Risky Driving Behavior Research Articles

Technical Demand Analysis of Judicial Big Data Application under Risky Driving Behavior of Drivers

According to the rapid development of the global economy and the continuous improvement of people's living standards, more and more people have a car driving license. We must pay attention to standardize the driving behavior of drivers, which is important to ensure personal safety. The survey also found that more than half of traffic accidents are related to dangerous driving behavior. However, in judicial judgment, there are many problems in judicial application which can not be solved because of the abstract provisions of laws in various countries. If the method of big data can be used as a reference for determining risky driving behaviors of drivers, it can provide evidence for identification, improve the accuracy of judicial judgment, and timely avoid some accidents. This paper aims to explore what application technical support is needed by judicial big data in determining risky driving behaviors of drivers.

Examining Risky Driving Behavior Among Older Drivers Using Self-Reported and Objective Data

Motor vehicle crashes represent a significant public health problem. Efforts to improve driving safety are multifaceted, focusing on vehicles, roadways, and drivers with risky driving behaviors playing integral roles in each area. As part of a study to create guidelines for developing risky driving countermeasures, 480 drivers (118 young/18-25, 183 middle-aged/35-55, 179 older/65 and older) completed online surveys measuring driving history, risky driving (frequency of engaging in distracted [using cell phone, texting, eating/drinking, grooming, reaching/interacting] and reckless/aggressive [speeding, tailgating, failing to yield right-of-way, maneuvering unsafely, rolling stops] driving behaviors), and psychosocial characteristics. A cluster analysis using frequency of the risky behaviors and seat belt use identified five risky behavior-clusters: 1) rarely/never distracted-rarely/never reckless/aggressive (n=392); 2) sometimes distracted-rarely/never reckless/aggressive (n=33); 3) sometimes distracted-sometimes reckless/aggressive (n=40); 4) often/always distracted-often/always reckless/aggressive (n=11); 5) no pattern (n=4). Older drivers were more likely in the first/lowest cluster (93.8% of older versus 84.2% of middle-aged and 59.3% of young drivers; p<.0001). Fifteen older drivers participated in a follow-up study in which their vehicles were equipped with a data acquisition system that collected objective driving and video data of all trips for three weeks. Analysis of video data from 145 older driver trips indicated that older drivers engaged in at least one distracted behavior in 115 (79.3%) trips. While preliminary, this suggests considerably more frequent engagement in distracted driving than self-reported and that older drivers should not be excluded from consideration when developing risky driving behavior countermeasures. Full study results and implications will be presented.

Effect of Culture on Gender Differences in Risky Driver Behavior through Comparative Analysis of 32 Countries

This study examines the effect of culture on gender differences in road user risky behaviors. With the hypothesis that gender differences are not solely because of biological factors, and that the existence and magnitude of differences between gender groups vary according to cultural context, because of differentiated social expectations in relation to gender roles, a secondary analysis was made of the E-Survey of Road Users’ Attitudes (ESRA) 2018 database, comprising 25,459 car drivers (53% male) surveyed by an online questionnaire in 32 countries distributed in eight cultural clusters. The interactions between gender and culture in reported behavior, and personal and social acceptability of four violations were analyzed: drinking and driving, speeding, not wearing a seatbelt, and the use of a cellphone while driving. The results show significant gender differences on risky behaviors and attitudes and complex interactions between gender and culture, with men valuing crash-risk behaviors more than women do in all cultural clusters observed. Interactions between gender and culture are more frequent on declared behaviors and personal acceptability than on perceived social acceptability, and on drinking and driving, and not wearing a seatbelt, more than on speeding and the use of a cellphone while driving. In addition, gender differences are greater in Western countries than in the Global South. These gender differences in road user behaviors, attitudes, and perceptions as results of an interaction between biological and evolutionary factors and cultural and social factors are discussed. These results could be useful to better tailor road safety campaigns and education.

