Fars province, central Iran during the years 2004-2019. Mortality statistics were collected through death registration of ministry of health and medical education for Fars Province. Age Standardized mortality Rate (ASR) was calculated and join point regression analysis carried out to examine the trend of YLL rate. Data were analyzed using Excel spreadsheet version 2016 and Join point Regression Program 4.9.0.0. During the 16-year study period, 25,858 deaths due to road traffic accidents occurred in the province. 79.2% (20483 cases) were in men, and 33.7% (8703 cases) were aged 15-29 years. Total YLL during the 16-year study period were 458,975 (14.6 per 1000 people) in men, 117,999 (3.8 per 1000 people) in women. According to the join point regression, the 16- year trend of YLL rate due to premature mortality was decreasing: AAPC was -4.9% (95% CI: -8.8 to -0.9; P=0.018) for male, and -3.5% (95% CI: -6.3 to-0.5; P=0.011) for female. Considering that the number of deaths, mortality rate and YLL has decreased in Fars province during the 16 years under study. Therefore, because the mortality rate due to road traffic accidents in Iran is higher than the global average, the need for training programs for drivers, compliance with standards and retrofitting of vehicles, road safety, driving supervision and the use of seat belts are essential.

Beyond adaptive cruise control and lane centering control: drivers' mental model of and trust in emerging ADAS technologies.

The potential safety benefits of advanced driver assistance systems (ADAS) highly rely on drivers' appropriate mental models of and trust in ADAS. Current research mainly focused on drivers' mental model of adaptive cruise control (ACC) and lane centering control (LCC), but rarely investigated drivers' understanding of emerging driving automation functions beyond ACC and LCC. To address this research gap, 287 valid responses from ADAS users in the Chinese market, were collected in a survey study targeted toward state-of-the-art ADAS (e.g., autopilot in Tesla). Through cluster analysis, drivers were clustered into four groups based on their knowledge of traditional ACC and LCC functions, knowledge of functions beyond ACC and LCC, and knowledge of ADAS limitations. Predictors of driver grouping were analyzed, and we further modeled drivers' trust in ADAS. Drivers in general had weak knowledge of LCC functions and functions beyond ACC and LCC, and only 27 (9%) of respondents had a relatively strong mental model of ACC and LCC. At the same time, years of licensure, weekly driving distance, ADAS familiarity, driving style (i.e., planning), and personability (i.e., agreeableness) were associated with drivers' mental model of ADAS. Further, it was found that the mental model of ADAS, vehicle brand, and drivers' age, ADAS experience, driving style (i.e., focus), and personality (i.e., emotional stability) were significant predictors of drivers' trust in ADAS. These findings provide valuable insights for the design of driver education and training programs to improve driving safety with ADAS.

Perceptions of novice driver education needs; Development of a scale based on the Goals for driver education using young driver and parent samples

Professional driver education and training has traditionally been used to improve the driving skills of young people with the expectation that they will drive more safely and therefore be less likely to crash. Despite this, much research has failed to find such an effect. The Goals for Driver Education (GDE) is a theoretical framework developed to improve the effectiveness of driver education. The GDE seeks to draw attention to a broader array of factors that may influence the driving of young people and hence increase the scope of what is included in driver education and training. Within the GDE, factors affecting driving and driver education are grouped into combinations of four hierarchical levels that represent influences on driving at increasing levels of abstraction (GDE level 1: Vehicle manoeuvring; GDE level 2: Mastery of traffic situations; GDE level 3: Goals and contexts of driving; GDE level 4: Goals for life and skills for living) and three types of individual competencies (Knowledge and skills; Risk-increasing factors; Self-evaluation skills). However, to date there have been few, if any, attempts to operationalise the GDE in a quantitative way. In response, the Perceptions of Driver Education Scale (PDES) was developed to gain an understanding of what two important stakeholder groups in the driver education and training ecosystem perceived to be important to be taught to novice drivers. Understanding these perceptions may lead to greater acceptance of the driver education and training that is designed and make it more effective. Following rigorous scale development procedures, thirty-five items were developed a priori using expert opinion and deployed, via an online survey, to a sample of parents of novice drivers (N = 518) and a separate sample of novice drivers (N = 247), both from Queensland, Australia. Exploratory Factor Analysis was performed on the parent responses and Confirmatory Factor Analysis was performed using the novice driver responses. A 29-item 9 factor solution was judged to be the best fit with the overall scale and each of the factors displaying strong levels of internal consistency. The 9 factors cover most hierarchical level and competency combinations, although removal of items meant that there is no representation concerning perceptions of Self-evaluation skills related to Level 3: Goals and contexts of driving within the PDES. Overall, the PDES is a valid and reliable instrument for investigating the aspects of driver education and training that are perceived as important by novice drivers and parents. The scale may also be applicable for use with other stakeholder groups and could also be used to evaluate pre-existing driver education and training programs.

