The Driver Behaviour Questionnaire as accident predictor; A methodological re-meta-analysis

Driver's journey from historical traffic violations to future accidents: A China case based on multilayer complex network approach

Traffic violation records serve as key indicators for predicting drivers' future accidents. However, beyond statistical correlations, the underlying mechanisms linking historical traffic violations to future accidents remain inadequately understood. This study introduces a research framework to address this gap: Using Propensity Score Matching and an adapted mutual information-based feature selection algorithm to precisely identify correlations and optimal time windows between drivers' historical traffic violations and future accidents. A multilayer complex network approach was then applied to abstract and model the progression from drivers' historical traffic violations to subsequent accidents, revealing intrinsic patterns through adapted network analysis metrics and ultimately uncovering underlying mechanisms. Actual data from over 17,000 drivers in Shenzhen, China, spanning the period of 2010 to 2020, was utilized. Results revealed significant heterogeneity among driver subtypes with various driving license types regarding optimal time windows and key traffic violations indicative of future accident risks. A universal "Stable Defect Effect" was identified across all driver subtypes, characterized by persistent driving-related deficiencies resistant to temporal decay and penalties. This effect's gradual formation and maturation appear to govern the progression from traffic violations to future accidents. In addition, multilayer complex network models demonstrated significant potential in accident risk studies, particularly in providing valuable latent information by overcoming the limitations of accident data samples.

Self-management of Risk for the Prevention of Traffic Accidents from a Health Perspective: A Qualitative Study

Introduction: In the world, deaths and injuries caused by traffic collisions have been considered a public health problem. In Colombia, 7.238 fatalities were recorded in 2021, with motorcycle riders representing the largest group of victims at 59.7%. Methods: The aim of this qualitative phenomenological study is to describe the risky experiences and deliberate actions of diverse road users that influence the self-management of the risk of traffic collisions. Results: Data were obtained from 22 participants: motorists, pedestrians and drivers. The content analysis describes various human conditions that affect self-management of the risk of traffic accidents, such as unsafe behaviors, non-compliance with traffic regulations by the different road actors, competitive culture among drivers, eagerness, among others. Additionally, factors related to care were determined: healthy recreational activities, promoting the value of one's own life and that of others, adequate time management and preventive behaviors by some road users. Conclusion: This research provides information on social and cultural aspects, experiences and risky behaviors of different road actors that influence the incidence of traffic accidents in Colombia.

Development and validation of e-scooter riding behavior questionnaire (ERBQ) among Korean riders

The main objective of this research was to develop a questionnaire that demonstrates elevated levels of reliability to assess the behaviour of e-scooter users. The researchers designed an E-scooter Riding Behaviour Questionnaire (ERBQ) with 27 items. This questionnaire aimed to assess the self-reported frequency of various e-scooter riding behaviours, including errors, violations and behaviours. Four hundred eighty-three e-scooter riders completed the ERBQ with subsequent data analysis. Factor analysis was used to identify a six-factor solution that includes control errors, traffic violations, slips and lapses, prohibited actions, positive behaviour and negative behaviour. The findings of the variance study revealed that, after accounting for gender as a confounding factor, errors, violations and negative behaviour emerged as the primary indicators of the likelihood of a crash, near miss and ticket experience. This study focuses on the inferences drawn from the findings about the most effective countermeasures to reduce e-scooter crashes.

Identification and screening of key traffic violations: based on the perspective of expressing driver's accident risk

Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.

