The mediating effect of driver characteristics on risky driving behaviors moderated by gender, and the classification model of driver's driving risk

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog-a driving simulator study

OBJECTIVE

Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment.

METHODS

A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed.

RESULTS

The results show that reasonably selecting colored pavement can effectively improve drivers' behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety.

CONCLUSIONS

The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Better understanding female and male driving offenders' behavior: Psychological resources and vulnerabilities matter!

Although driving risk taking appears to be mainly male, an increase in driving violations has been observed in recent years among French female drivers. The main objective of the present study was to explore the driving behaviors, psychological resources, and vulnerabilities of female and male driving offenders participating in a French driver rehabilitation program. The second aim was to examine to what extent females' and males' resources and vulnerabilities predicted their violations, engagement in distracting activities while driving, and prosocial driving behaviors. In the course of 110 rehabilitation programs, 1686 driving offenders (22.4% females) completed a paper-and-pencil questionnaire. Compared to male offenders, females were more likely to have received a higher education, be divorced, or separated, and drive fewer annual kilometers. They also had had fewer demerit points than males in the last three years. They were more empathetic but also more impulsive than their male counterparts and less self-compassionate and mindful. Regression and moderation analyses revealed that, across genders, certain psychological resources such as mindfulness can be considered as protective factors for driving offenders as they tend to decrease dangerous behaviors and increase prosocial ones, while vulnerabilities such as aggressive driving anger expression seem to have the opposite effect. Our results provide a better understanding of driving offenders' behavior and the influence of personal dispositions. They also open new interesting research avenues in the prevention of dangerous behaviors among this high-risk population.

Relations between risk perception, perceptions of peers' driving, and risky driving among Cambodian adolescents

Introduction

Traffic accidents are a leading cause of death globally, with substantial economic impact particularly in low-and-middle-income countries (LMIC). Adolescents are at particular risk, partly due to their tendency to engage in risky driving. However, most research designed to identify potential causes of risky adolescent driving has been conducted in Western, high-income countries, which often have substantial cultural differences from LMIC that potentially influence risky adolescent driving.

Methods

The present study, one of the first focused on this topic in Southeast Asia, cross-sectionally assessed 425 adolescent motorbike drivers in the Southeast Asian LMIC Cambodia. Adolescents' (a) beliefs about peers' driving (social norms) and (b) driving risk perception were assessed as predictors of four risky driving behaviors: aggressive driving; distracted driving; intoxicated driving; violating driving laws.

Results

Canonical correlation analysis identified a general relation between (a) beliefs about peers' driving, and (b) all four risky driving behaviors, with R2 = 0.35 indicating over one-third of the variance in risky driving was explained by perceptions of peers' driving. Risk perception was not involved in the significant canonical relation, however. Gender moderated two of the underlying relations, with females showing larger relations between perceptions of friends' driving, and distracted driving and violating driving laws.

Discussion

These findings provide useful directions for future research (e.g., assessing the accuracy of Cambodian adolescents' perceptions of peers' driving) useful for helping stakeholders tailor road safety programs (e.g., providing adolescent drivers with accurate information regarding their peers' actual driving behaviors) for adolescent motorcyclists in Cambodia and similar countries.

Feature selection for driving style and skill clustering using naturalistic driving data and driving behavior questionnaire

Driver's driving style and driving skill have an essential influence on traffic safety, capacity, and efficiency. Through clustering algorithms, extensive studies explore the risk assessment, classification, and recognition of driving style and driving skill. This paper proposes a feature selection method for driving style and skill clustering. We create a supervised machine learning model of driver identification for driving behavior data with no ground truth labels on driving style and driving skill. The key features are selected based on permutation importance with the underlying assumption that the key features for clustering should also play an important role in characterizing individual drivers. The proposed method is tested on naturalistic driving data. We introduce 18 feature extraction methods and generate 72 feature candidates. We find five key features: longitudinal acceleration, frequency centroid of longitudinal acceleration, shape factor of lateral acceleration, root mean square of lateral acceleration, and standard deviation of speed. With the key features, drivers are clustered into three groups: novice, experienced cautious, and experienced reckless drivers. The ability of each feature to describe individuals' driving style and skill is evaluated using the Driving Behavior Questionnaire (DBQ). For each group, the driver's response to DBQ key questions and their distribution of key features are analyzed to prove the validity of the feature selection result. The feature selection method has the potential to understand driver's characteristics better and improve the accuracy of driving behavior modeling.