How psychological factors affect speeding behavior: Analysis based on an extended theory of planned behavior in a Chinese sample

Speeding, the most frequent traffic offense, is a key contributor to road crashes. This study explores the psychological factors affecting drivers’ speeding behavior (exceeding the posted speed limit by more than 10%) based on the extended theory of planned behavior (TPB). The scale incorporating TPB constructs and additional variables (Type A personality, risk perception, and driving habit) was developed to collect empirical data, and 945 valid samples were obtained. The reliability and validity of the developed scales were verified, and a structural equation model of drivers’ speeding behavior was established to explore the causal relationship between speeding behavior and psychological characteristics and the interrelationships between TPB variables (speeding intention, attitude, subjective norm, and perceived behavioral control), explicit cognition predictors (risk perception and Type A personality) and implicit habitual factors (driving habit). The results showed that speeding intention and driving habit positively correlated with self-reported speeding behavior. In addition, attitude and risk perception positively correlated with speeding intention. Although Type A personality trait had no significant effects on speeding behaviors, it had significant effects on attitude and risk perception. In addition, the results of the mediation analysis demonstrated that speeding intention, driving habit, and attitude toward speeding are the most influential factors. Attitude and risk perception have a significant indirect effect on speeding behavior mediated by speeding intention. Drivers’ attitudes, driving habits, and risk perception played a mediating role in the relationships between Type A personality traits and speeding behavior. The findings yield insights into useful information and directions for designing road safety interventions, developing driver education and training programs, and strengthening social messaging aimed at the general public.

Self-reported aberrant driving behavior among Bus Rapid Transit drivers

While driving, drivers often engage in aberrant driving behaviors, increasing their risk of getting involved in road crashes. The present study examines the human factors affecting aberrant driving behavior among professional Bus Rapid Transit (BRT) drivers and investigates the relationship between behavioral factors, demographic variables, and crashes. A total of 135 drivers participated in a self-reported survey by responding to the driver behavior questionnaire (DBQ). Exploratory factor analysis performed on DBQ revealed three behavioral factors: violations, errors, and positives. It was found that behavioral factors (violations, errors, and positives) and demographic variables (education, driving experience, and alcohol consumption) were significant predictors of crashes. However, driver's age and marital status were not associated with crashes. The study highlights the importance of positive driver behaviors in reducing crashes; thereby, suggesting it as one of the essential components of driving behavior that should be integrated into driver training programs.

Learning to Drive With Neurological Conditions: A Mixed-Methods Study On Cognitive Factors and Self-Determination