Understanding key behavioral factors affecting road traffic citation and crash involvement of professional bus and passenger van drivers using a modified driver behavior questionnaire: an Indian perspective

A customized 27-item driver behavior questionnaire (DBQ) for professional long-distance bus and passenger van drivers in Bihar, India was tested separately and the underlying factor structures identified. In total, 156 bus and 149 passenger van drivers were surveyed and their self-reported aberrations, measured using the DBQ, were recorded along with their self-reported traffic citation and crash involvement in the past 3 years. A 21-item seven-factor DBQ and a 19-item five-factor DBQ were obtained for bus and passenger van drivers respectively through exploratory and confirmatory factor analysis. Structural equation modeling was used to find relations between DBQ factors and drivers' number of crashes and traffic citations involvements. Only speed issues could significantly explain traffic citation involvement but no factor could significantly explain crash involvement for bus drivers. For passenger van drivers, only aggressive violations could explain traffic citation involvement while unmindfulness, aggressive violations and errors could explain crash involvement.

Optimization method to reduce the risky driving behaviors of ride-hailing drivers

INTRODUCTION

To promote the safety level of ride-hailing services, this study develops the Targeted and Differentiated Optimization Method of Risky Driving Behavior Education and Training (TDOM-RDBET) founded on driver type classification of high-risk drivers.

METHOD

Based on value and goal orientations, 689 drivers were classified into four driver types and were assigned to three groups, including an experimental group, a blank control group, and a general control group. This research preliminarily analyzes the effectiveness of the TDOM-RDBET to reduce mobile phone use while driving by assessing the main effects of the group and test session on the risk value ranking of mobile phone use while driving (AR), the frequency per 100 km of mobile phone use while driving (AF), and the frequency per 100 km of risky driving behaviors (AFR), as well as the interactive effects of the two factors on AR, AF, and AFR, based on a two-way analysis of variance (two-way ANOVA).

RESULTS

The results demonstrate an overall significant reduction in AR (F = 8.653, p = 0.003), AF (F = 11.027, p = 0.001), and AFR (F = 8.072, p = 0.005) for the experimental group after training. Moreover, significant interactive effects of the driver group × test session on AR (F = 7.481, p = 0.001) and AF (F = 15.217, p < 0.001) were found. AR was significantly lower for the experimental group than for the blank control group (p < 0.05) in the post-training condition. Moreover, AF was also significantly lower for the experimental group than for the blank control group (p < 0.05) and general control group (p < 0.05) in the post-training condition.

PRACTICAL APPLICATIONS

On the whole, it was preliminarily verified that the TDOM-RDBET is more effective than the general training method at modifying the risky driving behavior.

Profiles of women who have suffered occupational accidents in cleaning: perceived health, psychosocial risks, and personality variables

PURPOSE

The main goal was to identify the variables (sociodemographic, work, psychosocial, perceived health, and personality) associated with occupational accidents suffered in the past by women in the cleaning sector.

METHODS

A sample of 455 women was evaluated.

RESULTS

A total of 23.5% of the workers (n = 107) had suffered an occupational accident with medical leave. In general, women who had suffered some accident in their life had a worse situation in all areas evaluated. Two subsamples of women had a greater association with accidents. Specifically, the presence of work accidents was 15.9 times higher among those who presented a worse perception of their physical effort and a greater tendency towards risky behaviours and 13.5 times higher among those who had a moderate perception of physical exertion and a disability.

CONCLUSION

In general, the characteristics of female workers were found to be associated with different accident rates. Preventive actions should be designed individually.

Is safety in the eye of the beholder? Discrepancies between self-reported and proxied data on road safety behaviors—A systematic review

Recent studies have problematized on the lack of agreement between self-reported and proxied data in the field of road safety-related behaviors. Overall, and although these studies are still scarce, most of them suggest that the way we perceive our own road behavior is systematically different from the perspective from which we perceive others' behavior, and vice versa. The aim of this review paper was to target the number and type of studies that have researched the behavioral perceptions of different groups of road users, contrasting self-reported behavioral data with those reported by other users (proxied), and their outcomes. This systematic review followed the PRISMA methodology, which allows for the identification of relevant articles based on the research term. A total number of 222 indexed articles were filtered, and a final selection of 19 articles directly addressing the issue was obtained. Search strategies were developed and conducted in MEDLINE, WOS, Scopus and APA databases. It is remarkable how road users perceive themselves as behaviorally “safer” than the rest of road users in what concerns the knowledge of traffic norms and their on-road performance. In addition, and regardless of the type of user used as a source, self-reported data suggest their perceived likelihood to suffer a traffic crash is lesser if compared to any other user. On the other hand, proxied reports tend to undervalue third users' performance, and to perceive riskier behaviors and crash-related risks among them. The outputs of this systematic review support the idea that the perception of road users' behavior and its related risks substantially differ according to the source. It is also necessary to increase the number, coverage and rigor of studies on this matter, perhaps through complementary and mixed measures, in order to properly understand and face the bias on road users' risk-related behaviors.

