Differences in Driving Anger among Professional Drivers: A Cross-Cultural Study.

Public transport systems have a vital role in achieving sustainable mobility goals, diminishing reliance on private individual transport and improving overall public health. Despite that, transport operators are often in situations that require them to cope with complex working conditions that lead to negative emotions such as anger. The current study represents a segment of the permanent global research agenda that seeks to devise and test a psychometric scale for measuring driving anger in professional drivers. The present research is one of the first attempts to examine the factorial validity and the cross-cultural measurement equivalence of the broadly utilized Driving Anger Scale (DAS) in three culturally different countries within the Western Balkans region. The respondents (N = 1054) were taxi, bus and truck drivers between 19 and 75 years of age. The results pertaining to confirmatory factor analysis showed that there were adequate fit statistics for the specified six-dimensional measurement model of the DAS. The measurement invariance testing showed that the meaning and psychometric performance of driving anger and its facets are equivalent across countries and types of professional drivers. Furthermore, the results showed that driving anger facets had positive correlations with dysfunctional ways of expressing anger and negative correlations with the form of the prosocial anger expression. In addition, the results revealed that taxi drivers displayed considerably higher levels of anger while driving and aggressive driving than truck and bus drivers. Overall, this study replicates and extends the accumulated knowledge of previous investigations, suggesting that the original DAS remains a reliable and stable instrument for measuring driving anger in day-to-day driving conditions.

Comparing driving behavior of humans and autonomous driving in a professional racing simulator.

Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers. This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario. A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data. In the first study, the participants' task was to achieve the fastest lap while keeping the car on the track. We grouped the resulting laps according to the performance (lap-time), defining driving behaviors at various performance levels. An extensive analysis of vehicle control features obtained from telemetry data was performed with the goal of predicting the driving performance and informing an autonomous system. In the second part of the study, a state-of-the-art reinforcement learning (RL) algorithm was trained to control the brake, throttle and steering of the simulated racing car. We investigated how the features used to predict driving performance in humans can be used in autonomous driving. Our study investigates human driving patterns with the goal of finding traces that could improve the performance of RL approaches. Conversely, they can also be applied to training (professional) drivers to improve their racing line.

Measuring the perception of aggression in driving behavior.

The study of aggressive driving is an important step in the reduction of (often fatal) crashes due to this behavior. However, even though various measures of aggressive driver behavior have been proposed, a more thorough examination of what the driving public perceives as aggressive driving behavior can be performed. A nationally representative sample of 198 American adults saw and rated the aggressiveness of various driving behaviors in videos. The videos were shown from a first-, second-, or third-person perspective. Some videos depicted close following, varying in speed and distance from the car ahead. Participants also saw illegal passing videos and collision or near collision videos. A number of variables that might influence judgments of aggressive driving were included as controls (i.e., trait anger, aggressive and prosocial driving attitudes, driving experience). Following other drivers closely was rated as aggressive, especially when viewed from a third-person perspective. Illegal passes were viewed as more aggressive than speeding. Faster speeds didn't increase aggressive ratings much, regardless of perspective. Aggressiveness ratings were especially high for acts that could be considered "road rage" (i.e., hitting or nearly hitting vehicles, cyclists, and pedestrians). People high in trait anger have a bias to view many driving behaviors as intentionally aggressive.

Adaptation of the driver behaviour questionnaire and behavioural risk factors for traffic violation arrest and self-reported crash involvement among Nigerian drivers.

CONTEXT: Few studies have been conducted to investigate the driving behaviour of drivers in Africa. AIMS: This study aims to determine the behavioural risk factors for road crashes among Nigerian drivers. SETTINGS AND DESIGN: This is a case-control study. Cases were drivers who were booked for traffic violation or who had been involved in road crashes in the past, while the controls were drivers with no such histories. SUBJECTS AND METHODS: Both the cases and controls were administered the Driver Behaviour Questionnaire (DBQ). Principal component analysis with varimax rotation was run to examine the factor structure of the scale. Cronbach's alpha was used for assessing the internal consistency of the DBQ, and logistic regression was used to determine risk factors for crash involvement. RESULTS: Six hundred active drivers consisting of 300 cases and 300 controls were selected. The mean scores of all DBQ items, except one, were significantly higher among booked drivers compared to those who had never been booked. Consistent with many previous studies, factor analysis identified three factors in the DBQ (aggressive violation, ordinary violation and error). However, the factors were constructed differently with most ordinary violation items in the original DBQ loading as aggressive violation in the present study. Eight variables were predictive of being booked for traffic offences while only five variables were predictive of self-reported crash involvement. CONCLUSIONS: The most important variable associated with previous crash involvement was alcohol use. A major policy implication of this is the need for better attention to anti-drunk driving measures.

