Risky Driving Research Articles

An Observational Study on Speeding among Malaysian Express Bus Drivers

Accidents involving express buses in Malaysia tend to attract a lot of local media attention. Based on statistics from the Royal Malaysia Police (RMP), express buses recorded the highest percentage of total bus crashes during the 3-year period from 2007 to 2009. In this study, data were collected from three different festive seasons in Malaysia with express bus drivers' behaviour being the main focus. Express buses and the journey they took were randomly selected regardless of the express bus company. Data collected from observations were analysed with respect to speeding. Findings of this study reveal that drivers were more likely to exceed the posted speed limit during night-time. Smoking and eating while driving were significantly associated with speeding. The study suggests that further efforts aimed at reducing risky bus driver behaviour demand critical consideration and actions from relevant enforcement agencies and stakeholders. In addition, express bus operators are encouraged to monitor the risky driving behaviour of drivers using suitable monitoring system.

The HEXACO Model of Personality and Risky Driving Behavior.

This research tested the association between the HEXACO personality model and risky driving behavior as well as the predictive power of the HEXACO model in explaining risky driving behavior compared with the Big Five model. In Sample 1, 227 undergraduate students completed measures of the HEXACO personality model, the Big Five model, and driving aggression. In Sample 2, 244 community respondents completed measures of the HEXACO personality model, the Big Five model, and driving styles. Results showed that the Honesty-Humility factor is an important addition to personality models that aim to explain risky driving behavior as being related to all forms of driving aggression as well as to maladaptive and adaptive driving styles and having incremental validity in predicting verbally aggressive expression, risky driving, high-velocity driving, and careful driving. Moreover, compared with the Big Five model, the HEXACO model had better predictive power of aggressive driving.

Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing

This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.

Influences of Fertility Status on Risky Driving Behaviour

SummaryThe effects of hormones on human behaviour have been extensively studied, but little attention has been paid to the influence of ovarian hormones on risky driving. Twenty‐five normally cycling women took part in three sessions, including an ovulatory phase estimation session and two experimental sessions: high vs low fertile phases. These two phases were monitored through a urine‐based luteinizing hormone predictor test. In the two experimental sessions, participants were administered the Driving Behaviour Questionnaire and the Vienna Risk‐Taking Test. Results showed that women are more risk‐averse in their driving behaviour during their high‐fertile phase. The influence of hormonal fluctuations on self‐perception of risk attitude when driving was non‐significant. Findings are discussed from an evolutionary perspective. Copyright © 2016 John Wiley & Sons, Ltd.

Self-concept as a risky driver: Mediating the relationship between racing video games and on-road driving violations in a community-based sample

PurposeThe mediating relationship of self-conceptions as a risky driver on self-reported driving violations was examined for players of “drive’em up” and “circuit” racing video games using an Internet survey of automobile and racing club members. Structural equation modelling (SEM) tested Fischer et al. (2012) extended socio-cognitive model on the effects of risk-glorifying media on cognitions and actions. MethodAn Internet questionnaire was developed and relied upon validated instruments or questions derived from previous surveys. Driver club members were asked about: (1) their frequency of video game playing, (2) self-perceptions as a risky driver and (3) self-reported driving violations. SEM was performed to examine mediating effects of racing video game playing on self-reported driving violations. ResultsPlaying “drive‘em up” video games positively predicted risky self-concept (β=.15, t=2.26), which in turn, positively predicted driving violations (β=.73, t=8.63), while playing “circuit racing” games did not predict risky self-concept, although risky self-concept did predict driving violations (β=.72, t=8.67). ConclusionsSelf-concept as a risky driver mediated the relationship between racing video game playing and self-reported driving violations for “drive’em up”, but not for “circuit racing” video games. These findings are congruent with Fischer and colleagues’ experimental model that self-concept as a reckless driver mediated the relationship between racing video game playing for “drive’em up”, but not for “circuit racing” games and risk-taking behavior in a video of road traffic scenarios.