The guilty, the unlucky, or the unaware? Assessing self-reported behavioral contributors and attributions on pedestrian crashes through structural equation modeling and mixed methods

INTRODUCTION

Recent literature suggests that the causation of pedestrians' crashes and the contribution of safety-related behaviors within them may substantially differ compared to other road users. This study aimed to test the effect of individual factors and safety-related road behaviors on the self-reported walking crashes suffered by pedestrians and, complementarily, to analyze the causes that pedestrians attributed to the crashes they suffered as pedestrians during the previous five years.

METHOD

For this cross-sectional research performed in Spain, data from a nationwide sample of 2,499 pedestrians from the 17 regions of the country were collected. Participants had a mean age of 31 years. They responded to a questionnaire on demographics, safety-related walking behaviors, and self-reported pedestrian crashes and the causes attributed to them.

RESULTS

Utilizing Structural Equation Models (SEM), it was found that self-reported walking crashes can be predicted through unintentional risky behaviors (errors). However, violations and positive behaviors remain non-significant predictors, allowing to hypothesize that they might, rather, play a key role in the pedestrian's involvement in pre-crash scenarios (critical situations preceding crashes). Also, categorical analyses allowed to determine that the causes that pedestrians attributed to the walking crashes they had suffered were principally their own errors (44.6%), rather than their own traffic violations (8.5%). Nevertheless, this trend is inverse when they believe the responsibility of the crash weighs on the driver. That is to say, they usually attribute the crash to their traffic violations rather than errors. However, many biases could help explain these attributional findings.

PRACTICAL APPLICATIONS

The results of this study highlight key differences in behavioral features and crash predictors among pedestrians, with potentially relevant applications in the study and improvement of walking safety from behavioral-based approaches.

To what extent do our walking and cycling behaviours relate to each other, and do we cycle as well as we think we do? Piloting the walking and cycling behaviour questionnaires in the UK

Greater uptake of active transport has been argued as necessary for the transport system to achieve relevant sustainability and public health goals; however, the research tools used to investigate behaviour when using these modes are far less well-developed than those used to investigate driving behaviour. This study takes two self-report behavioural measures, the Walking Behaviour Questionnaire (WBQ) and the Cycling Behaviour Questionnaire (WBQ), and pilots them in the UK. Exploratory and confirmatory factor analyses with data from 428 respondents revealed factor structures different to those described in the limited number of previous studies that used the CBQ and WBQ. Across both questionnaires, scales measuring intentional behaviour differed from original descriptions to a greater extent than did the scale concerning unintentional attention or memory errors. In addition to a validation exercise, this research explored the relationships between variables, finding a correlation between the reported performance of unintentional errors when walking and cycling. Looking in more detail at cycling behaviours, we found that those who rated themselves as more proficient cyclists also reported performing fewer unintentional cycling errors. Results also showed self-reported helmet use to bear little to no relationship with other self-reported cycling behaviours or self-rated cycling proficiency. Finally, using structural equation modelling, we demonstrated that responses to the CBQ add very little (over and above age, gender, and exposure to the road environment) to the explanation of self-reported past collision involvement. In total, only 7% of the variation in past collision involvement was explained by the included variables. We urge caution when using self-report behavioural measures that have not been validated in the context of intended use, and the importance of using such measures in combination with other approaches rather than in isolation when trying to develop an understanding of overall system performance.

