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

Driving Fine and its Relationship with Dangerous Driving Behaviour Among Heavy Vehicle Drivers

Context

There is a significant difference between actual and existing statistics of traffic fines; since some invisible fines and most of the visible traffic violations cannot be recorded by traffic officers. Therefore, dealing with driving fines and road fatalities is considered an important issue in social and public management worldwide.

Aims

Explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers.

Settings and Design

The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.

Methods and Material

This study used the driver behavior questionnaire (DBQ), demographic and driving characteristics, the number of fines, and structural equation modeling. Also, in this study 320 professional drivers participated.

Statistical Analysis Used

This article used structural equation modeling for Statistical analysis.

Results

The results of structural equation modeling analysis indicated that the data fit well with the theoretical model proposed in this study. The number of fines was directly predicted by both demographic and driving characteristics and risky driving behaviors. A significant relationship was observed between, driving hours, driving experience, and smoking, respectively, with a mistake, slip, and risky violation. There was a negative correlation between education and all four sub-scales of risky driving behaviors.

Conclusions

In order to reduce traffic fines, training courses on increasing attention and precision in drivers' observations and judgments are useful. The courses can decrease traffic violations by trying to change beliefs, attitudes, and social norms. It is therefore helpful to understand the ways to change the drivers' attitudes.

Assessing safety critical driving patterns of heavy passenger vehicle drivers using instrumented vehicle data - An unsupervised approach

Assessing the individual's driving profile and identifying the at-fault behaviors contributes to road safety, riding comfort, and driver assistance systems. This study proposes a framework to identify aggressive driving patterns in longitudinal control using real-time driving profiles of heavy passenger vehicle (HPV) drivers. The main objective is to detect and quantify the instantaneous driving decisions and classify the identified maneuvers (acceleration, braking) using unsupervised machine learning techniques without any prior-ground truth. To this end, total 8295 acceleration events, and 7151 braking events, were extracted from 142 driving profiles collected using high-resolution (10 Hz) GPS instrumentation. The principal component analysis was conducted on a multi-dimensional feature set, followed by a two-stage k-means clustering on the reduced feature subspace. The results showed that 86.5% of accelerations and 65.3% of braking maneuvers were characterized as non-aggressive, indicating safe or base-line driving behavior. However, 13.5% of accelerations and 34.7% of braking maneuvers were featured to be aggressive, indicative of the actual risky behaviors. Further analysis demonstrated the heterogeneity in drivers' trip-level frequency of aggressive maneuvers and highlighted the need for a continuous driving assessment. The study also revealed that the thresholds derived from the obtained clusters featuring the aggressive accelerations (+0.3 to +0.48 g) and aggressive braking (-0.42 to -0.27 g) maneuvers were beyond the acceptable limits of passenger safety and comfort. The insights from the study aids in developing driver assistance systems for personalized feedback provision and improve driver behavior.

Typology of aggressive and transgressive drivers

The present study investigates the pattern in which transgressive and aggressive driving motives and aggressive driving were ordered in different clusters of drivers. To establish the difference between profiles, anger disorders, state-trait anger, and motives for transgression and respect for traffic rules were studied and compared between clusters. A total of 383 participants (laypersons), of all age and gender, filled out self-report measures evaluating aggressive driving, state-trait anger, anger disorders, aggressive driving motives, and motives for transgression and respect for traffic rules. Results show the emergence of four profiles of drivers: Respectful, Aggressive-Avenger, Aggressive-Dominant, and Aggressive-Situational. The difference between these clusters has been confirmed by the high tendency of anger disorders for Aggressive-Dominant, low tendency of aggressive driving for Respectful, high tendency for motives for aggressive driving as altruistic protection for Aggressive-Avenger, and high tendency of speeding for Aggressive-Situational. Our findings strongly support the importance of creating programs adapted to each driver's profile.

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.

Association between higher-order driving instruction and risky driving behaviours: Exploring the mediating effects of a self-regulated safety orientation

Adolescents' risky driving behaviours contribute to their over-representation in road trauma. Higher-order driving instruction is suggested to reduce such behaviours. To sustain positive behaviours in the long-term, self-determination theory identifies self-regulation as fundamental. The current research explored associations between higher-order driving instruction, risky driving behaviours, and a self-regulated safety orientation. Learner drivers (n = 544), aged 16-19 years, responded to a 91-item survey. Self-regulated safety orientation was found to fully mediate the relationship between higher-order driving instruction and inattentive risky driving behaviours, and between anticipatory higher-order driving instruction and intentional risky driving behaviours. A partial mediation was found between self-regulatory higher-order instruction and intentional risky driving behaviours. These results support that higher-order driving instruction, delivered to develop a self-regulated safety orientation, has potential to reduce young novice drivers' risky driving behaviours. Further research is recommended to triangulate these results through direct observation and longitudinal evaluation.