Statistical Evaluation of Risky Driver Behavior Factors that Influence Road Safety based on Drivers Age and Driving Experience in Budapest and Islamabad

Driver behavior is considered as one of the most influential factors on road safety. Most of the drivers on road involve in risky driving attitudes which cause fatal and seriously injured road accidents. This study aims to evaluate and compare the risky driver behavior factors that influence road safety based on drivers age and driving experience for Budapest and Islamabad. To achieve this, the study utilized the well-proved driver behavior questionnaire (DBQ) designed on a three-point scale to analyse statistically the driver behavior responses on perceived road safety issues. The study overall results found that drivers with age group ‘18-21 year’ and drivers with driving experience less than one year are more likely to involve in risky driver behavior factors as compared to other studied groups. Furthermore, the Budapest drivers with age group ‘18-21 year’ and driving experience less than one year are more concerned in risky driver behavior factors such as ‘disregard speed limit’, ‘failing to use personal intelligent assistant’ and ‘frequently changing lanes’. While Islamabad drivers with the same demographic characteristics are more concerned in several risky driver behavior factors as compared to other age and driving experience groups. Moreover, ANOVA analysis was run to measure the statistical significance of risky driver behavior factors between designated groups of drivers. Finally, relative risk (RR) was measured to compare that how much times one driver group is more likely to involve in risky driver behavior factors as compared to the other driver group in the sample. The study highlighted the most frequent risky driver behavior factors for each observed group to help the local policymakers to solve related road safety issues.

Evaluating an Intervention to Reduce Risky Driving Behaviors: Taking the Fear Out of Virtual Reality.

Educational programs are the most common type of intervention to reduce risky driving behavior. Their success, however, depends on the content of the material used and the mode of delivery. In the present study, we examined the impact of fear versus positively framed road safety films and traditional technologies (2D) versus emerging technologies (VR) on young drivers' self-reported risky driving behaviors. One hundred and forty-six university students completed a similar set of questionnaires pre-intervention and post-intervention, two weeks later. In addition, they were randomly assigned to one of the four experimental conditions (VR vs. 2D; positive vs. negative). In the VR conditions, the film was presented using an HTC VIVE Virtual Reality headset. In the 2D conditions, the film was presented on a computer screen. Measures evaluating attitudes toward risky driving behavior were completed at both time frames, questions regarding the participants' emotional arousal were asked at pre-intervention as a manipulation check, and questions regarding willingness to take risks in potentially dangerous driving situations were asked at follow-up. The findings indicate that the positively framed films significantly decreased self-reported risky driving behaviors in both modalities, but especially when viewed in VR format. In contrast, the fear appeal film, when shown in VR, failed to reduce risky driving behaviors, and in fact, increased young drivers' self-reported risky driving behaviors. Theoretical frameworks regarding the strengths and weaknesses of fear appeals and positively framed appeals are discussed to aid future research to reduce risky driving. Practical implications on the future usage of VR are also considered.

The Complex Interrelationship of Work-Related Factors Underlying Risky Driving Behavior of Food Delivery Riders in Athens, Greece.

BackgroundIn this study, the association of work and demographic characteristics with different traffic offenses committed by food delivery riders in Greece was examined. Previous research has identified various factors related to risky driving however, there is a need for exploring the complex interrelationship of work-related factors underlying risky driving behavior.Materials and MethodsA 2-items uestionnaire was used exploring delivery riders demographic characteristics, terms of employment, issues of concern during work and type of traffic offenses committed. In total, uestionnaires were analyzed using logistic regression in order to identify characteristics independently associated with serious traffic offenses, namely, red-light running and helmet non-use.ResultsThe analysis showed that: (i) typical health and safety measures had no effect on serious traffic offenses, (ii) young age was related to both offenses however (iii) different sets of work conditions were associated with reports of red-light running (i.e. low work experience, use of personal vehicle for work, and payment by hour) and helmet non-use respectively (i.e. intense work pace, high tip income per day and low concern about vehicle condition).ConclusionThe above findings provide evidence that serious traffic offenses are manifestations of underlying conflict experienced by the riders between safety and various performance criteria. Each one of the two offenses is related to different rider profiles aiming to satisfy different goals, namely, those mainly trying to maximize profit non-helmet users and those, mostly inexperienced ones, trying to cope with work pressure red light runners. Potential regulatory measures to alleviate risky practices are discussed.

