Dimensions of driving anger and their relationships with aberrant driving

Causal factors identification and dynamics simulation of major road traffic accidents from China's evidence: A high-order mixed-method design

Mitigating the injury and severity of road traffic accidents has become a crucial objective in global road safety efforts. Major road traffic accidents (MRTAs) pose significant challenges due to their high hazard and severe consequences. Despite their widespread impact, the complex causation mechanisms behind MRTAs have not been thoroughly and systematically investigated, which hinders the development of effective control strategies and policies. This study introduces an innovative high-order embedded mixed-method design to explore the causes of MRTAs, marking the first application of mixed-method approaches in road traffic accident research. The proposed approach consists of three phases: First, qualitative analysis utilizing grounded theory examines 95 MRTAs investigation reports to identify causal factors, establish a classification framework, and derive quantitative data. The second phase employs the decision experiment and evaluation laboratory (DEMATEL) for static quantitative analysis, quantifying interactions within the classification framework, and generating cause-effect diagrams. Finally, data and results from the first two phases are integrated to construct a system dynamics (SD) model and conduct sensitivity analysis, analyzing the impact of causal factors and their interactions on MRTAs casualties, thereby evaluating the effectiveness of various control strategies. The findings reveal that the causal factors of MRTAs can be categorized into five levels: "driver errors," "vehicle, road and environment," "supervisory deficiencies," "organizational management and culture," and "outside factors." Complex interactions exist both among and within these levels, collectively influencing MRTAs. Moreover, in reducing MRTAs casualties, combined control strategies demonstrate significant superiority over single control strategies, especially when targeting key factors. It should also be noted that the importance ranking of causal factors dynamically adjusts with changes in the control environment, and the effectiveness of combined control strategies becomes more pronounced as the number of control factors increases. Specifically, comprehensive prevention strategies across all five levels exhibit the most remarkable efficacy. In conclusion, preventing MRTAs requires emphasizing the shared responsibility of all stakeholders and judiciously allocating control resources, while avoiding excessive reliance on interventions targeting any specific factor. This study provides a methodological foundation for a deeper understanding of the causation mechanisms behind MRTAs, and its results offer robust evidence to support the formulation of future prevention measures and policies.

An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model

OBJECTIVE

In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety.

METHODS

On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model.

RESULTS

In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%.

CONCLUSIONS

The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.

The road safety and risky behavior analysis of delivery vehicle drivers in China

The delivery industry has seen dramatic growth in demand and scale in China. Due to the stock limitations and delivery time restrictions, the couriers may commit traffic violations while delivering, resulting in a pessimistic road safety situation. This study aims to reveal critical factors that influence delivery vehicle crash risks. A cross-sectional structured questionnaire survey is conducted to collect demographic attributes, workload, work emotions, risky driving behavior, and road crash involvement data among 824 couriers in three developed regions of China. The collected data is then analyzed through an established path model to identify the contributing factors of delivery road crash risks and risky behaviors. The road crash risk level (RCRL) indicator is defined by taking into consideration both frequency and severity. While the risky behaviors are defined by both their frequency and correlations to crash risks. The results indicate that 1) Beijing-Tianjin Urban Agglomeration has the highest road crash frequency and RCRL; 2) distracted driving and wrong-lane-use are among the top three risky behaviors for both Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration. For Beijing-Tianjin Urban Agglomeration, distracted driving, aggressive driving, and lack of protection are the top three risky behaviors; 3) time demand and personal efforts are important factors contributing to the cognitive workload of couriers; 4) objective workload can affect the cognitive workload and both workloads influence drivers' emotions (anxiety and anger); 5) the objective, cognitive workload, drivers' emotions influence the RCRL through their impacts on risky behavior but in different paths for three agglomerations. The findings highlight the importance of developing targeted countermeasures to reduce the delivery workers' workload, improve their performance on roads, and mitigate severe crash risks.

Aberrant driving behaviors as mediators in the relationship between driving anger patterns and crashes among taxi drivers: An investigation in a complex cultural context

OBJECTIVE

Taxis have become an integrated component of Qatar's urban transportation network due to their convenience, comfort, and flexibility. Qatar has seen an uptick in the demand for professional taxi drivers. Most Qatari taxi drivers come from developing countries with poor awareness of road safety; therefore, they regularly engage in aberrant driving behavior, leading to traffic violations and crashes. For taxi rides to be safer, it is essential to determine the association between driving aberration and road traffic crashes (RTCs), with an emphasis on the underlying factors that trigger these behaviors.