Links between observed and self-reported driving anger, observed and self-reported aggressive driving, and personality traits.

Driving anger increases risk-taking in traffic and road traffic accident involvement. Herein, we examine the links between self-reported and observed driving anger, self-reported and observed aggressive driving, and personality traits. Specifically, sixty drivers drove in an anger-inducing simulated driving scenario. A video camera recorded their verbal and gestural expression during the simulator drive. Two weeks before the simulator drive, we assessed participants' basic personality traits, driving anger expression, and aberrant driving behaviour via an online survey. State anger was measured immediately before and after the simulator drive. From recorded simulator and video data, we obtained four measures: the number of accidents (simulator), an aggressive driving score (simulator), verbal expression of driving anger (video), and related gestures and headshakes (video). Verbal and gestural expression while driving were related to an increase in state anger in the simulator drive and different self-reported measures: While observed verbal expression was positively related to lapses and negatively related to constructive expression, gestural expression was positively related to both self-reported violations and self-reported aggressive expression. The traits Emotionality and Honesty-Humility were related to an increase in state anger and to verbal expression in the simulator drive, yet, age and gender modified the relation to personality traits. Results can support the development of personalised anger management interventions and anger mitigating in-vehicle devices.

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.

Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data.

This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and 'other' aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and 'other' aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers' awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and 'other' aggressive driving behaviors.

Persistent traffic offenders: alcohol consumption and personality as predictors of driving disqualification / Los infractores de tráfico persistentes: el consumo de alcohol y la personalidad como predictores de la retirada del permiso de conducir

Traffic safety is an important social problem. Many accidents are due to non-compliance with traffic regulations. Serious or repeated offenses are sanctioned with penalty points or court conviction, and sanctions can lead to disqualification from driving. This paper explores the relevance of alcohol consumption and personality factors as predictors of driving disqualification. The aim of the study is to determine whether the behaviors of persistent offenders and their propensity for law-breaking are related to their characteristics and patterns of drinking. A sample of 358 drivers participated in the study: 126 non-offender habitual drivers and 232 persistent traffic offenders disqualified from driving for serious or repeated traffic offenses, 127 of them after conviction, 105 without conviction (by accumulation of penalties). Participants were given a battery of tests measuring a set of explanatory personality and alcohol consumption factors. We used a cross-sectional study design and performed statistical analysis of variance and regression searching for differences among the groups. The results reveal group effects, with significant differences in a number of factors between traffic offenders and non-offenders, and between both categories of offenders in a number of variables, including traffic violations that lead to demerit points and/or loss of a driver's license and crash involvement. Certain variables, including problem drinking, high levels of activity or excitement, penchant for thrill or sensation seeking, and propensity to hostility while driving, can accurately predict group membership. Alcohol disorders are the best predictors of disqualification from driving for serious or repeat traffic offenses, both penalized and convicted

La seguridad vial es un importante problema social. Muchos accidentes se deben al incumplimiento de las normas de tráfico. Las infracciones graves o reiteradas se sancionan por la vía administrativa o judicial y en ambos casos las sanciones pueden suponer la pérdida del permiso de conducir. Este artículo explora la relevancia del alcohol y la personalidad como factores predictivos. El objetivo del estudio es determinar si el comportamiento delictivo de los infractores persistentes está relacionado con sus características de personalidad y patrones de consumo de alcohol. Se utilizó una muestra de 358 conductores: 232 infractores persistentes, a los que les había sido retirado el carnet de conducir (127 por sentencia judicial y 105 por pérdida total de puntos), y 126 conductores habituales no infractores. Se administró una batería de pruebas que miden un conjunto de factores explicativos de personalidad y consumo de alcohol. Se utilizó un diseño transversal y se realizaron análisis estadísticos de varianza y regresión buscando diferencias entre los grupos. Los resultados revelan diferencias significativas en el tipo de infracciones y accidentes entre infractores de tráfico y no infractores y entre ambas categorías de infractores. Además, ciertas variables, como el abuso de alcohol, altos niveles de actividad, activación emocional, búsqueda de sensaciones y la tendencia a la hostilidad durante la conducción, pueden predecir con precisión la pertenencia a uno u otro grupo. Los problemas con la bebida son el mejor predictor de la pérdida del permiso de conducir, tanto por condena como por acumulación de sanciones

Comparison among driving state prediction models for car-following condition based on EEG and driving features.

