Predictors of women's aggressive driving behavior

Predictors of women's aggressive driving behavior were explored in a study involving 256 female motorists. Sex role orientation, dispositional aggressiveness, age, and annual mileage were measured as independent variables, and aggressive driving behavior was included as the dependent variable. Stepwise hierarchical regression analysis showed that age was negatively related to driving aggression, whereas annual mileage had a positive relationship with driving aggression. Dispositional aggressiveness was a significant predictor of driving aggression. Of the two components of sex role orientation, only femininity was associated with driving aggression, with higher femininity scores predicting lower aggressive driving scores. Masculinity failed to predict aggressive driving, as did the interaction of masculinity and femininity. In combination, the predictors explained 29% of the variance in women's aggressive driving. The results are discussed with respect to the role of dispositional variables as predictors of driving aggression in women. Aggress. Behav. 00:1–10, 2005. © 2005 Wiley‐Liss, Inc.     

Emotional intelligence and risky driving behaviour in adults

BackgroundRisky driving is considered a key predictive factor in road traffic accidents resulting in morbidity and mortality. Intra-individual emotional and personality factors have been shown to influence risk-taking behaviours among drivers. Despite this, there is limited research investigating the individual dimensions of these constructs and their relationship to risky driving behaviour (RDB). The current study therefore aimed to assess whether the individual dimensions of Emotional Intelligence (EI) were implicated in RDB.MethodsThe sample comprised 179 adults (55% male) aged between 18 and 64 years (M = 29.85, SD = 11.46) and who currently held a valid driver’s licence completed an online survey. Emotional Intelligence was assessed via self-report using the Swinburne University Emotional Intelligence Test (SUEIT), and RDB was measured using both the Brief Distracted Driving Scale (BDDS) and the Dula Dangerous Driving Index (DDDI).ResultsRegression analyses revealed that ‘Risky Driving’ was related to greater levels of Emotional Recognition and Expression and lesser Age [F_{(6, 172)} = 2.27, p < 0.05: R² = 7.3%], and the Negative Emotions sub-scale of DDDI, was significantly predicted by Emotional Control and Age [F_{(6, 172)} = 6.41, p < 0.05: R² = 18.3%]. A mediation model incorporating Age, Emotional Control scores and the Negative Emotions driving behaviour score indicated that a significant indirect effect of Age through Emotional Control (K² = 0.08, 95% CI [−0.02, −0.03]). (H [3] = 10.98, p = 0.012).ConclusionsLower scores on specific indices of EI are associated with increased rates of RDB, suggesting that poor emotional control may impede an individuals’ ability to make safe behavioural decisions when driving. The effect sizes for these models were small, however, and further research is needed to explore the contributory components in this association. Greater awareness of the role of emotional regulation and driving behaviours may be useful in preventing RDB in adults.     

汽车驾驶员攻击性驾驶行为的研究

攻击性驾驶行为影响着交通运输的安全。本文简要叙述了攻击性驾驶行为操作性定义的有关研究 ,以及导致该行为的年龄、性别、人格特质、情绪等个人因素和社会环境因素。     

Predicting aggressive driving behavior: The role of macho personality,age, and power of car

A study is presented that explored the role of macho personality, age, and power of car as predictors of aggressive driving behavior. A total of 154 male motorists completed two subscales of the Hypermasculinity Inventory as a measure of macho personality and self‐reports of aggressive driving behavior based on the Driver Behaviour Questionnaire. They also provided information about their age, annual mileage, horsepower of their car, and features that had guided their choice of a car. A multiple regression analysis showed that each of the predictors was significantly related to aggressive driving: Younger motorists, those driving powerful cars, and those showing macho personality were significantly more likely to report driving aggression. Together, the three predictors accounted for 35.8% of the variance. In addition, macho men assigned greater importance to speed and sportiness of a car and less importance to safety aspects than nonmacho men. The findings are discussed with reference to individual and situational explanations of driving aggression. Aggr. Behav. 28:21–29, 2002. © 2002 Wiley‐Liss, Inc.     

Combined reasoning by automated cooperation

Different reasoning systems have different strengths and weaknesses, and often it is useful to combine these systems to gain as much as possible from their strengths and retain as little as possible from their weaknesses. Of particular interest is the integration of first-order and higher-order techniques. First-order reasoning systems, on the one hand, have reached considerable strength in some niches, but in many areas of mathematics they still cannot reliably solve relatively simple problems, for example, when reasoning about sets, relations, or functions. Higher-order reasoning systems, on the other hand, can solve problems of this kind automatically. But the complexity inherent in their calculi prevents them from solving a whole range of problems. However, while many problems cannot be solved by any one system alone, they can be solved by a combination of these systems.We present a general agent-based methodology for integrating different reasoning systems. It provides a generic integration framework which facilitates the cooperation between diverse reasoners, but can also be refined to enable more efficient, specialist integrations. We empirically evaluate its usefulness, effectiveness and efficiency by case studies involving the integration of first-order and higher-order automated theorem provers, computer algebra systems, and model generators.     

Can organisational safety climate and occupational stress predict work-related driver fatigue?

