Driver Risk Research Articles

A study on the optimal safety speed and warning sign distance for highways in windy and sandy environments based on driving behavior

Objective This study aims to address the issue of driving safety on highways in the desert region of Northwest China during extreme weather conditions such as sandstorms, with the goal of reducing driver risk. It explores driver behavior under extreme conditions of sandstorms and sand accumulation, proposing safety speed recommendations and warning models for different environments to calculate the optimal warning distance in windy and sandy conditions. Methods Natural driving simulation experiments were conducted in windy and sandy environments, collecting driving behavior data from 45 drivers under varying visibility and road conditions with or without sand accumulation. This data were utilized to determine safe speeds before and after deceleration, as well as before and after lane changes. Warning models were established for both sandstorm and sand accumulation conditions. Results Driving stages in sandy environments were classified into normal driving, deceleration and low-speed stable driving phases. In sandstorm-sand accumulation environments, the stages included driving, lane changing and low-speed stable driving after lane changes. The recommended post-deceleration speeds for visibility levels of 2000, 500, 300, 150, 100 and 50 m were 120 km/h, 90 km/h, 70 km/h, 60 km/h, 50 km/h and 40 km/h, respectively. The recommended speeds after lane changes in sand-accumulated areas were 90 km/h, 80 km/h, 60 km/h, 60 km/h, 50 km/h and 40 km/h. The optimal warning distance for highways in windy and sandy environments was calculated to be 1100 m. Conclusion The recommended safe speeds under different visibility conditions in sandstorms and sand accumulation environments are important references for future regulations. The suggested warning distance for highway signs is 1100 m, which can effectively ensure driving safety and reduce accident risks.

Major Depressive Disorder and Driving Behavior Among Older Adults

Depression and antidepressant use are independently associated with crash risk among older drivers. However, it is unclear what factors impact daily driving that increase safety risk for drivers with depression. To examine differences in naturalistic driving behavior and safety between older adults with and without major depressive disorder (MDD). A prospective longitudinal cohort study was conducted among older adults (≥65 years) from the Driving Real-World In-Vehicle Evaluation System Project collected from July 1, 2021, to December 30, 2023. The sample included 85 participants with MDD and 310 participants without. Neurological, clinical, mood, and neuropsychological tests were collected annually. Daily driving behavior was recorded using a commercial data logger. Statistical analysis was performed from January 31 to June 24, 2024. MDD and antidepressant usage. Linear mixed models with propensity score weighting compared slopes of driving behaviors over time (trips taken at night, speeding, hard braking, entropy, and radius of gyration) between groups. In a sample of 395 participants, 85 were classified as individuals with MDD (mean [SD] age, 69.6 [6.1] years; 60 [70.6%] female; 8 [9.4%] non-Hispanic Black and 77 [90.6%] non-Hispanic White) and 310 as individuals in the control group without depression (mean [SD] age, 70.1 [5.1] years; 153 [49.4%] female; 40 [12.9%] non-Hispanic Black and 270 [87.1%] non-Hispanic White). Adults with MDD had greater depressive symptoms (mean [SD], 8.35 [5.35] vs 2.33 [2.72]; difference, 6.02; 95% CI for difference, 5.17 to 6.85; P < .001), comorbidities (mean [SD], 4.08 [2.07] vs 2.79 [1.67]; difference, 1.29; 95% CI for difference, 0.87 to 1.70; P < .001), used more antidepressants (mean [SD], 0.94 [0.81] vs 0.27 [0.54]; χ21 = 65.8; P < .001), and had a higher number of medications (mean [SD], 3.80 [3.27] vs 1.98 [2.21]; χ21 = 21.0; P < .001) compared with controls at baseline. Longitudinal analysis demonstrated an association between adults with MDD and hard braking (mean [SE], 3.17 × 10-4 [7.30 × 10-5] vs 6.70 × 10-5 [4.00 × 10-5]; difference, 2.50 × 10-4; 95% CI for difference, 1.74 × 10-4 to 4.61 × 10-4; P < .001) and hard cornering events per trip (mean [SE], 0.80 [0.64] vs 0.57 [0.25]; difference, 0.23; 95% CI for difference, 0.08 to 1.06; P = .04), greater distances driven from home (mean [SE], 31.19 [7.35] vs 7.76 [3.80] km; difference, 23.43; 95% CI for difference, 0.28 to 15.2; P < .001), more unique destinations visited (mean [SE], 0.34 [0.10] vs -0.27 [0.03]; difference, 0.61; 95% CI for difference, 0.14 to 0.54; P < .001), and higher random entropy (mean [SE], 0.01 [0.01] vs -0.02 [0.00]; difference, 0.03; 95% CI for difference, -0.03 to -0.01; P < .001) over time. In this longitudinal cohort study of older drivers, adults with MDD demonstrated distinct and riskier driving behaviors than those in the control group without depression, with higher rates of hard braking, cornering, and unpredictability in driving patterns over time. Routine depression screening and tailored interventions are essential for enhancing driving safety and maintaining independence among older adults with MDD. Comprehensive care approaches addressing both mental and physical health are crucial for this vulnerable population.