Risky driving states such as aggressive driving and unstable driving are the cause of many traffic accidents. Many studies have used either driving data or physiological data such as electroencephalography (EEG) to estimate and monitor driving states. However, few studies made comparison among those driving-feature-based, EEG-feature-based and hybrid-feature-based (combination of driving features and EEG features) models. Further, limited types of EEG features have been extracted and investigated in the existing studies. To fill these research gaps aforementioned, this study adopts two EEG analysis techniques (i.e., independent component analysis and brain source localization), two signal processing methods (i.e., power spectrum analysis and wavelets analysis) to extract twelve kinds of EEG features for the short-term driving state prediction. The prediction performance of driving features, EEG features and hybrid features of them was evaluated and compared. The results indicated that EEG-based model has better performance than driving-data-based model (i.e., 83.84% versus 71.59%) and the integrated model of driving features and the full brain regions features extracted by wavelet analysis outperforms other types of features with the highest accuracy of 86.27%.

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.

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.

Analysis of cut-in behavior based on naturalistic driving data.

Cut-in maneuvers, when vehicles change lane and move closely in front of a vehicle in the adjacent lane, are very common but adversely affect roadway capacity and traffic safety. Yet little research has comprehensively explored cut-in behavior, particularly in China, which has a challenging driving environment and is often used for connected and autonomous vehicle testing. This study developed an extraction algorithm to retrieve 5608 cut-in events from the Shanghai Naturalistic Driving Study. The data were used to identify cut-in characteristics, including motivation, turn signal usage, duration, urgency, and impact. Results showed that almost half of drivers did not use a turn signal when cutting in, and that cut-ins had a shorter time to collision (TTC) than other lane changing. A lognormal distribution was found to produce the best fit for cut-in duration, which varied from 0.7 s to 12.4 s. As characteristics were found to vary by roadway type and motivation, multilevel mixed-effects linear models were developed to examine the influencing factors of cut-in gap acceptance. Acceptance of lead and lag gaps was significantly affected by environmental variables, vehicle type, and kinematic parameters, which has important implications for microsimulation, as does the large variance in duration that makes specifying duration essential when setting scenarios. Improvement in safety education is warranted by the high degrees of risk and aggression shown by TTC and turn signal usage; but the ability of drivers, who needed to yield to the cut-in, to predict danger and adopt safe, suitable, and timely strategies suggests that advanced driver assistance systems and connected and autonomous vehicles can learn similar responses.

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.

The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China.

Although the taxi industry is playing an important role in Chinese everyday life, little attention has been posed towards occupational health issues concerning the taxi drivers' working conditions, driving behaviour and road safety. A cross-sectional survey was administered to 1021 taxi drivers from 21 companies in four Chinese cities and collected information about (i) sociodemographic characteristics, (ii) working conditions, (iii) frequency of daily aberrant driving behaviour, and (iv) involvement in property-damage-only (PDO) and personal injury (PI) crashes over the past two years. A hybrid bivariate model of crash involvement was specified: (i) the hybrid part concerned a latent variable model capturing unobserved traits of the taxi drivers; (ii) the bivariate part modelled jointly both types of crashes while capturing unobserved correlation between error terms. The survey answers paint a gloomy picture in terms of workload, as taxi drivers reported averages of 9.4 working hours per day and 6.7 working days per week that amount on average to about 63.0 working hours per week. Moreover, the estimates of the hybrid bivariate model reveal that increasing levels of fatigue, reckless behaviour and aggressive behaviour are positively related to a higher propensity of crash involvement. Lastly, the heavy workload is also positively correlated with the higher propensity of crashing, not only directly as a predictor of crash involvement, but also indirectly as a covariate of fatigue and aberrant driving behaviour. The findings from this study provide insights into potential strategies for preventive education and taxi industry management to improve the working conditions and hence reduce fatigue and road risk for the taxi drivers.

The association between sensation seeking and driving outcomes: A systematic review and meta-analysis.

The purpose of this study was to evaluate the association between sensation seeking (SS) and driving outcomes (including four aberrant driving behaviors, accident involvement and tickets received) through a systematic review and meta-analysis. Forty-four eligible studies, representing 48 individual trials, were identified from a systematic literature search of four electronic databases, and included in the meta-analysis. Overall, the meta-analysis results showed that SS yielded significant positive correlations with risky driving (pooled r = 0.24, p < 0.001), aggressive driving (pooled r = 0.23, p = 0.019), and errors (pooled r = 0.22, p = 0.016). SS was also positively correlated with accident involvement (pooled r = 0.08, p < 0.001) and tickets received (pooled r = 0.19, p < 0.001), though at weaker levels. The correlations could also be moderated by a number of study and sample characteristics, such as country of origin, publication year, age, gender, driving experience and type of SS measure. The findings help facilitate our understanding of the role of SS in aberrant driving behaviors and accident risk, and provide new insight into the design of evidence-based driving education and accident prevention interventions.