What factors determine metro passengers’ risky riding behavior? An approach based on an extended theory of planned behavior

In China metro interruptions and casualties are largely attributable to incidents involving passengers. The present study examined metro passengers’ motivations to ride in potentially risky ways by focusing on belief-based measures of the theory of planned behavior (TPB), supplemented with several variables expected to be predictive. A survey based on this extended TPB, operationalized with respect to last-second riding, pushy riding and door-forcing scenarios, was conducted on a sample of regular passengers (N=576, 576 and 571, respectively). In support of the TPB, after controlling for the effects of passengers’ characteristics, the behavioral, normative and control beliefs accounted for a significant part of the variance in intentions to ride dangerously as depicted in the scenarios, with behavioral beliefs and control beliefs being consistently predictive across the behaviors. The inclusion of moral norms, past behavior, perceived risk and self-identity resulted in a significant increment in explained variance for each intention investigated, with past behavior being consistently predictive while moral norms and perceived risk showing significance in some scenarios and self-identity being consistently insignificant across the behaviors. Further, an extensive set of belief targets for metro safety interventions were identified by selecting those beliefs that differed most between those who intended to commit risky riding and those who did not. Theoretical and practical implications of the findings are discussed from the perspective of reducing passengers’ risky riding tendencies.

The Social Determinants of Risky Driving on the Intercity Roads of Tehran Province, Iran: A Case-Cohort Study

Background Traffic accidents are the major cause of injuries that endanger the lives of many people annually. It seems that studying the factors and grounds of this type of event including risky driving contributes to the identification of groups at risk and development of preventive programs. Objectives Given the importance of this issue, we decided to conduct a study to examine the social factors of risky driving. Methods The present study was implemented based on a case-cohort design. The target community was all the drivers who drove on the intercity roads of Tehran Province. Each driver with and without a history of being guilty in a car accident leading to injury or death during the last 5 years was assigned to the case and control groups, respectively. The questionnaires were completed through a review of documents and interview within the groups. In the analytical analysis, a chi-square test and, if necessary, the odds ratio and confidence intervals were used to determine the relationship between the variables. In each case, the necessary investigation of confounding or interacting variables was performed using regression models and the final model of the factors affecting a risky driving was extracted. Results From a total of 990 drivers studied, 54 cases (5.5%) were females and 936 (94.5%) were males. The mean and SD of the drivers’ ages were 39.4 and 11.8 years, respectively. People with a driving job, chronic disease, poor socio-economic status, having only a family dispute, without a religious attitude, and under medical supervision (all with P < 0.001), secondary education (P = 0.01), women (P = 0.01), using drugs (P = 0.03) were found to have a greater history of road traffic injuries or deaths. Conclusions This study showed that gender, education level, occupation, socioeconomic status, medical care, health condition, lifestyle, family conflict, drug abuse history, and religious attitudes are the major social factors of risky driving on the intercity roads of Tehran Province.

Combat Veterans’ Strategies to Manage Risky Driving and Preferences for Driving Intervention

Abstract Date Presented 4/7/2016 Combat veterans face an increased risk of motor vehicle crash, injury, or death and report driving difficulty. Our focus group addressed five themes: deployment training, deployment driving experiences, postdeployment driving experiences and challenges, strategies, and intervention preferences. Primary Author and Speaker: Sandra Winter Additional Author and Speaker: Emily Szafranski Contributing Authors: Sherrilene Classen, Abraham Yarney, Miriam Monahan, Kyle Platek, Amanda Lutz, Charles Levy

Commentary on Cavazos-Rehg and Colleagues: Selected State Policies and Associations with Alcohol Use Behaviors and Risky Driving Behaviors Among Youth-Findings from the Monitoring the Future Study.