METHODS

To this end, we collected the data from taxi drivers relying on standard questionnaires, namely the Driving Anger Scale (DAS) and the Driver Behavior Questionnaire (DBQ), together with the real crash data of the same taxi drivers obtained from the police department. We relied on factor analysis to identify the main factors of these tools and then structural equation modeling to predict their causal relationship with RTCs.

RESULTS

The results indicated that the component of DAS, namely "illegal driving", triggered all dimensions of aberrant driving behaviors, whereas hostile gestures had a positive correlation with lapses. In addition, the factor "error" was identified as a significant direct predictor, while the factor "illegal driving" was identified as a significant indirect predictor for RTCs. Regarding demographic characteristics, professional driving experience was found to be negatively associated with RTCs.

CONCLUSION

Driving aberration mediated the impact of driving anger on RTCs. The findings from this study could help road safety practitioners and researchers better understand these relations. In addition, these results could also be very helpful for driving instructors to train taxi drivers in a way to cope with provoking situations.

Driving anger dimensions and their relationship with aberrant driver behavior in Lebanon: Results from a national self-reported survey

Driving anger may vary across countries due to culture. This might affect driver behavior, which, in turn, impacts the driving outcomes. This study aims to investigate the relationship between socio-demographic variables, driving anger, and the self-reported aberrant behavior among Lebanese drivers and to determine which anger dimension is linked to driving behavior. A cross-sectional study was conducted among eligible Lebanese drivers from all Lebanese governorates. Data were collected using an anonymous Arabic self-reported questionnaire that included demographic information, driving-related variables, and two scales: the Driver Behavior Questionnaire (DBQ) and the Driver Anger Scale (DAS). Four hierarchical regressions were performed taking the DBQ subscales as the dependent variable and the DAS subscales as independent variables. Out of 1102 surveyed drivers, 68.4% were males, having a mean age of 34.6 ± 12.3 years and an average driving experience of 13.5 ± 10.8 years. DBQ, DAS, and their subscales showed good reliability. Older age and female gender were negatively associated with the tendency of committing aggressive violations. However, being a professional driver and increasing annual mileage were positively associated with a higher tendency to commit aggressive violations. In addition to these factors, a higher educational level was found associated with a lower risk of driver's involvement in traffic violations. However, increased driving experience was associated with a higher tendency to commit aggressive violations. Reported driving errors were also found positively associated with older age, increasing mileage, and being a professional driver. However, larger driving experience and higher education were found protectors from erroneous behavior. Hierarchical regression showed that anger prompted by hostile gesture, discourtesy, police presence, traffic obstruction, and slow driving were positively associated with aggressive violations. All the DAS subscales were found to be positively associated with ordinary violations. traffic obstruction was also found associated with a higher tendency of drivers to commit lapses, as well as anger, which originated from police presence and slow driving which were found also positively associated with errors. Driver anger dimensions were found positively associated with aberrant driver behavior. To overcome road anger, there is a need to train drivers on coping strategies to restrain aberrant driving behavior.

The relationship between self-reported ability emotional intelligence and risky driving behaviour: Consequences for accident and traffic ticket rate

Road safety represents one of the main public health issues worldwide, and risky driving behaviour is one of the most predominant factors in traffic road accidents. The primary objective of this research was to clarify the relationship between emotional intelligence (EI) abilities and the probability of engaging in risky behaviour during driving. Previous literature linking these constructs is limited, and research has yielded mixed findings. In the present study, 555 drivers from a Spanish community sample (M_age = 39.34, ranging from 18 to 79 years old; 49.19% women) were assessed on risky driving behaviour using the Dula Dangerous Driving Index while self-reported ability EI was measured using the Wong and Law Emotional Intelligence Scale. Gender, age, and driving experience were controlled. The results of this study revealed that a higher self-reported ability EI, particularly the ability to regulate emotions, was related to a lower tendency to engage in risky driving behaviours. In turn, self-reported ability EI was negatively and indirectly related to the number of road accidents and traffic tickets through the mediating effect of risky driving. The regulation of emotions (via direct and indirect effect) and the appraisal of the emotions of others (via direct effect) were the EI abilities that better predicted the number of accidents and traffic tickets. We discuss the practical implications of these findings, along with suggested future lines of research.