Development and validation of questionnaires on professional drivers’ knowledge and attitudes about various medications’ influence on driving ability

Introduction

Professional drivers’ knowledge about driving-impairing medications is not satisfactory. The aim of this study was to develop and test the reliability and validity of the questionnaires designed to measure the knowledge and attitude of professional drivers about the influence of various medications on driving ability.

Methods

The questionnaires for assessing professional driver’s knowledge (performance-based) and attitudes about influence of various medications on driving abilities were developed by creating the item pool, testing reliability and validity, and factor analysis. The study was conducted as a multicenter, cross-sectional study in Serbia and Bosnia and Herzegovina. The study population consisted of professional drivers, who filled out both questionnaires in three time intervals.

Results

Both questionnaires showed great internal consistency and temporal stability. Cronbach’s Alpha for the first questionnaire was 0.984 and for the second it was 0.944. The Kaiser–Meyer–Olkin test for the first questionnaire confirmed sampling adequacy with its value of 0.964 and for the second questionnaire it was 0.933. Exploratory factor analysis of the questionnaire showed that three factors were revealed after rotation for the first questionnaire and they explained 78.0% of variance. Both questionnaires showed high degree of correlation between scores after the first and repeated administration, Spearman’s rho coefficient of correlation for was 0.962 and 0.980.

Conclusion

Based on the results of this study, we believe that both questionnaires are useful tools for testing professional drivers’ knowledge and attitudes about the influence of medications on driving ability.

Driver behaviour and traffic accident involvement among professional heavy semi-trailer truck drivers in China

The purpose of this study was to develop a driving behavior scale for professional drivers of heavy semi-trailer trucks in China, and study the causes of such driving behavior and its impact on traffic safety operation. Data was processed by IBM SPSS 25. In addition to principal component analysis, Promax rotation, Bartlett's test, Cronbach's alpha, correlation analysis and binary logistic regression were examined. A DBQ with 4 dimensions and 20 items, and a PDBQ with 1 dimension and 6 items were developed for professional drivers of heavy semi-trailer trucks in China. The KMO coefficients of PDBQ and DBQ were 0.822 and 0.852, respectively, and the significant level of Bartlett's popularity test was p < 0.0001. The accident prediction model showed that the variables related to traffic accidents were negligence/lapses and driving time of heavy semi-trailer truck drivers. 1-5 a.m. was found to be the most dangerous period for drivers of medium and heavy semi-trailer trucks, during which accidents were most likely to happen. As negligence/lapses increased by one unit, the probability of traffic accidents increased by 2.293 times.

Influence of familiarity with traffic regulations on delivery riders' e-bike crashes and helmet use: Two mediator ordered logit models

Micro-mobility vehicles such as electric bicycles, or e-bikes, are becoming one of the essential transportation modes in metropolitan areas, and most deliveries in large cities are dependent on them. Due to the e-bike's popularity and vulnerability, e-bike crash occurrence has become a major traffic safety problem in many cities across the world; finding the most important human factors affecting e-bike safety has thus been an important recent issue in traffic safety analysis. Since delivery riders are a key group of e-bike users, and since helmet use plays a crucial role in reducing the severity of a crash, this study conducted a city-wide online survey to analyze the helmet usage of 6,941 delivery riders in Shanghai, China. To determine the in-depth mechanisms influencing helmet use and e-bike crash occurrence, including the direct and indirect effects of the relevant factors, two mediator ordered logistic regression models were employed. The mediator ordered logistic model was compared with the traditional logistic regression model, and was found to be superior for modeling indirect as well as direct influencing factors. Results indicate that riders' familiarity with traffic regulations (FTR) is an extremely important variable mediating between the independent variables of riders' educational level and age, and the dependent variables of helmet use and e-bike crashes. Improving riders' FTR can consequently increase helmet use and decrease crash occurrence. Authorities can apply these findings to develop appropriate countermeasures, particularly in legislation and rider training, to improve e-bike safety.