<h3>Research Objectives</h3> This study aimed to (1) document the profile of individuals with neurological conditions who participated in an adapted training program to obtain their first driver's license, (2) explore the relationship between cognitive functioning and program outcome, and (3) describe the users' experience before, during, and after the adapted driving program. <h3>Design</h3> Mixed-methods design including retrospective chart review and prospective cross-sectional data collection. <h3>Setting</h3> Adapted driver training program in a rehabilitation center (CIUSSS de la Capitale-Nationale, Québec, QC, Canada). <h3>Participants</h3> The sample included 71 individuals (39% women; mean age = 22.2 +/- 8.6 years) with developmental or acquired neurological conditions who participated in an adapted driving program (Objective 1). Of this number, 35 participants had complete neuropsychological and driving program outcome data (Objective 2), and 12 participated in a qualitative interview (Objective 3). <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measures</h3> Program outcome (success, failure/discontinuation), neuropsychological data (indices of global cognitive functioning, processing speed, working memory, attention, visuospatial skills, and executive functioning), individual qualitative interviews. <h3>Results</h3> Among the 71 participants, the driving program was successfully completed for 47%, failed for 7%, discontinued for 34%, and ongoing for 13%. Participants who successfully completed the program (n=15) showed better attention functioning, and better performance relative to global functioning in attention, executive functioning, and working memory, compared to those who failed/discontinued (n=20). Qualitative data provided enriched information about the participants' motivation to learn to drive (e.g., important personal goal, self-determination, fear of failure), repercussion of program success (e.g., personal and professional development opportunities) or failure (e.g., emotional response, alternative transportation means), and key adaptations of the driving program (e.g., team spirit, individualized teaching). <h3>Conclusions</h3> Adapted training is effective in helping individuals with neurological conditions to learn to drive. Attention, and to a lesser extent executive functioning and working memory, play a key role in driving program success. Knowledge on the repercussions of obtaining or failing to obtain their driver's license could help further adapt and support driving training for individuals with neurological conditions. <h3>Author(s) Disclosures</h3> The authors report no conflicts of interest.

Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets

Driving safety has been a concern since the first cars appeared on the streets. Driver inattention has been singled out as a major cause of accidents early on. This is hardly surprising, as drivers routinely perform other tasks in addition to controlling the vehicle. Decades of research into what causes lapses or misdirection of drivers’ attention resulted in improvements in road safety through better design of infrastructure, driver training programs, in- vehicle interfaces, and, more recently, the development of driving assistance systems (ADAS) and driving automation. This review focuses on the methods for modeling and detecting spatio-temporal aspects of drivers’ attention, i. e. where and when they look, for the two latter categories of applications. We start with a brief theoretical background on human visual attention, methods for recording and measuring attention in the driving context, types of driver inattention, and factors causing it. We then discuss machine learning approaches for 1) modeling gaze for assistive and self-driving applications and 2) detecting gaze for driver monitoring. Following the overview of state-of-the-art models, we provide an extensive list of publicly available datasets that feature recordings of drivers’ gaze and other attention-related annotations. We conclude with a general overview of the remaining challenges, such as data availability and quality, evaluation methods, and the limited scope of attention modeling, and outline steps toward rectifying some of these issues. Categorized and annotated lists of the reviewed models and datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ykotseruba/attention_and_driving</uri>

Moral disengagement mechanisms in interactions of human drivers with autonomous vehicles: Validation of a new scale and relevance with personality, driving style and attitudes

The introduction of autonomous vehicles (AVs) in the road transportation systems raises questions with respect to their interactions with human drivers’, especially during the early stages. Issues such as unfamiliarity or false assumptions regarding the timid and safe behaviour of AVs could potentially result in undesirable human driver behaviours, for instance “testing” AVs or being aggressive towards them. Among other factors, morality has been determined as a source of aggressive driving behaviour. Following previous approaches on moral disengagement, the current paper argues that moral standards during interactions of human drivers with AVs could potentially blur, leading to the disengagement of self-regulation mechanisms of moral behaviour. The study investigates the impact of moral disengagement on the intention of human drivers to be aggressive towards AVs. To that end, an online survey was conducted including a newly developed survey of moral disengagement, adapted to the context of AVs. Moreover, measures of personality, driving style, attitudes towards sharing the road with AVs and perceived threats were collected. A confirmatory factor analysis provided support for the concept of moral disengagement in the context of AVs. Moreover, relationships between personality, driving style and attitudes towards sharing the road with AVs were found, via a structural equation modelling approach (SEM). The results could have implications in the future driver training and education programmes, as it might be necessary to not only focus on driving skills but also on the development of procedural skills that will improve the understanding of AVs’ capabilities and ensure safer interactions. Efforts on improving attitudes towards AVs may also be necessary for improving human driver behaviour.