Driver's Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks

The visual attention system is the gateway to the human information processing system, and emotion is an important part of the human perceptual system. In this paper, the driver's visual attention characteristics and the influences of typical driving emotions on those were explored through analyzing driver's fixation time and identification accuracy to different visual cognitive tasks during driving. The results showed that: the increasing complexity of the cognitive object led to the improvement of visual identification speed. The memory and recall process increased drivers' fixation time to cognitive objects, and the recall accuracy decreased with the increase in time interval. The increase in the number of cognitive objects resulted in the driver improving the visual identification speed for the cognitive object at the end of the sequence consciously. The results also showed that: the visual cognitive efficiency was improved in the emotional states of anger and contempt, and was decreased in the emotional states of surprise, fear, anxiety, helplessness and pleasure, and the emotional state of relief had no significant effect on the visual cognitive efficiency. The findings reveal the driver's visual information processing mechanism to a certain extent, which are of great significance to understand the inner micro-psychology of driver's cognition.

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

Purpose

Researches addressing driving behaviors have not fully revealed how emotions affect risky driving behaviors and tend to focus on the effects of some negative emotions rather than those of more specific emotions. This study aimed to test the potential moderating effects of eight common driving emotions on the relationship between self-reported individual traits (sensation seeking and driving style) and actual risky driving behaviors, sequentially providing some implications for the risky driving behavior prevention.

Participants and Methods

A total of 78 licensed drivers were recruited from undergraduate students, company employees and taxi drivers in China. The participants’ data on self-reported driving style (SDBS) and self-reported sensation seeking (SSSS) were obtained through questionnaires. The participants’ data on actual risky driving behaviors (ARD) in eight driving emotional activation states were obtained through a series of emotion induction experiments and driving experiments. The Structural Equation Modeling (SEM) and moderating effect tests were employed to investigate the relationships of driving emotions, SDBS, SSSS and ARD.

Results

Results showed that anger and pleasure affected risky driving behaviors positively by enhancing the relationship between SDBS and ARD, while surprise and fear were negatively related to risky driving behaviors by weakening this relationship. Anxiety positively affected risky driving behaviors by synchronously enhancing the relationship between SDBS and ARD and the relationship between SSSS and ARD, while helplessness and relief affected risky driving behaviors negatively by weakening the two relationships. Contempt affected risky driving behaviors positively by enhancing the relation between SSSS and ARD.

Conclusion

The results illustrated the effects of different emotions on risky driving behaviors, and also partly explained the reasons for these effects. This research provided a source of reference for reducing traffic accidents caused by risky driving behaviors.

Relating Reactive and Proactive Aggression to Trait Driving Anger in Young and Adult Males: A Pilot Study Using Explicit and Implicit Measures

Driving anger and aggressive driving are main contributors to crashes, especially among young males. Trait driving anger is context-specific and unique from other forms of anger. It is necessary to understand the mechanisms of trait driving anger to develop targeted interventions. Although literature conceptually distinguished reactive and proactive aggression, this distinction is uncommon in driving research. Similar, cognitive biases related to driving anger, measured by a combination of explicit and implicit measures, received little attention. This pilot study related explicit and implicit measures associated with reactive and proactive aggression to trait driving anger, while considering age. The sample consisted of 42 male drivers. The implicit measures included a self-aggression association (i.e., Single-Target Implicit Association Test) and an attentional aggression bias (i.e., Emotional Stroop Task). Reactive aggression related positively with trait driving anger. Moreover, a self-aggression association negatively related to trait driving anger. Finally, an interaction effect for age suggested that only in young male drivers, higher proactive aggression related to lower trait driving anger. These preliminary results motivate further attention to the combination of explicit and implicit measures related to reactive and proactive aggression in trait driving anger research.

Regulating Road Rage

Road rage has been a problem since the advent of cars. Given the ubiquity of road rage, and its potentially devastating consequences, understanding road rage and developing interventions to curb it are important priorities. Emerging theoretical and empirical advances in the study of emotion and emotion regulation have provided new insights into why people develop road rage and how it can be prevented and treated. In the current article, we suggest an integrative conceptual framework for understanding road rage, based upon a psychological analysis of emotion and emotion regulation. We begin by defining road rage and other key constructs. We then consider the interplay between road rage generation and road rage regulation. Using an emotion regulation framework, we describe key points at which emotion-regulation difficulties can lead to road rage, followed by strategies that may alleviate these difficulties. We suggest that this framework usefully organizes existing research on road rage, while exposing key directions for future research.