Analysis of driver characteristics, self-reported psychology measures and driving performance measures associated with aggressive driving

Whereas aggressive driving mainly causes speed-related crashes, aggressive driving may be reduced to improve road safety by identifying aggressive driving behaviour, aggressive drivers' characteristics, and their underlying motivational and psychological processes. Previous studies show that both driving performance and self-reported measures of aggressive driving are effective means to identify aggressive drivers. However, these studies assessed aggressive driving patterns across only a limited number of events, did not relate driver characteristics to aggressive driving in each event, and used chiefly vehicle kinematics variables (e.g., mean speed), but not vehicle dynamics variables (e.g., brake pedal force) which better capture driver reaction and decision-making. To address these limitations, this study assessed driver characteristics, self-reported psychological measures, and driving performance measures associated with aggressive driving among 55 drivers' behaviours in 9driving events using a driving simulator and survey responses. The results of structural equation models showed that unique aggressive driving patterns and driver characteristics related to aggressive driving vary among different driving events. As such, we recommend road safety policies to reduce aggressive driving based on the findings in this study.

The association between the Big Five personality traits and driving behaviors: A systematic review and meta-analysis

Although a large number of studies have examined the relationship between the Big Five personality traits and driving behaviors, consistent evidence for their relationships is still lacking. The main purpose of this study was to systematically review the relationships between the Big Five personality traits and various driving behaviors with different intentions (including risky, aggressive, and positive driving behaviors) through a meta-analysis. A total of 34 articles met the inclusion criteria for the meta-analysis. The results showed that risky and aggressive driving behaviors were negatively associated with conscientiousness (r = -0.21; r = -0.26), agreeableness (r = -0.23; r = -0.37), and openness (r = -0.08; r = -0.07), positively associated with neuroticism (r = 0.11; r = 0.26), and nonsignificantly associated with extraversion (r = 0.06; r = -0.06). Positive driving behaviors were positively associated with conscientiousness (r = 0.30), agreeableness (r = 0.32) and openness (r = 0.20) but nonsignificantly associated with extraversion (r = 0.08) and neuroticism (r = -0.10). In addition, the association between the Big Five personality traits and driving behaviors could be moderated by age, gender and type of personality measure. In conclusion, this study contributes to the literature by quantitatively synthesizing existing findings and reconciling previous debates on the relationship between the Big Five personality traits and driving behaviors. From a practical perspective, our findings provide valuable insights into driver selection and screening, policy development, and safety intervention design.

Effects of personality traits on bus drivers' prosocial and aggressive behaviours: The moderated mediating role of risk perception and gender

PURPOSE

The present study aimed to examine the effects of personality traits on bus drivers' self-reported prosocial and aggressive driving behaviours as well as the mediating role of risk perception and the moderating role of gender in this relationship.

METHODS

Three hundred and ten bus drivers who were 24-57 years old agreed to participate in this study. The measures utilized included personality scales, a risk perception scale and a prosocial and aggressive driving behaviour scale.

RESULTS

A moderated mediation model was established. The effects of normlessness/anger on prosocial driving behaviour are mediated by risk perception. Risk perception has a stronger promoting effect on the prosocial driving behaviour of male drivers (b = 0.358, p < 0.01) than it does on that of female drivers (b = 0.072, p > 0.05). The effects of normlessness/anger on the aggressive driving behaviour of both male and female drivers are also mediated by risk perception. Moreover, gender plays a moderating role in the influences of personality and risk perception on aggressive driving behaviour. Drivers with higher risk perception and less anger exhibit less aggressive driving behaviour, and this effect is clearer among male drivers than it is among female drivers.

CONCLUSION

The present study revealed the relationship between personality traits and the prosocial and aggressive driving behaviours of bus drivers through a moderated mediation model. These findings highlight the importance of taking risk perception and gender into consideration when examining the effects of personality on bus drivers' driving behaviours.

An exploration of the role of driving experience on self-reported and real-world aberrant driving behaviors

A significant proportion of global road crashes are attributed to unsafe driving behaviors. The current study aimed to explore potential differences in driving behaviors across experienced and novice drivers using two separate approaches; a questionnaire study and an instrumented vehicle study (IVS). The analysis of 260 questionnaires and 1,372 traffic interactions within the IVS revelated that driving experience affects driving performance for different driving tasks. Factor analysis of the questionnaire data revealed the impact of driving errors, lapses, violations, and aggressive violations on the behavior of novice and experienced drivers. Behavioral models of novice and experienced drivers encountering other road users were determined using binary logistic regression. The results showed that novice drivers were more likely to engage in driving violations while experienced drivers were more likely to engage in aggressive violations. Unauthorized speeding, zigzag movements, using a mobile phone while driving, and unauthorized overtaking on roads were the most frequent driving violations by novice drivers. The most frequent aggressive violations by experienced drivers were tempting other drivers to create a race and chasing other drivers. These findings may be used as a framework to facilitate safer driving behaviors by reducing errors, lapses, violations and aggressive violations, and facilitating safety-promoting attitudes.