Analysis of safety climate and individual factors affecting bus drivers' crash involvement using a two-level logit model

Although traffic crashes involving buses are less frequent than those involving other vehicle types, the consequences of bus crashes are high due to the potential for multiple injuries and casualties. As driver error is a primary factor affecting bus crashes, driver safety education is one of the main countermeasures used to mitigate crash risk. In China, however, safety education is not as focused as it should be, largely due to the limited research identifying the specific driver behaviors, and potential influences on those behaviors, that are correlated with crashes. The aim of this study is, therefore, to explore the fleet- and driver-level risk factors underlying bus drivers' self-reported crash involvement, including analyzing the effect of psychological distress on the most influential driver-level factors. A survey was conducted of 725 drivers from a large Shanghai bus company, and a random-effects two-level logit model was developed to integrate fleet and individual variables. Results showed that: 1) the fleet-level safety climate explained about 8.5% of the model's variance, indicating it was a valid predictor of self-reported crash involvement; 2) the driver-level factors of drivers' age, seniority, marital status, positive behavior, and driving anger influenced drivers' self-reported crash involvement, but ordinary violations, lapses, aggressive violations, and insomnia were the most influential variables; 3) psychological distress appeared to associate with the high frequency of risky driving behavior and the high severity of driving anger. This study's findings will help bus companies to give more attention to their safety climate and implement more targeted improvements to their driver safety education programs.

Study on the Relationship between Drivers' Personal Characters and Non-Standard Traffic Signs Comprehensibility

Drivers’ incorrect perception and interpretation of the road space are among reasons for human errors. Proper road markings are elements improving perception of road space. Their effectiveness relies on traffic participants receiving the provided information correctly. The range of signs used is constantly expanding and unusual situations in traffic require use of non-standard signs or an unusual combination of existing standard signs. The aim of this study was to explore the level of comprehensibility of four different types of non-standard signs. The relationship between the level of comprehensibility of these signs and personality traits of the drivers was also studied. A total of 369 drivers were tested using a questionnaire to analyze the traffic signs comprehensibility and Five Factor Inventory (NEO-FFI). The obtained results indicate that symbolic signs, unlike symbolic and text ones, are much better comprehended by drivers. Men comprehend the significance of non-standard symbolic regulatory signs better than women. Higher level of comprehensibility of symbolic and text regulatory signs is shown by older, better educated drivers and professional drivers. The study found there is a link between personality traits of the driver and the comprehensibility of symbolic regulatory signs.

Evaluating the impact of Heavy Goods Vehicle driver monitoring and coaching to reduce risky behaviour

Determining the impact of driver-monitoring technologies to improve risky driving behaviours allows stakeholders to understand which aspects of onboard sensors and feedback need enhancement to promote road safety and education. This study investigates the influence of camera monitoring on Heavy Goods Vehicle (HGV) drivers' risky behaviours. We also assess whether monitoring affects individual driving events further when coupled with safe driving practices coaching. We evaluate the outcome of those practices on three telematics incidents heavily reliant on driving errors and violations, i.e., the number of vehicle harsh braking, harsh cornering and over speeding incidents. The objective is to understand how frequently individual incidents caused by risky driving behaviour occur (a) without camera monitoring and without any coaching; (b) after camera installation; and (c) after camera installation and coaching. We investigate two commercial HGV companies (Company 1 and Company 2) with 263 and 269 vehicles, respectively, over a 16 months period, from which the first 8 months contain data collected before the installation of cameras (baseline) and the rest of the dataset contains incident counts after the installation of cameras (intervention). Company 1 provides coaching during the intervention phase while Company 2 does not offer coaching. Our analysis considers the baseline and the intervention phases during the same seasons to eliminate any possible bias due to the influence of weather on driving behaviour. Results show an overall significant reduction in the mean frequency of harsh braking incidents from baseline to intervention by 16.82% in Company 1 and 4.62% in Company 2, and a significant reduction in the mean frequency of over speeding incidents from baseline to intervention by 34.29% in Company 1 and 28.13% in Company 2. Furthermore, the effect of coaching has a significant difference in reducing the frequency of harsh braking (p = .011) and harsh cornering (p < .001) compared to just camera monitoring. These results suggest that coaching interventions are more effective in reducing driving errors while monitoring reduces both driving errors and violations.