The factors shaping car drivers' attitudes towards cyclist and their impact on behaviour.

Cycling for transportation has multiple benefits to both individuals and societies. However, in many countries, cycling rates are very low. One major deterrent is hostile or aggressive behaviours directed towards cyclists. Past research has established that negative attitudes towards cyclist are a major driver of aggressive behaviour. However, the attitudinal roots that motivate these negative attitudes are currently not well understood. This study investigates to what extent negative attitudes towards cyclists are rooted in a sense of attachment to cars, and environmental attitudes. Furthermore, the study examines whether the distinctiveness of group-membership of cyclists, as signalled by cycling attire, influences the link between attitudes and aggressive behaviours directed at cyclists. An online survey of 308 car drivers measured automobility and environmental attitudes, attitudes towards cyclist, and aggressive behaviour addressed at two groups of cyclists (lycra-clad or casually dressed cyclists). Hierarchical regression analyses showed that automobility attitudes, but not environmental attitudes, were associated with negative car driver attitudes towards cyclists. A significant link between negative attitudes towards cyclists and aggressive behaviour addressed at cyclists was not moderated by the type of cyclist shown. These findings provide a more refined understanding of the basis in which negative attitudes towards cyclists are rooted and how they affect driver behaviour. This research may inform campaigns and initiatives aimed at changing attitudes towards cyclists.

Effect of personality traits on driving style: Psychometric adaption of the multidimensional driving style inventory in a Chinese sample.

Driving style is an important factor in driving psychology, which is useful for effectively evaluating driving behaviors. Exploring driving style in a Chinese sample could help understand the overall situation of Chinese drivers. This study aims to develop a measurement of driving style fit for the Chinese situation and to validate the validity and reliability of this scale. In this study, 296 drivers from China completed the Chinese version of the multidimensional driving style inventory (MDSI), the Big Five Inventory (BFI), and the Driver Behavior Questionnaire (DBQ) as well as several questions about socio-demographic information. After testing the assumed structure by a confirmatory factor analysis, and adjusting the structure, a brief version of Chinese version of MDSI with twelve items categorized into four driving styles, namely, risky style, angry- high-velocity style, careful style and anxious style was revised. The validity and reliability of the scales were acceptable. The results showed that driving styles were closely correlated with self-reported driving behaviors. Specifically, risky style, angry- high-velocity style, and anxious style are all positively associated with dangerous driving behaviors. Meanwhile, careful style was positively associated with positive driving behaviors and negatively correlated with dangerous driving behaviors. Anxious was also found to be negatively associated with fines. For personality, we found a positive relationship between risky, angry- high-velocity, careful and anxious styles and the personality traits that often have negative effects on driving, such as extraversion and neuroticism. Meanwhile, these three styles were negatively correlated with conscientiousness and agreeableness in general. In addition, careful style was positively correlated with conscientiousness and agreeableness. The current study shows convincing evidence for the validity and reliability of the brief MDSI-C and develops a useful tool for identify the driving style of Chinese drivers for future research and relevant departments of road safety.

Association of posttraumatic stress disorder and traumatic brain injury with aggressive driving in Iraq and Afghanistan combat veterans.

PURPOSE: Aggressive driving contributes to the high rates of postdeployment motor vehicle-related injury and death observed among veterans, and veterans cite problems with anger, aggressive driving, and road rage as being among their most pressing driving-related concerns. Both posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) have been associated with driving-related deficits in treatment-seeking samples of veterans, but the relative contribution of each of these conditions to problems with aggressive driving in the broader population of combat veterans is unclear. METHOD: χ2 and logistic regression analyses were used to examine the relative association of PTSD, TBI, and co-occurring PTSD and TBI to self-reported problems with road rage in a sample of 1,102 veterans living in the mid-Atlantic region of the United States who had served in Afghanistan or Iraq. RESULTS: Results indicate that controlling for relevant demographic variables, PTSD without TBI (odds ratio = 3.44, p < .001), and PTSD with co-occurring TBI (odds ratio = 4.71, p < .001) were associated with an increased risk of road rage, but TBI without PTSD was not. CONCLUSIONS: Our findings suggest that PTSD, with or without comorbid TBI, may be associated with an increased risk of aggressive driving in veterans. Clinical implications for treating problems with road rage are discussed, including use of interventions targeting hostile interpretation bias and training in emotional and physiological arousal regulation skills. (PsycINFO Database Record

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.