A game-theoretic driver steering model with individual risk perception field generation

Driver-automation shared steering control (SSC) has emerged as a promising technology for enhancing vehicle safety, but desire to achieve seamless collaboration between the driver and automation requires an in-depth understanding of driver steering behavior in interaction with automation. In this paper, we introduce a game-theoretic driver steering model with individual risk perception field generation. Firstly, a driver risk perception field is developed based on a novel concept of potential injury risk (PIR) to provide a quantitative estimation of the driver’s perceived driving risk. This approach offers an explicit and physically meaningful structure for simulating the driver’s risk perception process and elucidating the reasons for discrepancies in risk perception. Then, this driver risk perception field is integrated into the framework of non-cooperative Nash game to model the steering interaction between the driver and automation, and the analytical expression of Nash equilibrium is derived in detail. The resulting combined driver model effectively captures the driver adaptation at both the control and planning levels. Next, the key parameters of the combined driver model and its comparators are identified using measured driver steering behavior data from thirty subjects in a series of driving simulator experiments. Finally, the effectiveness and superiority of the combined driver model is validated through a comprehensive comparative analysis. The results demonstrate that the combined driver model achieves the lowest prediction errors compared to its comparators and effectively captures the individual differences in risk perception and steering behavior.

The biosocial health of U.S. long haul truckers: Syndemics of the road

IntroductionDue to daily and enduring economic and other pressures, poor working conditions, and a common pattern of behaviors and practices, long-haul truck driving is a physiologically stressful and both biologically and psychologically challenging job that is not conducive to healthy living or routine healthcare seeking. The work context of this occupation fits the definition of a “risk environment,” namely a space in which a variety of factors interact to increase the chances of harm occurring. While prior research on this population has tended to focus on one or two primary health conditions or separately review multiple health conditions as a consequence of specific trucker lifestyle and working conditions, newer models of health, like syndemic theory, consider synergistic interactions among comorbid diseases that are interconnected within a set of linked social determinants of health. This approach is concerned with assessing how intersecting living/working conditions promote the clustering and adverse interaction of diseases and other health factors. MethodsThis paper reviews identified bodies of literature that address: the nature of the work conditions of long haul truckdrivers and the factors responsible for these conditions, the health risks associated with the conditions, and the potential syndemic interactions of diseases that are frequent in this population and pathways of disease interaction. Because of the multiple literatures involved the paper does not employ a systematic review approach. Settingand Population: The paper focuses on long haul truck drivers--who may travel coast to coast remaining on the road for weeks and sometimes months at a time--in the U.S. ResultsA number of likely syndemics common among long haul truck drivers are identified and assessed. This review affirms that a syndemics approach is well suited to the challenge of better understanding and improving worker health. A syndemics lens pushes researchers to develop a comprehensive biosocial picture of health in a population and to consider biosocial and multi-disease interventions. ConclusionThis paper employs a syndemics lens to review the key health risks of long haul truck drivers in the U.S., identify the factors increasing their behavioral and other health risks, and suggest likely synergistic health consequences in need of public health intervention. In light of the serious consequences of the health, social, and economic cost of syndemics of the road, there is a need for expanded attention to this issue in research, funding, and policy.