Commentary on Cavazos-Rehg and Colleagues: Selected State Policies and Associations with Alcohol Use Behaviors and Risky Driving Behaviors Among Youth-Findings from the Monitoring the Future Study.

Covariability in three dimensions of teenage driving risk behavior: impaired driving, risky and unsafe driving behavior, and secondary task engagement

ABSTRACTObjective: This research examined the extent to which teenagers who engaged in one form of risky driving also engaged in other forms and whether risky driving measures were reciprocally associated over time.Methods: The data were from waves 1, 2, and 3 (W1, W2, and W3) of the NEXT Generation study, with longitudinal assessment of a nationally representative sample starting with 10th graders starting in 2009–2010. Three measures of risky driving were assessed in autoregressive and cross-lagged analyses: driving while alcohol/drug impaired (DWI), Checkpoints Risky Driving Scale (risky and unsafe driving), and secondary task engagement while driving.Results: In adjusted autoregression models, the risk variables demonstrated high levels of stability, with significant associations observed across the 3 waves. However, associations between variables were inconsistent. DWI at W2 was associated with risky and unsafe driving at W3 (β = 0.21, P < .01); risky and unsafe driving at W1 was associated with DWI at W2 (β = 0.20, P < .01); and risky and unsafe driving at W2 is associated with secondary task engagement at W3 (β = 0.19, P < .01). Over time, associations between DWI and secondary task engagement were not significant.Conclusions: Our findings provide modest evidence for the covariability of risky driving, with prospective associations between the Risky Driving Scale and the other measures and reciprocal associations between all 3 variables at some time points. Secondary task engagement, however, appears largely to be an independent measure of risky driving. The findings suggest the importance of implementing interventions that addresses each of these driving risks.

Development of Model to Identify the Factors for Risky Driving Behaviour by Analyzing EEG Waveform

Development of Model to Identify the Factors for Risky Driving Behaviour by Analyzing EEG Waveform

Selected State Policies and Associations With Alcohol Use Behaviors and Risky Driving Behaviors Among Youth: Findings from Monitoring the Future Study.

Effective policies that can reduce alcohol use behaviors and impaired driving among young people at a population level are needed. Graduated driver licensing (GDL) laws increase the driving privileges of young novice drivers as they age and gain more driving experience. In this study, we seek to determine the effects of GDLs on risky driving behaviors of youth and to assess if GDLs have an unintended effect on underage drinking behaviors. We utilized 2000 to 2013 data on 12th grade students from the Monitoring the Future (MTF) study, an ongoing, annual national survey (since 1975) that studies the substance use behaviors of adolescents, as well as data on GDL laws obtained via the Insurance Institute for Highway Safety (IIHS). We conducted a series of regular logistic regression models that included fixed effects for year and state, and adjusted for demographic characteristics, school characteristics, and other state alcohol policies. Total weighted sample size was 129,289 12th graders. Past month alcohol use and binge drinking (i.e., ≥5 drinks on one occasion) in the past 2weeks were 45 and 26%, respectively. Seventeen percent of respondents reported riding with a driver who drank alcohol. Nearly 12% reported driving in the past 2weeks after drinking alcohol, and 7% reported driving after binge drinking. Over half of the students lived in a state with a "good" GDL law. The logistic regression models suggest a link between restrictive GDL policies and a reduction of alcohol use behaviors and risky driving behaviors among youth. Our findings indicate that the effects of GDLs extend beyond driving-related risks and into other drinking-related behaviorsthat pose immediate or delayedhealth risks for young people. We speculate that GDLs may dictate social norms and expectations for youth risk behaviors, and should be maximized throughout the United States.