Predicting Errors, Violations, and Safety Participation Behavior at Nuclear Power Plants

Commissioning workers at nuclear power plants have long been ignored in previous studies, although their performance is closely related to the overall safety of plants. This study aimed to explain and predict three types of behavior, i.e., errors, violations, and safety participation, of commissioning workers, under the general framework of the theory of planned behavior (TPB) and by considering organization and planning factors. The validity of the model was evaluated with a sample of 167 commissioning workers who completed a self-reported questionnaire. The results showed that perceived behavioral control, along with organization and planning, significantly affected all types of behavior. It was also found that violations and errors were a direct result of attitude. Besides, errors were predicted by subjective norm; unexpectedly, this occurred in a positive way. These findings revealed the underlying mechanisms for the development of errors, violations, and safety participation among commissioning workers and provided practical implications for safety improvement at the commissioning workplace.

Ready to bully automated vehicles on public roads?

Automated vehicles (AVs), the wide adoption of which is expected to improve traffic safety significantly, are penetrating our roads. The AVs that are testing on public roads have been bullied by human road users. We are not sure whether the bullying incidents are isolated or will be common in the future. In a cross-national survey (N = 998 drivers in China and South Korea), we developed an eleven-item bullying intention questionnaire. We assumed and confirmed that, overall, participants had a greater intention to bully machine drivers than to bully other human drivers. Compared to the Korean participants, the Chinese participants reported a greater intention to drive aggressively. The correlations of their intention to bully AVs with their attitude toward AVs and with risk-benefit perception of AVs were weak. Male participants (vs. female participants) and younger participants (vs. older participants) reported a greater intention to drive aggressively. Drivers' aggressive behaviors toward AVs might be common in the future, which might increase traffic risk and hinder the implementation of this technology.

Personality and Behavioral Predictors of Cyclist Involvement in Crash-Related Conditions

In recent years, the increasing rate of road crashes involving cyclists with a disproportionate overrepresentation in injury statistics has become a major concern in road safety and public health. However, much remains unknown about factors contributing to cyclists’ high crash rates, especially those related to personal characteristics. This study aims to explore the influence of cyclist personality traits and cycling behaviors on their road safety outcomes using a mediated model combining these constructs. A total of 628 cyclists completed an online questionnaire consisting of questions related to cycling anger, impulsiveness, normlessness, sensation seeking, risky cycling behaviors, and involvement in crash-related conditions in the past year. After the psychometric properties of the employed scales were examined, the relationships among the tested constructs were investigated using structural equation modeling. The results showed that cyclists’ crash risks were directly predicted by risky cycling behaviors and cycling anger, and the effects of cycling anger, impulsiveness, as well as normlessness on crash risks, were mediated by cycling behaviors. The current findings provide insight into the importance of personality traits in impacting cycling safety and could facilitate the development of evidence-based prevention and promotion strategies targeting cyclists in China.

Multigroup invariance of the DAS across a random and an internet-sourced sample

It is well established that angry and, subsequently, aggressive drivers pose a problem for road safety. Over recent years, there has been an increase in the number of published studies examining driver anger, particularly using the Driving Anger Scale (DAS). The DAS measures six broad types of situations likely to provoke anger while driving (i.e., police presence, illegal driving, discourtesy, traffic obstructions, slower drivers, and hostile gestures). The majority of the recent studies have moved away from traditional paper-and-pencil methodologies, using the internet to collect data, for reasons of convenience. However, it is not yet completely clear whether data obtained from this methodology differs from more traditional methods. While research outside of the driving arena has not found substantial differences, it is important to establish whether this also applies to driving-related research and measures, such as the DAS. The present study used Multigroup Confirmatory Factor Analysis (MGCFA) to investigate the invariance of the DAS across a random sample from the electoral roll (n = 1,081: males = 45%) and an internet sourced sample (n = 627; males = 55%). The MGCFA showed the same six-factor solution was supported in both datasets. The relationships between the DAS factors and age, sex, trait anger, and annual mileage were broadly similar, although more significant differences were identified in the internet sample. This research demonstrates that driving measures administered over the internet produce similar results to those obtained using more traditional methods.

Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model

Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015-2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.