Assessing Significant Factors Affecting Risky Riding Behaviors of Motorcyclists

This study was conducted in Malaysia, where motorcycle traffic accidents represent a high percentage of fatality among overall traffic accidents. Studies have shown that risk perception and positive outcome of risky riding behavior have a significant impact on a rider’s decision making. Therefore, this study is targeted at further understanding of Malaysian motorcyclists within the locality of their home country. A questionnaire survey was conducted to gather motorcycle rider’s information, together with their perception of the three factors mentioned above. A reliability test of the findings was analyzed using Cronbach’s Alpha, while a PCA analysis was conducted to determine the linear combinations that have maximum variance. Subsequently, a statistical model was constructed based on the latent variables’ relations, the relation between the latent variables and observed variables, and also the hypothesis model. The model confirms that the positive affect of the risky behavior has a significant positive relationship with motorcyclists’ risk behavior (estimate coefficient = 1.016). Findings in the model also show that older motorcyclists are less likely to take part in risky riding behavior while riding on the road, with an estimate coefficient of −0.037 and a negative relationship with positive affect (estimate coefficient = −0.032).

Patterns of risky driving behaviors among Tuscan adolescent drivers: a cluster analysis

Abstract Background Adolescents have a high risk of road traffic accident (RTA) because of their high engagement in risky driving behaviors (RDBs); to date, very few studies have investigated the patterns of RDBs. The aim of the study is to identify distinctive RDBs patterns and to examine their associations with RTAs in a sample of adolescent drivers Methods The EDIT project is a cross-sectional survey carried out in a representative sample (6.824) of Tuscany Region students aged 14-19 years. The study analyses a subsample of students who reported to drive/ride at least once a week (2764). Self-reported frequency in the last year of the following RDBs was determined: talking on phone; texting; using GPS; talking to passengers; smoking; eating; listening to loud music; fatigued driving; speeding; and driving under the influence (DUI) of alcohol or drugs. A cluster analysis was conducted to identify RDBs patterns. A multivariate model was used to evaluate the difference in the risk of RTA across clusters; ANOVA and post-hoc pairwise comparisons were used to further characterize cluster membership Results Four distinct RDBs clusters were identified: “safe”(45.6%), “average”(21.8%), “careless but not DUI”(21.5%) and “reckless and DUI”(11.2%) drivers. When compared with “safe” drivers, “careless but not DUI” and “reckless and DUI” drivers showed a significantly higher risk of RTA (respectively, OR 1.68, 95%CI 1.29-2.18, p &amp;lt; 0.001; OR 2.88; 95%CI 2.10-3.95, p &amp;lt; 0.001). Clusters were characterized by several significant differences in sociodemographic variables, cell-phone use, quality of the relationships with parents, school performances, mental health and well-being, health behaviors, gaming, bullying and risky sexual behaviors Conclusions RDBs evidently occur in typical patterns that are linked with different RTA risks. Several domains of adolescent life seem to be involved in cluster membership. An awareness of this clustering enables to better targeting adolescents at higher risk of RTA Key messages RDBs occur in patterns in adolescents, and indicators of risky behaviors and of mental and social well-being may help to identify RDBs clusters at high risk of road traffic accidents. Multimodal prevention approaches in risky driving behaviors are likely to be more successful than targeting a single behavior in adolescents.