Abstract WP151: Increased Risky Driving in Acute Compared to Chronic Ischemic Stroke Drivers

Introduction: Guidelines recommend that patients refrain from driving in the acute phase of stroke. Patients are permitted to resume driving if no significant motor, cognitive, or perceptual deficits are present. This study investigated the driving performance of patients with mild ischemic stroke in the acute and chronic stages of recovery. It was hypothesized that patients with acute stroke would commit more risky driving errors (e.g. collisions, lane deviations) and that patients with chronic stroke would perform comparably to healthy matched controls. Methods: Driving simulator technology (STISIM) was used to compare the driving performance of 11 patients with acute mild ischemic stroke (NIHSS&lt;5, &lt;1 month post-stroke), 11 patients with chronic mild ischemic stroke (NIHSS&lt;5 at discharge, &gt;3 months post-stroke), and 11 healthy age- and education-matched controls. Participants completed a city-driving task and a car following task, which requires sustained attention and error monitoring. Results: Patients with acute stroke committed over three times as many risky driving errors compared to healthy controls during the city-driving task (6.2 vs. 1.7, p&lt;0.01). Acute stroke patients made more errors (8.7 vs. 2.0, p&lt;0.05) and had difficulty maintaining proper lane position (p&lt;0.05) compared to controls during the car following task. Patients with chronic stroke committed fewer errors than acute stroke patients during the car following task (3.0 vs. 8.7, p&lt;0.05) and tended to commit fewer risky driving errors during the city-driving task (2.7 vs. 6.2, p=0.076). Patients with chronic stroke performed comparably to controls during the driving tasks. Conclusions: Results suggest that patients with acute mild ischemic stroke may demonstrate impaired driving ability and that performance may improve in the chronic phase of recovery. The results reinforce the importance of refraining from driving in the acute phase of stroke and suggest that patients with good functional and cognitive recovery may be safe to resume driving in the chronic phase of injury. A large-scale longitudinal study is required to determine the optimal time for patients to resume driving after stroke.

Are Perceptions About Driving Risk and Driving Skill Prospectively Associated with Risky Driving Among Teenagers?

The objective of this study was to examine prospective associations of perceptions of driving risk and skill with teenage risky driving. The vehicles of 42 newly licensed teenage drivers were instrumented with a data acquisition system. Objective measures of risky driving [crash and near crash (CNC) and kinematic risky driving (KRD) rates] obtained from accelerometers were aggregated over three 6-month periods (T1, T2, and T3). The Checkpoints Self-Reported Risky Driving Scale (C-RDS) and perceptions of driving risk and driving skills were collected at corresponding 6-month intervals. The results indicated that CNC, KRD, and C-RDS were significantly correlated at T1 to T3. Perceptions of driving risk and skill were not significantly correlated with objective or self-reported measures of risky driving. No evidence was found that perceptions of risk or skill were prospectively associated with risky driving. It was concluded that perceptions of risk and driving skills may have limited utility as objectives of prevention efforts.

Modeling Risky Driver Behavior Under the Influence of Flashing Green Signal with Vehicle Trajectory Data

In China, a 3-s flashing green (FG) indication followed by a 3-s yellow (Y) indication is commonly applied to end the green phase at signalized intersections regardless of speed limits. Past research has indicated that the FG may bring more uncertainty and dynamics to drivers’ stop–go decisions at the end of the green phase, particularly at high-speed intersections. Hence, the objective of this study was to propose a probabilistic model for interpreting drivers’ dynamic decision-making processes in response to the combination of FG and Y at rural high-speed intersections, which could help quantitatively analyze the safety effects of FG. The inputs of the proposed model included mainly the acceleration and deceleration rate and the perception-and-reaction time. The outputs were the frequencies of risky behavior, including abrupt stop and aggressive pass on the basis of the cycle. The trajectories of 1,186 vehicles, collected at three rural high-speed intersections in Shanghai, China, with a speed limit of 80 km/h were used for the uncertainty analysis of input variables and model validation. On the basis of the proposed model, three typical scenarios of phase transition periods—3-s FG + 3-s Y, 0-s FG + 5-s Y, and 0-s FG + 3-s Y—were valuated. Results showed that the proposed model was capable of producing an average error of less than 10%. In addition, the scenario of 0-s FG + 5-s Y performed best in reducing risky behavior. It is recommended that a 5-s Y be implemented for high-speed intersections when most of the traffic consists of passenger cars.