The health and wellbeing of cardiology trainees in the united kingdom

Abstract Background Cardiology is a rewarding medical speciality. However, the intense workload can burden cardiologists with physical and psychological demands, and risks contributing to ill health. Cardiologists suffering poor health may increase absenteeism, stretching a workforce, and potentially reduce quality of patient care. Purpose We sought to describe the prevalence of physical and mental health conditions of cardiology trainees within the United Kingdom. We also aimed to identify factors associated with risk of burnout and negative drivers of psychological wellbeing. Methods The British Junior Cardiologists Association (BJCA) is a professional body which represents cardiology trainees in the UK. The BJCA conducts an annual survey of trainees, covering various aspects of working lives and all UK cardiology trainees are eligible to participate. For the 2023 survey, in addition to demographics and training questions, we asked questions on ill health, burnout, support for wellbeing, and invited responders to complete validated screening questionnaires for anxiety (GAD-7) and depression (PHQ-9). Responders were eligible for inclusion if they consented to take part in the health and wellbeing questions, and if they had completed four demographic questions. Anxiety and depression were deemed significant if scoring in the moderate or severe range (PHQ-9≥10; GAD7≥10). Significant burnout was defined as responders indicating being "somewhat" or "very" close to burnout. Logistic regression was used to determine odds ratios (OR) between univariate predictors of burnout, anxiety, and depression. Results Of the 398 responders, 212 fulfilled inclusion criteria (66% male, age 34±4 years, 42% had dependents). A physical health condition was reported by 11% of trainees, and 9% a reported a mental health condition. Significant burnout was reported by 76% of trainees. Those with dependents reported lower rates of burnout compared to those without (OR 0.35, 95% CI 0.18-0.67, p&amp;lt;0.01). Significant depression scores were reported by 25%, significant anxiety scores in 18%, and significant scores of either depression or anxiety in 29% (Fig 1). Scores from 17% of trainees were consistent with significant depression or anxiety and yet not known to have a mental health condition. Less Than Full Time trainees reported higher rates of anxiety (OR 4.38, 95% CI 1.37-14.10, p=0.01) and depression (OR 10.31, 95% CI 2.98-35.71, p&amp;lt;0.01). Training was reported to have a negative impact on physical health by 43% of trainees, and 47% reported a negative impact on mental health. Principal drivers of negative wellbeing included the amount of service provision; curriculum requirements, and lack of training opportunities (Fig 2). Conclusions The prevalence of anxiety, depression and burnout is high amongst cardiology trainees. Urgent action is needed by training bodies and employers to ensure training programmes consider the physical and mental wellbeing of trainees.Depression and Anxiety ScreeningNegative Drivers of Wellbeing

Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.

Validating risk behavior in driving simulation using naturalistic driving data

The absence of physical accident risk in driving simulation, which allows for safely studying critical driving situations, also reduces the driver's risk perception, which may result in unrealistic driver behavior. Validation studies in this context are rare for ethical reasons, making it difficult to assess the extent of this issue at present. The present study addresses this gap by utilizing naturalistic driving data. Four critical cut-in situations on German highways were extracted from naturalistic data and replicated in a driving simulator study with N = 58 participants. Both in-situ self-ratings on subjective criticality and post-hoc video-based ratings (from the driver and objective observers), as well as presence ratings, were collected to supplement driver behavior. Although driver reactions in the simulator and the field were not equivalent in absolute terms, drivers in both the simulation and the real world exhibited accident-avoidance behavior through braking reactions, indicating relative validity. No clear mediating role of the sense of presence towards a more careful driver behavior was found. This work shows that drivers exhibit accident-avoiding behavior in the simulator and tend to react to hazards in the simulator similarly as they would in a real situation, while absolute numerical values should only be interpreted with caution.

Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

Age-related driver injury occurrence from crashes at curve–grade combined segments

Objectives Due to their relatively complex roadway characteristics, horizontal and vertical curve segments are associated with decreased visibility and a higher risk of rollovers. Multiple studies have identified the associated risk of young and older drivers separately in such complicated driving environments. This study investigated the relationship between driver age and injury occurrence from crashes occurring along curve–grade combined segments. Methods Crash data recorded in Ohio State between 2012 and 2017 were used in this study. Driver age was categorized into 3 groups: teen (age <20 years), adult (age 20–64), and older adult (age >64). Descriptive statistics were summarized using random forest, gradient boosting, and extreme gradient boosting (XGBoost) to estimate the probability of a driver incurring an injury in case of a crash at curve–grade combined segments. The area under the receiver operating characteristics curve (AUROC) was used to select the best performing model. Partial dependence plots (PDPs) were used to interpret the model results. Results The probability of injury occurrence is different for older drivers compared to teen and adult drivers. Although teen and adult drivers showed a higher probability of sustaining injuries in crashes with an increase in the degree of curvature, older drivers were more likely to sustain injuries in roadways with higher annual average daily traffic (AADT), steeper grades, and more occupants in the vehicle. Older drivers were observed to have a higher probability of sustaining injuries during peak hours and when unrestrained compared to teen and adult drivers. Conclusions The results emphasize the significance of tailored education and outreach countermeasures, particularly for teen and older drivers, aimed at decreasing the likelihood of injuries in such driving environments. This research adds to the expanding body of knowledge concerning the age-related occurrence of driver injuries resulting from crashes at curve–grade combined segments. The study findings provide insights into the potential over- or underrepresentation of certain age groups in analyzing crash injury occurrence. The insights gained from the machine learning analysis could also assist policymakers, transportation agencies, and traffic safety experts in developing targeted strategies to enhance road safety and protect vulnerable age groups.

Data-driven neural network model for maximum speed profile based on driver risk

In the field of vehicle control systems, the primary objective is to ensure the safety of road users. The intricate dynamics of road vehicles necessitate a high level of precision. Vehicle safety encompasses a multitude of considerations, including vehicle trajectory, prevailing traffic conditions, road structure, and meteorological factors. This study employs an Artificial Neural Network (ANN) trained with human driver data using SCANeR Studio software to evaluate the risk for the driver. The risk has been defined as a five-level parameter, which depends on the potential danger of a situation, where speed and direction play a crucial role. The system incorporates a simulator, an ANN, and a display interface to present the surroundings and communicate important information to the driver. This research employs a simulated driving scenario comprising a multi-lane roundabout with vehicles travelling in different directions to simulate real-world challenges. Risk estimation is achieved through a Time Delay Neural Network (TDNN) trained with various information about the environment in relation to the driven vehicle. The research employs a Jackknife technique for overall evaluation and introduces an adaptive algorithm for speed limit setting. The findings demonstrate the stability, generality, and practical applicability of the ANN in enhancing road safety.

Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior

Weather visibility interference has a significant impact on driver car-following behavior. To investigate drivers’ car-following behavior and emergency avoidance behaviors under different visibility disturbances, scenarios are constructed under different foggy concentration environments based on driving simulation, and the drivers’ response behaviors are collected in the stable car-following state and emergency rear-end scenarios. Exploring the differential effects of gender and driving experience on driving behavior for fog concentrations based on multifactorial analysis of variance. A quantitative model of car-following risk is constructed based on factor analysis, and a linear mixed model is used to explore the comprehensive effects of fog concentration, speed, and the following distance at the braking time on drivers’ braking reaction time by fully considering the differences in individual behaviors. The results show that driving behavior is significantly affected by visual visibility, driver’s gender, and driving experience. With the decrease of visibility, following driving speed decreases, the following distance is shortened, the headway decreases, and the standard deviation of lane lateral offset distance increases. The rear-end collision risk of an experienced driver is higher than that of a novice driver, and the rear-end collision risk of the female is higher than that of male. The risk of collision is higher when traveling in light fog. In emergency rear-end collision scenarios, as visibility decreases, braking reaction time increases, and the risk of collision conflict increases at the moment of driver braking. The braking reaction time of the driver decreases with the increase of the speed and increases with the increase of the distance when the front vehicle is braking. The results of this study provide theoretical support and technical reference for effectively improving driving safety in a bad-visibility environment.

Novel Sleep Phenotypic Profiles Associated With Incident Atrial Fibrillation in a Large Clinical Cohort

BackgroundWhile sleep disorders are implicated in atrial fibrillation (AF), the interplay of physiologic alterations and symptoms remains unclear. Sleep-based phenotypes can account for this complexity and translate to actionable approaches to identify at-risk patients and therapeutic interventions. ObjectivesThis study hypothesized discrete phenotypes of symptoms and polysomnography (PSG)-based data differ in relation to incident AF. MethodsData from the STARLIT (sleep Signals, Testing, And Reports LInked to patient Traits) registry on Cleveland Clinic patients (≥18 years of age) who underwent PSG from November 27, 2004, to December 30,2015, were retrospectively examined. Phenotypes were identified using latent class analysis of symptoms and PSG-based measures of sleep-disordered breathing and sleep architecture. Phenotypes were included as the primary predictor in a multivariable-adjusted Cox proportional hazard models for incident AF. ResultsIn our cohort (N = 43,433, age 51.8 ± 14.5 years, 51.9% male, 74.9% White), 7.3% (n = 3,166) had baseline AF. Over a 7.6- ± 3.4-year follow-up period, 8.9% (n = 3,595) developed incident AF. Five phenotypes were identified. The hypoxia subtype (n = 3,245) had 48% increased incident AF (HR: 1.48; 95% CI: 1.34-1.64), the apneas + arousals subtype (n = 4,592) had 22% increased incident AF (HR: 1.22; 95% CI: 1.10-1.35), and the short sleep + nonrapid eye movement subtype (n = 6,126) had 11% increased incident AF (HR: 1.11; 95% CI: 1.01-1.22) compared with long sleep + rapid eye movement (n = 26,809), the reference group. The hypopneas subtype (n = 2,661) did not differ from reference (HR: 0.89; 95% CI: 0.77-1.03). ConclusionsConsistent with prior evidence supporting hypoxia as an AF driver and cardiac risk of the sleepy phenotype, this constellation of symptoms and physiologic alterations illustrates vulnerability for AF development, providing potential value in enhancing our understanding of integrated sleep-specific symptoms and physiologic risk of atrial arrhythmogenesis.

Assessment of Health Risk from Exposure to Respirable Particulate Matter (RPM) among Motorcycle Taxi Drivers in Bangkok and Adjacent Provinces, Thailand

Exposure to outdoor air pollutants, particularly respirable particulate matter (RPM), can cause adverse health outcomes. The cross-sectional study aimed to assess motorcycle taxi driver’s health risk from exposure to RPM. A total of 153 motorcycle taxi drivers were recruited in Bangkok and five adjacent provinces during May and June 2022. The standardized questionnaire for data collection contained exposure time (hour/day), frequency of exposure (days/years), duration of exposure (year), body weight (kg), and averaging time (days). The average RPM concentration from six provinces were assessed personal air sampling pumps and ranged from 0.006-0.031 mg/m3. Bangkok showed the highest average RPM concentration (0.031 mg/m3), followed by Pathumthani (0.028 mg/m3), Samut Prakan (0.009 mg/m3), Nakhon Pathom (0.008 mg/m3), Nonthaburi (0.007 mg/m3), and Samut Sakhon (0.006 mg/m3), respectively. The Hazard Quotient (HQ) values for a non-carcinogenic risk to human health caused by RPM exposure in each province indicated a negligible risk (HQ=0.005-0.028). HQ averages (HQ=0.013) from all provinces were also at an acceptable level (≤1). Not all motorcycle taxi drivers are safe from RPM exposure, although their exposure is within acceptable limits depending on their individual susceptibility. Therefore, this is the first report on quantifying exposure to RPM from personal air sampling and health risk assessment among motorcycle taxi drivers. These findings would be useful information for further preventing and controlling ambient air pollution including policies and strategies to mitigate the risks for motorcycle-taxi drivers and the other exposed populations.