Road crashes are a significant cause of work-related injury and death. Driver fatigue is thought to cause 20–30% of fatal crashes. The current study utilised a survey to examine the relationship between safety climate, occupational stress and work-related driver fatigue. Drivers (n = 219) from two government organisations responded to items from the job-related tension scale [Kahn, R. L., Wolfe, D. M., Quinn, R. P., & Snoek, J. D. (1964). Organisational stress: Studies in role conflict and ambiguity. Malabar, FL: Krieger Publishing], safety climate questionnaire [Glendon, A., & Litherland, D. (2001). Safety climate factors, group differences and safety behaviour in road construction. Safety Science, 39, 157–188] and purpose-designed items on fatigue-related behaviour. Outcome measures were current self-reported, fatigue-related behaviour and self-reported ‘near (crash) misses’ during the previous 6 months. Together, occupational stress and safety climate predicted fatigue-related behaviour, accounting for 29% of the variance over and above that explained by control variables. Further, logistic regression revealed occupational stress and safety climate to be significant predictors of fatigue-related near misses. Safety climate emerged as a stronger predictor of both fatigue-related behaviour and near misses than occupational stress. Results suggest that organisations can play a part in improving the safety-related behaviours of their workforce through attention to safety climate and occupational stress.     

Prediction of driving ability after stroke and the effect of behind-the-wheel training

The aim of this study was to examine the predictive value of a neuropsychological test battery relating to an on-the-road driving evaluation and to determine whether patients who failed the driving test could improve their driving through behind-the-wheel training. Thirty-four stroke patients were compared with 20 healthy, matched controls. Patients who failed the driving test were offered driving practice at a driving school and were then reassessed (neuropsychologically and practically). On most of the cognitive tests, patients performed significantly less well than control subjects. Almost 50% of the controls and the patients failed the driving evaluation. None of the neuropsychological tests was able to predict the driving outcome. Of the patients who failed the first driving evaluation, 85% passed the second evaluation after driving practice. There are few controlled studies focusing on the stroke population and the effect of behind-the-wheel training. It is suggested that more controlled studies are needed with more homogenous patient-groups and reliable and quantitative outcome measures.     

The psychophsiology of aggressive drivers: comparison to non-aggressive drivers and pre- to post-treatment change following a cognitive-behavioural treatment

Twenty drivers were remanded to our treatment program by the courts following arrests related to serious aggressive driving behaviors. Ten additional drivers entered our program in response to our advertisements thus identifying themselves as aggressive drivers. Psychophysiological assessments were conducted on all 30 drivers and heart rate (HR), systolic and diastolic blood pressure (SBP and DBP), and skin resistance level (SRL) were measured in response to exposure to a mental arithmetic neutral stressor, two idiosyncratic, audio-taped, aggressive driving vignettes (audio 1 and audio 2), and one non-driving related fearful vignette. Fourteen non-aggressive driving controls also completed the assessment. The results indicated that the aggressive drivers (ADs) showed significantly more SBP responsivity during audio 1 and audio 2 and significantly less SBP reactivity during the mental arithmetic stressor than the controls. The aggressive drivers then completed a four-week, group intervention which included relaxation techniques specifically targeting aggressive driving behaviors. The same psychophysiological assessment was conducted at post-treatment. The results showed significant pre- to post-treatment decreases in HR, p<0.003, SBP, p<0.01, and DBP, p<0.02 during audio 1. Similarly, decreases were evident in audio 2 in SBP, p<0.03 only. No decreases in reactivity occurred during the neutral stressor or fearful situation.     

The driving anger expression inventory: a measure of how people express their anger on the road

Four ways people express their anger when driving were identified. Verbal Aggressive Expression (alpha=0.88) assesses verbally aggressive expression of anger (e.g., yelling or cursing at another driver); Personal Physical Aggressive Expression (alpha=0.81), the ways the person uses him/herself to express anger (e.g., trying to get out and tell off or have a physical fight with another driver); Use of the Vehicle to Express Anger (alpha=0.86), the ways the person uses his/her vehicle to express anger (e.g., flashing lights at or cutting another driver off in anger); and Adaptive/Constructive Expression (alpha=0.90), the ways the person copes positively with anger (e.g., focuses on safe driving or tries to relax). Aggressive forms can be summed into Total Aggressive Expression Index (alpha=0.90). Aggressive forms of expression correlated positively with each other (rs=0.39-0.48), but were uncorrelated or correlated negatively with adaptive/constructive expression (rs=-0.02 to -0.22). Aggressive forms of anger expression correlated positively with driving-related anger, aggression, and risky behavior; adaptive/constructive expression tended to correlate negatively with these variables. Differences in the strengths of correlations and regression analyses supported discriminant and incremental validity and suggested forms of anger expression contributed differentially to understanding driving-related behaviors. Theoretical and treatment implications were explored.     

A technology to measure multiple driving behaviors without self-report or participant reactivity

An in-vehicle information system (IVIS) was used to videotape drivers (N = 61) without their knowledge while driving 22 miles in normal traffic. The drivers were told that they were participating in a study of direction following and map reading. Two data-coding procedures were used to analyze videotapes. Safety-related behaviors were counted during consecutive 15-s intervals of a driving trial, and the occurrence of certain safety-related behaviors was assessed under critical conditions. These two methods of data coding were assessed for practicality, reliability, and sensitivity. Interobserver agreement for the five different driving behaviors ranged from 85% to 95%. Within-subject variability in safe driving was more pronounced among younger drivers and decreased as a function of age. Contrary to previous research that has relied on self-reports, driver risk taking did not vary significantly as a function of gender. These results are used to illustrate the capabilities of the technology introduced here to design and evaluate behavior-analytic interventions to increase safe driving.