Analyzing E-Bikers’ Risky Riding Behaviors, Safety Attitudes, Risk Perception, and Riding Confidence with the Structural Equation Model

To identify and quantify the factors that influence the risky riding behaviors of electric bike riders, we designed an e-bike rider behavior questionnaire (ERBQ) and obtained 573 valid samples through tracking surveys and random surveys. An exploratory factor analysis was then conducted to extract four scales: riding confidence, safety attitude, risk perception, and risky riding behavior. Based on the exploratory factor analysis, a structural equation model (SEM) of electric bike riding behaviors was constructed to explore the intrinsic causal relationships among the variables that affect the risky e-bike riding behavior. The results show that the relationship between riding confidence and risky riding behavior is mediated by risk perception and safety attitudes. Safety attitude was found to be significantly associated with risky riding behaviors. Specifically, herd mentality is most closely related to safety attitudes, which means that those engaged in e-bike traffic management and safety education should pay special attention to riders’ psychological management and education. Risk perception has a direct path to risky riding behaviors. Specifically, stochastic evaluation and concern degree are significantly related to e-bike riders’ risk perception. The findings of this study provide an empirical basis for the creation of safety interventions for e-bike riders in China.

Association between Adult Attention-Deficit/Hyperactivity Disorder and Driving Behaviors among Iranian Motorcyclists.

To investigate the association between attention-deficit/hyperactivity disorder (ADHD) score and driving behaviors among motorcycle drivers in Iran. This multi-center cross-sectional study was conducted on 1747 motorcyclists in three cities of Iran. We used a random sampling method in this study and gathered data using two standard questionnaires. Data were presented using descriptive statistics, also t-test, and ANOVA used for analysis. The mean age of participants was 27.41±8.80 years. ADHD scores of the participants ranged from 0 to 87, with a mean score 31±15.86. All risky driving behaviors (RDBs) were significantly associated with a higher mean of ADHD score. For example, driving with illegal speed (p<0.001), not wearing a crash helmet (p=0.016), driving while exhausting (p<0.001), talking with other passengers (p<0.001), being fined by the police in the past year (p=0.028), and maneuvering while driving (p<0.001) were related to a higher mean of ADHD score. All RDBs were significantly associated with the ADHD score among motorcyclists in Iran. In this regard, health care providers should inform people with ADHD about the negative consequences associated with driving and ADHD. Public health policymakers should consider management of ADHD through a comprehensive approach to improve driving performance and competencies among motorcyclists in order to decrease RDBs and traffic accidents.

Risky Driving Behavior Recognition Based on Trajectory Data

Risky Driving Behavior Recognition Based on Trajectory Data

Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors

Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. This study introduces surrogate safety measures (SSMs) into simulator-based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. The results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. This study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. The risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. The results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

The Relationship between Norms and Risky Driving Behavior: A Systematic Review

Road accident statistics has been seen increasing over the years despite numerous efforts made by the authorities. Human factors have contributed 90% of accident occurrence with risky driving behavior being one of the significant human factors that can be further explained through norms. This review paper aimed to investigate the relationship between norms and drivers' risky driving behavior. A systematic review process was conducted through four academic databases namely Scopus, Wiley Online Library, Emerald and Web of Science of no limitation for date. Overall, 3443 titles were identified and after several screening and reviewing processes, only 27 studies were included. The results of the review demonstrated mixed findings between subjective norm and risky driving behavior, whereas the relationship between group norm, moral norm, injunctive norm, descriptive norm and risky driving behavior were observed significant. Appropriate educational awareness is required to educate the society in practicing good norms for mutual benefit of the society. Parents also need to set a good example for their children by abiding the traffic rules and regulation.

The Role of Impulsivity, Attentional bias and Decision-Making Styles in Risky Driving Behaviors