Transfer and Retention: A Systematic Exploration of the Effect of a Driver Attention Training Program

Young drivers are found to be particularly poor at anticipating latent hazards compared to experienced drivers. Road Awareness and Perception Training (RAPT; Pradhan et al., 2009) is a PC-based driver training program that was designed and demonstrated to improve latent hazard anticipation in young drivers. The current longitudinal study aims to examine retention and transfer of RAPT. Participants will complete pre- and post-training evaluation of their latent hazard anticipation skills in both near- and far-transfer scenarios, and all participants will be randomly assigned to either a RAPT or Placebo training group. Two months later, they will be asked to return for the second evaluation session where their latent hazard anticipation skills will be measured again. We hypothesize that latent hazard anticipation performance will persist for the near-transfer scenarios but decay for the far-transfer scenarios.

Refreshing Older Adults’ Driving Skills (ROADSkills): A randomized controlled trial examining the effect of video feedback

ObjectivesThis study evaluated the effectiveness of a personalized video-based driver training program on the behind-the-wheel skills of community-dwelling older adults. MethodIn this randomized controlled trial (RCT), 80 older drivers [mean age (SD) = 71.0 (3.9)] were randomly assigned to view one of two educational videos: 1) personalized video feedback on their driving (n = 40) or 2) a generic video on aging-in-place (n = 40). The primary outcome, the total number of errors accrued in a 30-minute standardized on-road evaluation, was analyzed at baseline and 4 weeks after watching the assigned video. On-road evaluations were video-recorded and scored by a blinded rater. Self-report measures of driving abilities, behaviors, and comfort were also compared. ResultsAt the 4-week follow-up, the mean difference in the number of on-road performance errors [mean (95% CI) = -12.0 (-16.6, −7.4), p < 0.001] favored the intervention group compared to controls, with significant improvements across multiple domains: vehicle control [mean (95% CI) = -4.8 (-8.1, −1.5), p < 0.01], observing the roadway [mean (95% CI) = -5.5 (-8.4, −2.6), p < 0.001], and compliance with traffic rules [mean (95% CI) = -1.3 (-2.3, −0.2), p < 0.05]. Within-group change on behind-the-wheel errors was significant for the intervention group [mean (95% CI) = -10.3 (-13.5, −7.1), p < 0.001], but not for the control group [mean (95% CI) = 1.7 (-1.6, 4.9), p > 0.05]. No significant differences were found on the self-report measures. DiscussionA novel, video-based approach that provided personalized feedback to older drivers significantly reduced behind-the-wheel errors 4-weeks post-intervention. Changes in self-reported driving behaviors and abilities were not significant. Future research will examine the long-term effects of this approach on older adults’ actual and perceived driving abilities.

Exploring the Determinants of School Bus Crash Severity

Although the school bus is considered a safe form of transportation, school bus crashes are a major safety concern. School bus crashes are a result of driver error of either the at-fault school bus or another at-fault vehicle (where the school bus is not at fault). To examine the risk factors associated with school bus related crashes and crash outcomes, this study segments and develops two binary logit models for each school bus crash type. A total of 1702 school bus related crashes recorded between 2009 and 2016 were used to estimate the model. According to the model results, sideswipe collisions were less likely to result in injury outcomes for at-fault school buses than rear-end and side-impact collisions. Speeding, driving impaired or under the influence, and negotiating a curve all have significant positive associations with injury outcomes in not-at-fault school bus crashes. This study’s practical implications include enforcing the school bus safe driving guidelines, training programs for school bus drivers that include elements of nonroutine trips, training for crossing guards, and awareness programs for drivers of other vehicles to instill safe driving practices around school buses.