Trait driving anger and driving styles among Colombian professional drivers

This study analyzes the association between trait driving anger and driving styles in a sample of Colombian professional drivers. Additionally, the internal and external validity of the Deffenbacher's Driving Anger Scale (DAS-14) was examined in the study population. The DAS-14 and the Spanish Version of the Multidimensional Driving Style Inventory (S-MDSI) were administered to 492 urban bus and taxi operators. Average trait driving anger scores in the study population were similar to those reported in previous validation studies from Spain, Argentina, China, and Malaysia. After deleting three cross-loaded items, confirmatory factor analyses revealed a three-dimensional latent structure for the DAS-14, similar but not equal to the previous Spanish speaking validations. This factorial structure fits the data reasonably well. Finally, linear regression analyses revealed that the three factors of the DAS-14 (impeded progress by others, illegal driving, and direct hostility) significantly predict adaptive and maladaptive driving styles. Overall, the results of this study suggest that the DAS-14 is a reasonably reliable measure of driving anger traits among professional drivers, and it also provides relevant insights for the prevention of risky driving styles in this occupational group.

German bike messengers' experiences and expressions of cycling anger

Objective: The present study has 3 objectives: First, we wanted to examine whether the Cycling Anger Scale (CAS) applies to German professional bike messengers, because this scale was previously developed with nonprofessional cyclists in Germany. Second, we wanted to look at possible differences in cycling anger experience and expression between professional German bike messengers and nonprofessional German cyclists. Third, we explored whether cycling anger is somehow related to driving anger and general anger. Methods: We applied German versions of the CAS, the Driving Anger Scale (DAS), and the State-Trait Anger Expression Inventory (STAXI) to a sample of 123 professional German bike messengers. Then we compared their ratings with the results of 421 nonprofessional German cyclists. Results: Regarding our first objective, results indicate that the CAS model fit is better for nonprofessional than for professional cyclists. However, the CAS in a slightly modified version can be used for professional cyclists as well. As for our second objective, we show that professional cyclists experience significantly less cycling anger than nonprofessional cyclists. However, bike messengers report more frequent aggressive cycling behaviors when angry, indicating a weaker link between trait anger while cycling and aggressive behavior among professionals. Thirdly, we found relations between cycling anger, driving anger, and general anger. Conclusions: We conclude that the CAS in its slightly modified 13-item version with the established 4 subscales produces an acceptable model fit and can be applied to professional German bike messengers for further research purposes or applied issues; for example, traffic education or self-awareness in terms of accident prevention behaviors. In addition, for professional cyclists, less anger does not result in less aggressive cycling behaviors. Subsequent research should explore the role of anger in behavioral regulation of cyclists' unsafe cycling behaviors taking different levels of experience and professionalism into account in order to reduce adverse effects of anger on traffic safety.

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.

Dimensions of driving-related emotions and behaviors: An exploratory factor analysis of common self-report measures

OBJECTIVE

A wide variety of driving self-report measures are purported to assess drivers' behaviors and emotions. However, little is known about the underlying factor structure of these measures. This study examined the factor structure of several self-report measures frequently utilized in the assessment of driving-related behaviors and emotions.

DESIGN

Cohort survey in a large sample (n = 287) of young adults (mean age = 19.91 years, SD = 1.65).

RESULTS

Exploratory factor analysis revealed a four-factor structure that included reckless driving behaviors, negative driving-related emotions, aggressive driving behaviors in response to perceived transgressions from other drivers, and perceived aggressive driving behaviors from other drivers. Aggressive driving behaviors not performed in response to other drivers loaded onto both aggressive driving-related factors.

CONCLUSIONS

The factor structure derived in the present study suggests considerable overlap in the content across commonly administered driving self-reports, while also suggesting four distinct dimensions of self-reported driving emotions and behaviors. Whereas some of these dimensions have been explored considerably in the literature (e.g., negative emotions), others deserve further exploration (e.g., perceived aggressive driving behaviors from other drivers). Implications for clinical practice and future investigations are discussed.