Different deployments of attentional breadth selectively predict UFOV task performance in older adults

The Useful Field of View task (UFOV) is a strong and reliable predictor of crash risk in older drivers. However, while the functional domain of attention is clearly implicated in UFOV performance, the potential role of one specific attentional process remains unclear: attentional breadth (the spatial extent of the attended region around the point of visual fixation). The goal of the present study was to systematically test the role of two distinct aspects of attentional breadth, maintaining a specific breadth of attention and resizing the attended region, in UFOV performance. To this end, 135 older adults completed the UFOV and modified Navon tasks to measure their efficiency in maintaining, contracting, and expanding the breadth of attention. We then examined individual-difference associations between these aspects of attentional breadth deployment and UFOV performance. We found that performance on UFOV Subtask 2 was associated with efficient contraction of attentional breadth (i.e., resizing the attended region to a smaller area), while Subtask 3 performance was associated with the efficiency of expanding attentional breadth (i.e., resizing the attended region to a larger area). The selectivity of these relationships appears to implicate these specific deployments of attentional breadth in how people complete the task, as it suggests that these relationships are not simply attributable to shared variance in a broader domain of cognitive functioning. The implications of these results for our understanding of UFOV, as well as future research directions that test the relative contributions of different cognitive processes in predicting task performance, are discussed.

A Novel Technology Stack for Automated Road Quality Assessment Framework using Deep Learning Techniques

Road infrastructure plays a pivotal role in supporting societal, economic, and cultural progress. The capacity of a road refers to its ability to handle vehicular volume. Inadequate road capacity and the presence of defects like potholes and cracks result in suboptimal travel conditions and pose significant safety risks for drivers, cyclists, and pedestrians. The regular evaluation of these road quality aspects is essential for effective maintenance. However, current methods for assessing road capacity are time-consuming, subjective, and heavily reliant on manual labor. Moreover, existing deep learning-based approaches for detecting road defects often lack accuracy. To overcome these challenges, a fully automated and accurate system for evaluating road quality is imperative. Thus, the objective of this research work is to propose a novel technology stack for a comprehensive Automated Road Quality Assessment (ARQA) framework designed to assess road quality. The experimental findings demonstrate that the suggested vehicle detection and pothole detection methods work effectively and exhibit enhancements of 18% and 6%, respectively, in comparison to existing approaches.

Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

Objective Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers’ crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. Methods Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. Results The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). Conclusions The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Advancing a revised UB-DBQ: A new insight into driver behaviour and older passenger injuries in urban buses