Risky driving behaviour in Abu Dhabi, United Arab Emirates: a cross-sectional, survey-based study

Background

Traffic collision fatality rates per mile travelled have declined in Abu Dhabi similar to many developed countries. Nevertheless, the rate is still significantly higher than the average of countries with similar GDP and socio-demographic indicators. The literature on the subject in the UAE is limited especially in the area of studying drivers behaviour. This study aims to find determinants of risky driving behaviours that precipitate having a road traffic collision (RTC) in the United Arab Emirates (UAE).

Methods

A cross-sectional, survey-based study was employed. Participants were 327 active drivers who were attending Abu Dhabi Ambulatory Health Care Services clinics. They were provided with a questionnaire consisting of demography, lifestyle history, medical history, driving history, and an RTC history. They were also given a driving behaviour questionnaire, a distracted driving survey, depression screening and anxiety screening.

Results

Novice drivers (less than 25 years old) were 42% of the sample and 79% were less than 35 years. Those who reported a history of an RTC constituted 39.8% of the sample; nearly half (47.1%) did not wear a seatbelt during the collision. High scores in the driving behaviour questionnaire and high distraction scores were evident in the sample. Most distraction-prone individuals were young (90.5% were less than 36 years old). High scores in the driving behaviour questionnaire were also associated with high distraction scores (p < 0.001). Respondents with high depression risk were more likely to be involved in the RTC. With each one-point increase in the driver’s distraction score, the likelihood of a car crash being reported increased by 4.9%.

Conclusion

Drivers in the UAE engage in risky behaviours and they are highly distracted. Some behaviours that contribute to severe and even fatal injuries in RTCs include failing to wear a seatbelt and being distracted. Younger people were more likely distracted, while older drivers were more likely to have higher depression scores. Depression is suggested as a determinant factor in risky driving. These findings are informative to other countries of similar socioeconomic status to the UAE and to researchers in this field in general.

Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

INTRODUCTION

Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data.

RESULTS

The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.

Experience as a Safety Factor in Driving; Methodological Considerations in a Sample of Bus Drivers

Experience is generally seen as an important factor for safe driving, but the exact size and details of this effect has never been meta-analytically described, despite a fair number of published results. However, the available data is heterogeneous concerning the methods used, which could lead to very different results. Such method effects can be difficult to identify in meta-analysis, and a within-study comparison might yield more reliable results. To test for the difference in effects between some different analytical methods, analyses of data on bus driver experience and crash involvement from a British company were conducted. Effects of within- and between-subjects analysis, non-linearity of effects, and direct and induced exposure methods were compared. Furthermore, changes in the environmental risk were investigated. Between-subject designs yielded smaller effects as compared to within-subjects designs, while non-linearity was not found. The type of exposure control applied had a strong influence on effects, as did differences in overall environmental risk between years. Apparently, “the effect of driving experience” means different things depending upon how calculations have been undertaken, at least for bus drivers. A full meta-analysis, taking several effects of methodology into account, is needed before it can be said that the effect of driving experience on crash involvement is well understood.