Self-reported violations, errors and lapses for older drivers: Measuring the change in frequency of aberrant driving behaviours across five time-points

The current study aimed to: 1. to confirm the 21-item, three-factor Driver Behaviour Questionnaire (DBQ) structure suggested by Koppel et al. (2018) within an independent sample of Canadian older drivers; 2. to examine whether the structure of the DBQ remained stable over a four-year period; 3. to conduct a latent growth analysis to determine whether older drivers' DBQ scores changed across time. Five hundred and sixty Canadian older drivers (males = 61.3%) from the Candrive/Ozcandrive longitudinal study completed the DBQ yearly for four years across five time-points that were approximately 12 months apart. In Year 1, the average age of the older drivers was 76.0 years (SD = 4.5 years; Range = 70-92 years). Findings from the study support the 21-item, three-factor DBQ structure suggested by Koppel and colleagues for an Australian sample of older drivers as being acceptable in an independent sample of Canadian older drivers. In addition, Canadian older drivers' responses to this version of the DBQ were stable across the five time-points. More specifically, there was very little change in older drivers' self-reported violations, and no significant change for self-reported errors or lapses. The findings from the current study add further support for this version of the DBQ as being a suitable tool for examining self-reported aberrant driving behaviours in older drivers. Future research should investigate the relationship between older drivers' self-reported aberrant driving behaviours and their performance on functional measures, their responses to other driving-related abilities and practice scales and/or questionnaires, as well their usual (or naturalistic) driving practices and/or performance on on-road driving tasks.

Driving Anger, Aberrant Driving Behaviors, and Road Crash Risk: Testing of a Mediated Model

With the dramatic increase in motorization, road traffic crashes have become the leading cause of death in China. To reduce the losses associated with road safety problems, it is important to understand the risk factors contributing to the high crash rate among Chinese drivers. This study investigated how driving anger and aberrant driving behaviors are related to crash risk by proposing and testing one mediated model. In this model, the effects of driving anger on road crash risk were mediated by aberrant driving behaviors. However, unlike previous studies, instead of using the overall scale scores, the subscales of driving anger and aberrant driving behaviors were used to establish the mediated model in this study. To test the validity of this model, an Internet-based questionnaire, which included various measures of driving anger, aberrant driving, and road crash history, was completed by a sample of 1974 Chinese drivers. The results showed that the model fitted the data very well and aberrant driving behaviors fully mediated the effects of driving anger on road crash risk. Findings from the present study are useful for the development of countermeasures to reduce road traffic crashes in China.

Which drivers are at risk? Factors that determine the profile of the reoffender driver

Finding appropriate assessment tools to predict recidivism is a difficult aim, which may lead to actions with unintended consequences. Aims don't have consequences. At times, the research has been used to justify penalising reoffenders with punitive measures rather than treating them with effective psychological interventions. This study aims to contribute to untangling and assessing the potential predictors of reoffender drivers. In this study, 296 drivers: 86 reoffenders (7 women and 79 men) and 206 non-reoffenders (105 women and 101 men) responded to a battery of assessment questionnaires in which they were asked for demographic data (i.e. gender and age), alcohol consumption habits, driving styles, general estimation of risk in everyday life, sensitivity to reward and punishment and anger while driving. The results provided a logistical regression model capable of predicting reoffending and explaining 34% of variability, successfully classifying 77.6% of participants. In this model, the best predictor of reoffending is higher consumption of alcohol (Alcohol Use Disorders, AUD), followed by incautious driving (since cautious driving style correlates negatively with reoffending) and to a lesser extent, infraestimation of recreational risk and a greater sensitivity to reward. Relying on results to predict recidivism could be important to plan better interventions to prevent it.

Work Environment, Stress, and Driving Anger: A Structural Equation Model for Predicting Traffic Sanctions of Public Transport Drivers

Public transport is an effective and sustainable alternative to private vehicle usage, also helping to reduce the environmental impact of driving. However, the work environment of public transport operators is full of adverse conditions, which, together with their high mileage, may increase the occurrence of negative safety outcomes such as traffic accidents, often preceded by risky road behaviors enhanced by stress, anger, and difficult operating conditions. The aims of this study were, first, to determine the association between work-related psychosocial factors and individual characteristics of public transport drivers and the rate of traffic sanctions they are subject to; and second, to assess the mediation of driving anger in this relationship. A sample of professional drivers (57.4% city bus, 17.6% taxi, and 25% inter-urban bus male operators) was used for this cross-sectional study, responding to a five-section survey including demographic data and driving-related factors, psychosocial work factors including job stress, driving stress, risk predisposition, and driving anger. The results of this study showed significant associations between work-related factors: measures of stress and self-reported rates of traffic fines. Second, it was found that driving anger mediates the associations between driving stress, risk predisposition, and traffic sanctions; and partially mediates the association between driving experience, hourly intensity, and job stress. This study supports the idea that traffic penalties reported by public transport rates are preceded by work-related, personality, and other individual factors that, when combined with driving anger, enhance the occurrence of road misbehavior that may affect overall road safety.