Psychological Functioning, Defense Strategies and the Contribution of Perceived Family Collaboration in Adolescents Who Experienced Multiple Motor Vehicle Crashes: A Descriptive Study

Adolescents frequently experience motor vehicle collisions (MVCs). Research has mostly underlined the psychological outcomes, rather than adolescents’ general emotional–behavioral functioning and the role played by family. This study aims to explore the emotional–behavioral functioning, measured with the Youth Self Report (YSR), difficulties to identify and describe emotions, measured with the Toronto Alexithymia Scale (TAS-20), the use of defense strategies, measured with the Response Evaluation Measure for Youth (REM-71), and perceived family collaboration, measured with the Perceived Collective Family scale, in adolescents that have experienced more than three MVCs in a year. N = 150 adolescents who visited an emergency department for MVCs were assessed through self-report questionnaires. Adolescents showed difficulties to identify and describe their emotions and a massive use of defense strategies. Moreover, lower perceived family collaboration predicted adolescents’ alexithymic traits and the massive use of maladaptive defense strategies. These results may be useful in assessing and creating prevention programs for risky driving behaviors in adolescence.

Data-Driven Estimation of a Driving Safety Tolerance Zone Using Imbalanced Machine Learning

Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project “i-DREAMS”, which aims at defining, developing, testing and validating a ‘Safety Tolerance Zone’ (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.

Eye movement as a function to explore the effects of improved signs design and audio warning on drivers' behavior at STOP-sign-controlled grade crossings

Train-vehicle collisions at STOP-sign-controlled grade crossings attract many concerns in China and across the world. Researchers have demonstrated that the cost-effective approaches to improve grade crossing safety are the redesign of signs and pavement markings as well as the application of in-vehicle audio warning. However, the impacts of improved signs design and audio warning on drivers' visual performance have barely been discussed. This study explored the effects of improved signs design and audio warning on drivers' eye movement patterns and driving behavior at STOP-sign-controlled grade crossings, by conducting a driving simulator experiment. Three types of grade crossing scenarios: 1) the conventional signs design (Baseline), 2) improved signs design (PS), and 3) improved signs design and three-stage audio warning (PSW), were modeled in a driving simulation system and tested under a series of train TTC (no train, 4 s, 7 s, 10 s, 13 s) conditions. Foggy conditions and drivers' characteristics, i.e., gender and vocation were considered in the experiment design. Seven variables describing both drivers' fixation patterns and driving performance were collected and analyzed in this study, e.g., total fixation duration, distance to stop line at the first fixation, fixation transition probability, stop compliance, speed, maximum deceleration rate and minimum time-to-collision. Results revealed that the improved design of signs and the audio warning could prime drivers' expectation of the grade crossing in advance since drivers could drive at a lower speed, perceive signs timely, and conduct an earlier visual search for the train with these countermeasures. Besides, in PS and PSW scenarios, drivers attached more importance to the STOP sign, and they were more cautious in estimating the time-to-arrival of the train by repeatedly fixating on these two areas. The improvement in fixation performance of drivers in PS and PSW contributed to a more comfortable deceleration. Compared with no warning scenarios, higher compliance rates were observed with audio warning, especially with a short train TTC (4 s and 7 s). However, no significant difference was found between PS and Baseline, indicating the limited safety benefits of improved signs design. Minimum time-to-collision for those drivers who ignored the warning did not increase significantly in both PS and PSW. Additionally, heavy fog limited drivers' perception of signs and led to a later and shorter fixation. For gender effect, males had a lower fixation duration on the STOP sign and lower compliance rate than females. Moreover, female drivers could perceive the approaching train earlier than males, especially in PS and PSW. These findings suggested that the improved signs design and in-vehicle audio warning improved drivers' visual and behavioral performance and had the potential to enhance safety at STOP-sign-controlled grade crossings.