Personality Traits and Attitudes Toward Traffic Safety Predict Risky Behavior Across Young, Adult, and Older Drivers

In the last few decades, several studies have investigated the role of personality traits and attitudes toward traffic safety in predicting driving behaviors in diverse types of drivers across several countries. However, to the best of our knowledge, no studies so far have investigated the possible moderating role played by age in relation to predictors of accident risk. Answering this open question would provide information about the generalizability of the model across different subpopulations and would make possible the tailoring of the interventions to specific target groups. The study involved 1,286 drivers from three different age groups (young: n = 435; adult: n = 412; old: n = 439) which completed a questionnaire measuring drivers’ personality traits (i.e., anxiety, hostility, excitement seeking, altruism, normlessness), positive attitudes toward traffic safety, risky driving behaviors (i.e., errors, lapses, and traffic violations), accident involvement and number of traffic fines issued in the last 12 months. Multi-group Variance Based Structural Equation Modeling (VB-SEM) across the three age groups showed that the hypothesized model had a good fit with the data in all the three age groups. However, some pattern of relationships between the variables varied across the three groups, for example, if considering the direct effects of personality traits on risky driving behaviors, anxiety, altruism, and normlessness predicted violations only in young and adult drivers, whereas excitement seeking was associated with lapses only in young drivers; anxiety was a positive predictor of drivers’ errors, both in adult and older drivers, whereas excitement seeking predicted errors in adult and young drivers. On the other hand, attitudes significantly and negatively predicted violations and errors in all the three age groups, whereas they significantly and negatively predicted lapses only in young and older drivers. The results of the present study provided empirical basis to develop evidence-based road safety interventions differently tailored to the specific life’s stage of the drivers.

Validating Driver Behavior and Attitude Measure for Older Italian Drivers and Investigating Their Link to Rare Collision Events

The present study aimed to: (a) validate the factor structures of three scales assessing driving behavior, attitudes toward traffic safety (ATTS) and self-regulation in driving, in a sample of Italian older adults, through confirmatory factor analyses and (b) to determine the effectiveness of these measures in predicting the likelihood and the frequency of collision involvements in the following year. A 28-item driver behavior questionnaire (DBQ), a 16-item ATTS, a 21-item extended driving mobility questionnaire (DMQ-A) were administered to 369 active Italian drivers, aged between 60 and 91 years. Results showed a four-factor structure for the DBQ, a five-factor structure for the ATTS and a two-factor structure for the Extended DMQ-A, as the best fitting models. Hurdle model analysis of count data with extra-zeros showed that all factors of DBQ predicted the likelihood of road collisions. Risky behavior, except for aggressive violations, self-regulation and attitudes toward traffic rules were associated with the frequency of collision involvement. The aforementioned three scales seemed to be a useful and concise suite of instruments assessing risky as well as protective factors of driving behavior in elderly.

Personality of young drivers in Oman: Relationship to risky driving behaviors and crash involvement among Sultan Qaboos University students

OBJECTIVE

Drivers' behaviors such as violations and errors have been demonstrated to predict crash involvement among young Omani drivers. However, there is a dearth of studies linking risky driving behaviors to the personality of young drivers. The aim of the present study was to assess such traits within a sample of young Omani drivers (as measured through the behavioral inhibition system [BIS] and the behavioral activation system [BAS]) and determine links with aberrant driving behaviors and self-reported crash involvement.

METHODS

A cross-sectional study was conducted at the Sultan Qaboos University that targeted all licensed Omani's undergraduate students. A total of 529 randomly selected students completed the self-reported questionnaire that included an assessment of driving behaviors (e.g., Driver Behaviour Questionnaire, DBQ) as well as the BIS/BAS measures.

RESULTS

A total of 237 participants (44.8%) reported involvement in at least one crash since being licensed. Young drivers with lower BIS-Anxiety scores and higher BAS-Fun Seeking tendencies as well as male drivers were more likely to report driving violations. Statistically significant gender differences were observed on all BIS and BAS subscales (except for BAS-Fun) and the DBQ subscales, because males reported higher trait scores. Though personality traits were related to aberrant driving behaviors at the bivariate level, the constructs were not predictive of engaging in violations or errors. Furthermore, consistent with previous research, a supplementary multivariate logistic regression analysis revealed that only driving experience was predictive of crash involvement.