Assessing the relationship between self-reported driving behaviors and driver risk using a naturalistic driving study

The Manchester Driver Behavior Questionnaire (DBQ) identifies risky driving behaviors resulting from psychological mechanisms. Investigating the relationships between these behaviors and drivers' crash risk can provide a better understanding of the personal factors contributing to the incidence of crashes, allowing the more effective development of safety education and road management countermeasures and interventions. The objectives of this study are therefore: 1) to determine the extent to which driver involvement in both crashes and near crashes (CNCs) is related to self-reported driving behaviors, and 2) to assess the relationship between each type of risky behavior and individual driver CNC risk. Driver and crash data were acquired from the Shanghai Naturalistic Driving Study and included 45 males and 12 females, participants with the mean age of 38.7. A K-mean cluster method was adopted to classify participants into three CNC groups of high-, moderate- and low-risk drivers. Drivers completed the DBQ to self-evaluate the frequency during their daily driving of the questionnaire's 24 risky behaviors. Principal component analysis of the 24 items led to a five-component structure including aggressive violations, ordinary violations, lapses, inattention errors, and inexperience errors. Two logistic regression models were developed to investigate the correlation between the five DBQ components and drivers' CNC levels. Conclusions are as follows: 1) high-risk drivers were significantly more likely to have engaged in inattention errors (e.g., missing a "yield" sign) and ordinary violations (e.g., running a red light) than the other drivers, and, 2) aggressive violations (e.g., racing against others) and ordinary violations were positively related to the probability of being a high- or moderate-risk driver.

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.

The relationship between driving skill and driving behavior: Psychometric adaptation of the Driver Skill Inventory in China

Most road accidents are caused by human factors alone or in combination with other factors. Deficits in driving skill are a human factor that contributes to accidents. It is important to focus on driving skills to reduce traffic accidents and enhance safe driving. In this study, we adopted a Chinese version of the Driver Skill Inventory (DSI) and explored its correlation with driving behaviors, sociodemographic factors and personality. A total of 295 licensed drivers voluntarily completed a survey that covered the DSI, the Driver Behavior Questionnaire, the Positive Driver Behavior Scale, self-reported traffic accidents, penalty points and fines, the Big Five Inventory, and sociodemographic parameters. First, the results of principal axis analysis on the DSI yielded two clear factors: perceptual-motor skills and safety skills. Second, both perceptual-motor skills and safety skills were positively correlated with positive behaviors. Safety skills were negatively correlated with all aberrant driving behaviors (e.g., aggressive violations, ordinary violations, errors, and lapses), whereas perceptual-motor skills were negatively correlated with errors and lapses. Third, with regard to penalties, safety skills were negatively associated with penalty fines and points received within the past year, whereas perceptual-motor skills showed no such correlation. Fourth, with regard to sociodemographic parameters, perceptual-motor skills were positively correlated with years of holding a driving license, weekly driving distance and annual driving distance. Men reported higher perceptual-motor skills than women, whereas safety skills were unrelated to gender. Fifth, structural equation modeling was conducted to test the effects of personality traits on driving skill. The results showed that conscientiousness, neuroticism and openness to experience were significant predictors of perceptual-motor skills, whereas agreeableness and conscientiousness were significant predictors of safety skills. Overall, based on these results, the Chinese version of the DSI has acceptable internal consistency and a stable structure; thus, it represents a useful tool to measure driving skill. Moreover, the measurement of personality traits, which are important individual factors closely linked to driving skill, can aid in the education of professional drivers or to inform preventative and educational activities that focus on personality traits in addition to knowledge.

The relationship between the Driver Behavior Questionnaire, Sensation Seeking Scale, and recorded crashes: A brief comment on Martinussen et al. (2017) and new data from SHRP2

We provide a brief comment on the work of Martinussen et al. (2017), who studied the relationships between self-reported driving behavior, registered traffic offences, and registered crash involvement. It is argued that if the number of crashes is small, then the correlation with crashes is also small. Our analysis of the SHRP2 naturalistic driving study shows that the violations score of the Driver Behavior Questionnaire and the Sensation Seeking Scale exhibit small correlations with recorded crashes, and small-to-moderate correlations with recorded near-crashes and measures of driving style.

Risk factors for adverse driving outcomes in Dutch adults with ADHD and controls

OBJECTIVE

To identify risk factors for adverse driving outcomes and unsafe driving among adults with and without ADHD in a Dutch sample.

METHODS

In this cross-sectional study, validated self-report questionnaires were used to compare driving history and current driving behavior between 330 adults diagnosed with ADHD and 330 controls.

RESULTS

Adults with ADHD had significantly more adverse driving outcomes when compared to controls. Having an ADHD diagnosis significantly increased the odds for having had 3 or more vehicular crashes (OR = 2.72; p = .001). Driving frequency, male gender, age, high anxiety levels, high hostility levels, and alcohol use all significantly influenced the odds for unsafe driving behavior, for having had 12 or more traffic citations, and/or for having had 3 or more vehicular crashes.

CONCLUSIONS

Alcohol use, and high levels of anxiety and hostility are highly prevalent among adults with ADHD, and they mediate the risk for negative driving outcomes in this group.