Background: Road accidents are a major cause of deaths, injuries, and financial losses globally, especially in developing countries. Iran is one of the countries with a high rate of road accidents causing considerable damage in different domains. Therefore, in order to tackle this problem, we need to examine its causes. Objectives: The aim of the present study was to examine the association of risky driving behavior with impulsiveness, attentional bias, and decision-making styles. Patients and Methods: This was a descriptive-correlational study. The sample included 117 male drivers, aged 20 - 34 years, attending car insurance agencies in Tehran. The participants were selected using the convenience sampling method. The data were gathered using the Manchester Driver Behavior Questionnaire (DBQ), the Barratt Impulsiveness Scale (BIS), the Decision-Making Style Scale (DMS), and the Dot Probe Task to assess attentional bias. All data analysis was conducted using Pearson correlation coefficient and multiple regression analysis, by using SPSS, version 22. Results: According to the results of the Pearson correlation coefficient, risky driving behavior was significantly correlated with impulsiveness subscales (P &lt; 0.01) and attentional bias (P &lt; 0.05). In addition, significant relationships were observed between risky driving behaviors and three decision-making styles, including rational (P &lt; 0.05), spontaneous (P &lt; 0.01), and avoidant (P &lt; 0.01). Conclusions: Based on the study results, impulsivity, decision-making styles, and attentional bias as factors influencing drivers’ cognitive skills related to driving, could explain the increase in the frequency of risky driving behavior.

Road Crashes in Adults with Attention Deficit Hyperactivity Disorder and Risky Driving Behavior

Objective: Attention deficit hyperactivity disorder (ADHD) is one of the most common problems in adolescents. Risky behaviors in patients with ADHD are due to impaired impulse control resulting from problems with inhibition of proponent responses, controlling interference, and stopping ongoing responses after feedback on errors. The present study investigated the relationship between ADHD and risky driving behavior and the likelihood of car accident in Arak, Iran, in 2015-16. Method : This case-control study was conducted in the Emergency Department of Vali-Asr hospital in Arak (Iran) on drivers who met the inclusion criteria. The data gathering tools included the Demographic Questionnaire, Manchester Driving Behavior Questionnaire (MDBQ), and Wender Utah Rating Scale (WURS). Statistical analyses were performed using SPSS version 20 software. Results: The mean of ADHD (±SD) was higher among cases (81.64 [26.78]) than in controls (64 [24.28], P = 0.000). The mean of risky driving behaviors (±SD) was higher among cases (66.41[26.78]) than in controls (36.79 [25.42]). There was a significant relationship between ADHD, risky behavior, lapse errors, slips, deliberate ‎violation, and unintentional violation and car accident (P = 0.000). Conclusion: This study showed that ADHD increases the risk of road crashes and motor vehicle injuries. These drivers tend to drive at unauthorized speed, have less control over the vehicle, drive more carelessly, and are more likely to have an accident.

Do People Drive Light Cars Carefully? A Comparative Study of Risky Driving Behaviors between Light Cars and Others

The first aim of this study was to examine whether people drive light cars carefully in comparison with standard-sized cars. The second aim was to evaluate the factors that influence these risky driving indicators. Data were collected from 49 drivers in Aichi Prefecture, Japan, from November 2014 to January 2015. Risky driving behaviors included; (1) speeding, (2) high speed on a non-expressway, (3) high speed on an expressway, (4) high right/left turn rate, (5) long travel, (6) driving at night, (7) driving on an expressway, and (8) driving frequency. At first, the frequency or number of these indicators was compared between the light car group and the standard size car group by a t-test. Second, regression models were established to evaluate the influence of age, gender, living area, and car classification on each risky indicator. The t-test results showed that there was no significant difference in risky driving behaviors between the light car group and the others. The regression models confirmed that car classification did not significantly influence risky driving behaviors. Although age might affect car size selection, an interaction effect between these two factors was not observed. The results of the comparison and regression analysis revealed that drivers of light cars did not drive more carefully than drivers of standard size cars. The risky driving behaviors may partially contribute to the high injury and fatality rates of light cars. Therefore, we suggest that automakers and policymakers should provide more safety education and driving assistance for drivers of light cars.