CONCLUSIONS

The findings highlight that though personality traits influence self-reported driving styles (and differ between the genders), the relationship with crash involvement is not as clear. This article further outlines the key findings of the study in regards to understanding core psychological constructs that increase crash risk.

How indicative is a self-reported driving behaviour profile of police registered traffic law offences?

Although most motorised countries have experienced massive improvements in road safety over the last decades, human behaviour and differences in accident risk across sub-groups of drivers remains a key issue in the area of road safety. The identification of risk groups requires the identification of reliable predictors of safe or unsafe driving behaviour. Given this background, the aim of this study was to test whether driver sub-groups identified based on self-reported driving behaviour and skill differed in registered traffic law offences and accidents, and whether group membership was predictive of having traffic law offences. Sub-groups of drivers were identified based on the Driver Behaviour Questionnaire (DBQ) and the Driver Skill Inventory (DSI), while traffic offences and accidents were register-based (Statistics Denmark). The participants (N=3683) were aged 18-84 years and randomly selected from the Danish Driving License Register. Results show that the driver sub-groups differed significantly in registered traffic offences but not in registered accidents. In a logistic regression analysis, the sub-group "Violating unsafe drivers" was found predictive of having a traffic offence, even when socio-demographic variables and exposure were controlled for. The most important predictive factor, however, was having a criminal record for non-traffic offences, while gender, living without a partner, and being self-employed also had a significant effect. The study confirms the use of the DBQ and DSI as suitable instruments for predicting traffic offences while also confirming previous results on accumulation of problematic behaviours across life contexts. The finding that driver sub-groups did not differ in registered accidents supports the recent research activities in finding and modelling surrogate safety measures.

Injury risks in schoolchildren with attention-deficit/hyperactivity or autism spectrum disorder: Results from two school-based health surveys of 6- to 17-year-old children in Sweden

INTRODUCTION

Injuries are one of the leading causes of death and disability among children in Sweden and attention-deficit/hyperactivity disorder (ADHD) has previously been associated with an increased risk of injury in pediatric populations elsewhere in the world. Current evidence regarding the possible link between autism spectrum disorder (ASD) and injury risk appears limited, even though some potentially risk-increasing symptoms overlap. The purpose of this study was thus to study the association between both ADHD and ASD concerning the risk of injury among Swedish schoolchildren.

METHODS

Two samples were used: a population-based register study containing data from 18,416 children ranging from the ages of 6-17years collected by school nurses during 2012/2014 (Survey A), and a national cross-sectional study of 3202 ninth-grade children (~15years old) collected from 92 schools in 2011 (Survey B). The data were analyzed using χ(2)-tests and log-binomial generalized linear models to obtain risk ratios (RR), comparing cases reportedly affected by ADHD or ASD to unaffected controls.

RESULTS

After adjusting for confounders, ADHD was associated with a 65% increased risk of injury (RR 1.65 [95% CI: 1.32-2.05] in Survey A, and a 57% increased risk of injury (RR 1.57 [95% CI: 1.27-1.95]) in Survey B. ASD was not significantly associated with any differences in injury risk (RR 0.81 [95% CI: 0.57-1.14]).

CONCLUSIONS

The results indicate that there is an elevated injury risk among Swedish schoolchildren with ADHD but not for children with ASD. Future studies should focus on causal mechanisms mediating the association between ADHD and injuries in order to facilitate injury prevention strategies.

PRACTICAL APPLICATIONS

Parents and teachers of schoolchildren with ADHD should be made aware of the elevated injury risks associated with the diagnosis. Safety experts and injury control professionals should consider the development of specialized prevention strategies in order to reduce these risks.

Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data

Background

Traffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes.

Methodology and Principal Findings

Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean.

Significance

The effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.