A study of the factors affecting driving risk perception using the Bivariate Ordered Probit model

This paper aims to examine the key factors influencing driving risk perception in Iran. We conducted separate surveys for two groups of Iranian drivers, namely passenger car drivers and truck drivers. In order to assess driving risk perception, respondents were asked what they think about their Probability of Having a Road Accident (PHRA) and if they eventually have an accident as a driver, what they think about the Probability of it being Fatal or causing Severe Injury (PFSI). A Bivariate Ordered Probit model, which considers the possible correlation between PHRA and PFSI, was developed to explain the observed driving risk perception using type of vehicle, driving experience, socio-demographic information, and driving behaviour. According to the results, vehicle type, vehicle age, driving experience, sleep quality, at-fault accidents over the past three years, vehicles safety-related equipment, and education level have significant effects on driving risk perception (p-value < 0.05). In addition, this paper compares the driving risk perception of truck and passenger car drivers. The results show that truck drivers have a higher perception of PHRA and PFSI compared with passenger car drivers (p-value < 0.05). The results may convince policy-makers to consider the characteristics of the two categories of drivers when designing regulations.

Age Differences in Hazard Perception of Drivers: The Roles of Emotion

With the increasingly powerful functions of vehicle-mounted entertainment facilities, people (especially young drivers) like to listen to music while driving to render different atmospheres and emotions. However, emotions are important factors affecting drivers’ decisions, behavior and may reduce drivers’ hazard perception (HP), even promote dangerous driving behaviors of drivers. The purpose of this study is to explore the young and elderly drivers in assessing the HP difference under different emotional states. We conducted a 3 × 2 mixed experimental design with emotion as a within-participants variable and age as a between-participants factor. A sample of 14 young drivers (mean age = 22.21, SD = 1.05) and 13 elderly drivers (mean age = 54.08, SD = 2.72) completed the HP self-assessment of road traffic warning signs under negative emotion, neutral emotion, and positive emotion, randomly. The results showed that the young had the highest self-assessment HP under the negative emotion arousal condition, while the old had the highest self-assessment HP under the positive emotion arousal condition. In addition, When both groups were in a positive arousal state, the older group perceived more hazards than the young group. The results could help designers create driving emotions suitable for different driver groups, thus improving their perception of hazards and reducing risky driving.

Underestimated Risk Perception Characteristics of Drivers Based on Extended Theory of Planned Behavior

Aggressive driving behaviors due to drivers’ underestimation of risks are one of the major causes of traffic accidents. Due to the complexity of factors influencing risk perception, the mechanism of risk underestimation remains unclear. In this study, the theory of planned behavior (TPB) was extended by adding a new variable, namely drivers’ normlessness, forming an extended TPB (ETPB) framework to analyze the factors influencing risk underestimation and the extent of their influence. A total of 376 drivers’ perceived characteristics of risk underestimation were collected through an online survey, and a structural equation model was applied to investigate the effects of normlessness, behavioral attitudes, subjective norm, and perceived behavioral control on the tendency to underestimate the risk. The results showed that the ETPB model can explain the variance in the underestimation risk behavior by 69%; perceptual behavior control, attitude, and subjective norm (in descending order) had significant positive effects on driver’s tendency to underestimate risk; the normlessness variable can directly promote attitude and underestimated risk behavior; drivers with low annual mileage, complete insurance coverage, and no prior accident experience were more likely to underestimate driving risk. The study contributes to understanding of risk perception characteristics and provide theoretical basis for reducing underestimated risk behavior.

Mobile Phone Use "on the Road": A Self-Report Study on Young Drivers

BACKGROUND

Extensive research showed that multitasking negatively affects driving performance. Multitasking activities can range from talking and texting to listening to music; particularly among young drivers, multitasking behavior is caused mainly from mobile phone use while driving which is one of the main causes of road accidents.

OBJECTIVE

The main purpose of this study was to investigate whether some variables (e.g., Sensation-Seeking, preferences of Multitasking) could affect mobile phone use while driving in young drivers and whether any gender differences were present among the examined variables.

SETTING AND PARTICIPANTS

The sample consists of 424 Italian students (56% males) with an age range of 18-21 years. A self-report questionnaire was specifically developed to assess variables such as: Attitude toward Multitasking, Perceived Self-efficacy in Multitasking, Accident Risk Perception, General Multitasking Habits, and Sensation Seeking.

RESULTS

Through SEM modeling, we found the attitude to multitasking while driving to be largely explained by the considered variables. Using multigroup analysis (MGSEM), the model we developed appears to be suitable for explaining the behaviors of both male and female young drivers. Furthermore, data comparison showed that females were more likely to risk perception toward multitasking, and risk perception when using a mobile phone while driving, while males obtained higher mean scores in Sensation Seeking, Perceived Self-Efficacy in Multitasking, and in Multitasking caused by mobile phone use while driving.

CONCLUSION

Our research showed how some variables may influence the inclination of some subjects to engage in multitasking while driving. Furthermore, we discussed the importance of considering these variables in the implementation of effective road safety education projects on driving multitasking.