An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety

Driver behavior plays a major role in road safety because it is considered as a significant argument in traffic accident avoidance. Drivers mostly face various risky driving factors which lead to fatal accidents or serious injury. This study aims to evaluate and prioritize the significant driver behavior factors related to road safety. In this regard, we integrated a decision-making model of the Best-Worst Method (BWM) with the triangular fuzzy sets as a solution for optimizing our complex decision-making problem, which is associated with uncertainty and ambiguity. Driving characteristics are different in different driving situations which indicate the ambiguous and complex attitude of individuals, and decision-makers (DMs) need to improve the reliability of the decision. Since the crisp values of factors may be inadequate to model the real-world problem considering the vagueness and the ambiguity, and providing the pairwise comparisons with the requirement of less compared data, the BWM integrated with triangular fuzzy sets is used in the study to evaluate risky driver behavior factors for a designed three-level hierarchical structure. The model results provide the most significant driver behavior factors that influence road safety for each level based on evaluator responses on the Driver Behavior Questionnaire (DBQ). Moreover, the model generates a more consistent decision process by the new consistency ratio of F-BWM. An adaptable application process from the model is also generated for future attempts.

Application of the AHP-BWM Model for Evaluating Driver Behavior Factors Related to Road Safety: A Case Study for Budapest

The use of driver behavior has been considered a complex way to solve road safety complications. Car drivers are usually involved in various risky driving factors which lead to accidents where people are fatally or seriously injured. The present study aims to dissect and rank the significant driver behavior factors related to road safety by applying an integrated multi-criteria decision-making (MCDM) model, which is structured as a hierarchy with at least one 5 × 5 (or bigger) pairwise comparison matrix (PCM). A real-world, complex decision-making problem was selected to evaluate the possible application of the proposed model (driver behavior preferences related to road safety problems). The application of the analytic hierarchy process (AHP) alone, by precluding layman participants, might cause a loss of reliable information in the case of the decision-making systems with big PCMs. Evading this tricky issue, we used the Best Worst Method (BWM) to make the layman’s evaluator task easier and timesaving. Therefore, the AHP-BWM model was found to be a suitable integration to evaluate risky driver behavior factors within a designed three-level hierarchical structure. The model results found the most significant driver behavior factors that influence road safety for each level, based on evaluator responses on the driver behavior questionnaire (DBQ). Moreover, the output vector of weights in the integrated model is more consistent, with results for 5 × 5 PCMs or bigger. The proposed AHP-BWM model can be used for PCMs with scientific data organized by traditional means.

Risky Driving Behaviour among the Motorized Two Wheeler Novice Riders in Davanagere City, Karnataka

Road traffic accidents(RTA) account for greater mortality and morbidity rates worldwide, resulting in considerable global burden. In India motor vehicle accidents are one of the common reasons for mortality among young riders. The present study provides insight to different aspects of risky driving behavior from individual attitudes, and psychological factors like, anger, mood and emotions. Road traffic accidents are the third leading cause of death among young people. If timely actions are not taken, road traffic accidents are predicted to become the fifth leading cause of death in the world. Considering the issue and concern about younger age group, this study was carried out to understand risky driving behaviour among young novice two wheeler riders. A cross sectional study was conducted among college students, findings from this study says young males are more involved in risky driving behavior. Finally, the results focus on the role that risky driving behaviour plays in two-wheeler motor vehicle accidents and suggests the need for further research in this area of risky driving behaviour to improve road safety education and law enforcement policies that focus upon ensuring good driving behaviours.

Research on Risky Driving Behavior of Novice Drivers

Novice drivers have become the main group responsible for traffic accidents because of their lack of experience and relatively weak driving skills. Therefore, it is of great value and significance to study the related problems of the risky driving behavior of novice drivers. In this paper, we analyzed and quantified key factors leading to risky driving behavior of novice drivers on the basis of the planned behavior theory and the protection motivation theory. We integrated the theory of planned behavior (TPB) and the theory of planned behavior (PMT) to extensively discuss the formation mechanism of the dangerous driving behavior of novice drivers. The theoretical analysis showed that novice drivers engage in three main risky behaviors: easily changing their attitudes, overestimating their driving skills, and underestimating illegal driving. On the basis of the aforementioned results, we then proposed some specific suggestions such as traffic safety education and training, social supervision, and law construction for novice drivers to reduce their risky behavior.