Driver Personality as a Valid Predictor of Risky Driving

A multifaceted activity, driving requires experience and a number of skills and personality and motivational prerequisites. Research has shown that such skills are not enough to ensure safe driving in the long term. In addition to the driver's performance characteristics, modern research into traffic psychology points out the role of personality traits and motivational factors: not only do the driver's capacities (skills) need to be assessed, but also motivational and personality factors (what they want to do). The present paper reports the results of a study of a group of 2,471 Czech drivers who underwent psychological assessment of their fitness to drive in the period 2013-2014. The reason for this procedure was their driving licence being suspended because of their reaching 12 points in the demerit point system or a single serious traffic violation (such as DUI or speeding). Another group comprised professional drivers with no previous problems in the performance of their work. Personality traits (personality tests – e.g. NEO-PI-R, PSSI) and skills (performance tests – e.g. VMT, D2, IST-2000-R) were assessed. The results indicate differences in personality traits between the groups of risk drivers (licences suspended) and good drivers (professional drivers). As for the performance characteristics, the differences between the two groups were not significant.

Analysing Self-Reported Risky Behaviours of Motorcyclists in Bali using Structural Equation Modelling

This study investigates cause and effect relationships among risk perception, positive affect and risky behaviours of motorcyclists in Denpasar, Bali using Structural Equation Modelling. Questionnaires were distributed to collect age, gender, self-reported risky riding behaviour, perceived risk and positive affect of motorcyclists. This study found that positive affect influences more than risk perception on risky riding behaviours. Accordingly, this shows that motorcyclists on the road are relied more on intuitive than rational processes. Motorcyclists have also been found to consistently perceive and behave on such a risky behaviour that may distract them on the road, for instance using a mobile phone while riding. In addition, riding fast on curves is a behaviour factor which strongly indicates both rational and intuitive processes of motorcyclists' decision making while on the road. Further studies should be more focused on the influence of positive affect of motorcyclists' ages and gender on risky riding behaviours.

Risky driving among UK regular armed forces personnel: changes over time

ObjectivesTo compare the prevalence of self-reported risky driving in a sample of UK military personnel at 2 different time points (2004 and 2009), and to identify the incidence of new...

English

English

Risky Driving Behaviours among Medical Students in Erbil, Iraq.

This study aimed to assess risky driving behaviours among medical students in Erbil, Iraq, and to explore the relationship between risky driving behaviours and perceptions of risky driving. This self-administered questionnaire-based survey was conducted from January to May 2014 among a random sample of 400 medical students at Hawler Medical University in Erbil. The questionnaire was designed to assess the frequency of engagement in 21 risky driving behaviours, the perceived risk of each behaviour and the preference for each behaviour as ranked on a 5-point scale. A total of 386 students responded to the survey (response rate: 96.5%). Of these, 211 reported that they currently drove a vehicle (54.7%). Drivers most frequently engaged in the following behaviours: playing loud music (35.9%), speeding (30.4%), allowing front seat passengers to not wear seat belts (27.9%) and using mobile phones (27.7%). Least frequent driving behaviours included not stopping at a red light (3.9%), driving while sleepy (4.4%), driving after a mild to moderate intake of alcohol (4.5%) and drunk driving (6.4%). Mean risky driving behaviour scores were significantly higher among males (P <0.001) and those who owned a car (P = 0.002). The mean risk perception score was higher among >20-year-olds (P = 0.028). There was a significant positive relationship between the preference for risky behaviours and risky driving behaviours (beta = 0.44; P <0.001). Medical students in Erbil reported high frequencies of several serious risky driving behaviours. The preference for risky behaviours was found to be an important predictor of risky driving behaviours among medical students in Erbil.