Analysis of risk factors affecting driver injury and crash injury with drivers under the influence of alcohol (DUI) and non-DUI

OBJECTIVE

The objective of this research was to study risk factors that significantly influence the severity of crashes for drivers both under and not under the influence of alcohol.

METHODS

Ordinal logistic regression was applied to analyze a crash data set involving drivers under and not under the influence of alcohol in China from January 2011 to December 2014.

RESULTS

Four risk factors were found to be significantly associated with the severity of driver injury, including crash partner and intersection type. Age group was found to be significantly associated with the severity of crashes involving drivers under the influence of alcohol. Crash partner, intersection type, lighting conditions, gender, and time of day were found to be significantly associated with severe driver injuries, the last of which was also significantly associated with severe crashes involving drivers not under the influence of alcohol.

CONCLUSIONS

This study found that pedestrian involvement decreases the odds of severe driver injury when a driver is under the influence of alcohol, with a relative risk of 0.05 compared to the vehicle-to-vehicle group. The odds of severe driver injury at T-intersections were higher than those for traveling along straight roads. Age was shown to be an important factor, with drivers 50-60 years of age having higher odds of being involved in severe crashes compared to 20- to 30-year-olds when the driver was under the influence of alcohol. When the driver was not under the influence of alcohol, drivers suffered more severe injuries between midnight and early morning compared to early nighttime. The vehicle-to-motorcycle and vehicle-to-pedestrian groups experienced less severe driver injuries, and vehicle collisions with fixed objects exhibited higher odds of severe driver injury than did vehicle-to-vehicle impacts. The odds of severe driver injury at cross intersections were 0.29 compared to travel along straight roads. The odds of severe driver injury when street lighting was not available at night were 3.20 compared to daylight. The study indicated that female drivers are more likely to experience severe injury than male drivers when not under the influence of alcohol. Crashes between midnight and early morning exhibited higher odds of severe injury compared to those occurring at other times of day. The identification of risk factors and a discussion on the odds ratio between levels of the impact of the driver injury and crash severity may benefit road safety stakeholders when developing initiatives to reduce the severity of crashes.

The multidimensional driving style inventory a decade later: Review of the literature and re-evaluation of the scale

The Multidimensional Driving Style Inventory (MDSI; Taubman - Ben-Ari, Mikulincer, & Gillath, 2004a), a self-report questionnaire assessing four broad driving styles, has been in use for the last ten years. During that time, numerous studies have explored the associations between the MDSI factors and sociodemographic and driving-related variables. The current paper employs two large data sets to summarize the accumulated knowledge, examining MDSI factors in samples of young drivers aged 17-21 (Study 1, n=1436) and older drivers aged 22-84 (Study 2, n=3409). Findings indicate that driving-related indicators are coherently and systematically related to the four driving styles in the expected directions, revalidating the structure of the MDSI. The results also help clarify the relationships between the driving styles and variables such as gender, ethnicity, car ownership, age, and experience, and suggest that driving styles are largely unaffected by sociodemographic characteristics, except for gender and ethnicity, and appear to represent a relatively stable and universal trait. The two studies highlight the validity and reliability of the MDSI, attesting to its practical value as a tool for purposes of research, evaluation, and intervention.

Dimensions of driving anger and their relationships with aberrant driving

The purpose of this study was to investigate the relationship between driving anger and aberrant driving behaviours. An internet-based questionnaire survey was administered to a sample of Chinese drivers, with driving anger measured by a 14-item short Driving Anger Scale (DAS) and the aberrant driving behaviours measured by a 23-item Driver Behaviour Questionnaire (DBQ). The results of Confirmatory Factor Analysis demonstrated that the three-factor model (hostile gesture, arrival-blocking and safety-blocking) of the DAS fitted the driving anger data well. The Exploratory Factor Analysis on DBQ data differentiated four types of aberrant driving, viz. emotional violation, error, deliberate violation and maintaining progress violation. For the anger-aberration relation, it was found that only "arrival-blocking" anger was a significant positive predictor for all four types of aberrant driving behaviours. The "safety-blocking" anger revealed a negative impact on deliberate violations, a finding different from previously established positive anger-aberration relation. These results suggest that drivers with different patterns of driving anger would show different behavioural tendencies and as a result intervention strategies may be differentially effective for drivers of different profiles.