IntroductionOlder individuals in China face a heightened risk of injuries on urban buses. While the Driving Behaviour Questionnaire (DBQ) effectively gauges drivers' behaviours and predicts injury involvement, the link between aberrant driving and older passenger injuries among urban bus drivers remains underexplored. This study refines the UB-DBQ for urban bus drivers, probing its association with older passenger injuries. MethodsThe Urban Bus Driving Behaviour Questionnaire (UB-DBQ) was adapted, incorporating unique aspects of urban bus driving. An online survey, based on this revised UB-DBQ, was administered to 710 Chinese urban bus drivers in August 2022. Using principal component factor analysis and oblique rotation-promax, a four-factor UB-DBQ emerged. Correlation and difference tests analysed the relationship between driving behaviours and older passenger injury incidents. ResultsThe four-factor UB-DBQ offers a robust fit for Chinese urban bus drivers. Analysis revealed a prevalence of positive driving behaviours, offset by fewer aberrant actions, including aggression, errors, and violations. Notably, higher numbers of older passengers heightened aggressive and erroneous driving behaviours. A notable trend emerged: drivers largely attributed injuries to older passengers, a sentiment growing with more older passengers aboard. Crucially, driver self-assurance in safety and aggressive behaviour were key predictors of injuries involving older adult passengers. ConclusionsThe revised UB-DBQ serves as an effective tool for assessing urban bus driver behaviour and older passenger injury incidents in China. These insights emphasize the need for tailored driver training and risk strategies in line with the unique demands of urban bus driving in China. The UB-DBQ holds potential for shaping more precise and effective transport safety policies.

Identification of novel driver risk genes in CNV loci associated with neurodevelopmental disorders

Copy number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 patients with NDDs and 4,031 controls from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to i) converge in protein-protein interaction networks; ii) be among most expressed genes in the brain across all developmental stages; and iii) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical controls from 1,000 Genomes and iHART, that resulted in the validation of the association of 162 dosage sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

Heavy vehicle driver fatigue: Observing work and rest behaviours of truck drivers in Australia

The extant transport literature suggests that truck driver fatigue is a leading contributor in truck crashes. Various factors are known to affect a truck driver’s ability to comply with work and rest requirements, including long working hours, irregular work schedules, sleep deprivation and driving at night. In Australia, while control measures restrict the number of hours of work a truck driver may undertake without rest, they travel vast distances and work with a high level of autonomy. This makes it difficult for transport companies to monitor and manage truck drivers’ work and rest compliance; thereby relying on them to self-manage driver fatigue at designated rest-stops. However, there is little Australian research examining rest-stop behaviour of truck drivers in relation to driver fatigue compliance. To better understand how truck drivers manage driver fatigue, an exploratory participant observation study of truck drivers (N = 378) was undertaken at two Australian truck rest stops in rural New South Wales. The findings indicate that while most truck drivers comply with rest requirements regarding driver fatigue, load type, driver changeovers, access to parking, and time spent on work-related activities at the rest areas affects the time truck drivers spend at rest, and these factors are likely to increase the risk of truck drivers not complying with driver fatigue regulation. They are also likely to increase the number of truck drivers involved in fatigue related crashes.

Does a cautious driving style reduce the crash risk of older drivers? An analysis using a novel driving style recognition method

Older drivers who are aware of their declining functions tend to adopt a cautious driving style to compensate for their declining driving abilities. However, whether cautious older drivers are associated with a reduced crash risk has not been well studied. In this study, we examined the association between older drivers’ cautious driving style and crash risk using a novel driving style recognition method. We proposed a driving style recognition method that does not require predefined thresholds and applied it to the driving data of 71 drivers comprising 59 drivers aged 65 years or older (mean age 72.4; 67.8 % male) and 12 drivers aged 51–64 years (mean age 58.3; 66.7 % male). Drivers were classified into cautious and relatively aggressive groups based on the classification results of their speed, acceleration, and braking styles. The results showed that cautious drivers were characterized by older age, poorer Trail Making Test Part B performance, less frequent night driving, and lower driving exposure, indicating that older drivers may adopt multiple compensatory strategies. Although the crash involvement rate of cautious older drivers was approximately half that of relatively aggressive drivers, the difference was not statistically significant. Furthermore, this difference was expected to decrease when driving exposure was considered. Older drivers who adopted a cautious driving style but were involved in traffic crashes had poorer visual acuity and contrast sensitivity. The results of this study indicate that a cautious driving style alone may not be sufficient to compensate for older drivers’ declining functions, particularly the loss of two visual functions observed in the present study.