Trait Anger Cause Risky Driving Behavior by Influencing Executive Function and Hazard Cognition

2021 ◽  
Author(s):  
Zhenhao Yu ◽  
Weina Qu ◽  
Yan Ge
Keyword(s):  
Executive Function ◽  
Protective Factor ◽  
Negative Binomial ◽  
Driving Behavior ◽  
Medium Effect ◽  
Trait Anger ◽  
Road Rage ◽  
Online Questionnaire ◽  
Risky Driving ◽  
Relevant Variable

Road rage is a serious phenomenon around the world in the driving context and may contribute to risky driving behavior, further increasing the probability of collisions. Among several factors, trait anger is the most relevant variable towards road rage. This research aims to interpret how trait anger influences risky driving behavior in detail. We used an online questionnaire, which contains trait anger scale (TAS), executive function index (EFI), hazard cognition scale (HCS; represents attitudes towards risky driving behavior), driver behavior questionnaire (DBQ), and self-reported traffic violations (e.g., accidents, penalty points, fines). The linear regression model showed that trait anger is a medium but statistically significant predictor of risky driving behavior and drivers’ attitude towards risky situations can significantly predict risky driving behavior in statistics up to medium effect. But risky driving behavior cannot be predicted by executive function. Interestingly, for the objective indicators, the zero-inflated Poisson regression or negative binomial regression results suggested that age is a small protective factor towards accidents/penalty points/fines, and trait anger also is a small protective factor in accidents/fines. While executive function alleviates penalty points and fines, whereas hazard cognition alleviates penalty points only. They all represented a small effect on risky driving behavior. Path analysis suggested that trait anger influences risky driving behavior through executive function and hazard cognition. This study provides a theoretical framework for further research about road rage and offers some possible intervene towards road rage.

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

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 465-465
Author(s):  
Jennifer Zakrajsek ◽  
Lisa Molnar ◽  
David Eby ◽  
David LeBlanc ◽  
Lidia Kostyniuk ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Seat Belt ◽  
Driving Behavior ◽  
Video Data ◽  
Older Drivers ◽  
Driving Frequency ◽  
Middle Aged ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Study Results

Abstract 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.

scholarly journals Road Casualties and Changes in Risky Driving Behavior in France Between 2001 and 2004 Among Participants in the GAZEL Cohort

2009 ◽  
Vol 99 (7) ◽  
pp. 1247-1253 ◽  
Author(s):  
Aymery Constant ◽  
Louis Rachid Salmi ◽  
Sylviane Lafont ◽  
Mireille Chiron ◽  
Emmanuel Lagarde
Keyword(s):  
Driving Behavior ◽  
Risky Driving

Personality Traits, Boredom, and Loneliness as Predictors of Facebook Use in On-Campus and Online University Students

2017 ◽  
Vol 7 (2) ◽  
pp. 36-48 ◽  
Author(s):  
Jason Skues ◽  
Ben J. Williams ◽  
Lisa Wise
Keyword(s):  
Personality Traits ◽  
Undergraduate Students ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Big Five Personality ◽  
Online Questionnaire ◽  
Psychological Variables ◽  
Two Samples ◽  
Facebook Use ◽  
Online University Students

This study examined the relationship between individual differences (Big Five personality traits, self-esteem, loneliness, narcissism, shyness, and boredom) and social networking behaviours in two samples of Australian undergraduate students, one enrolled on-campus (n = 93) and another in a completely online (n = 113) version of the same subject. Participants completed an online questionnaire measuring personality traits, psychological variables, and Facebook use. Negative binomial regression models showed that on-campus students with higher levels of neuroticism, extraversion, and loneliness tended to have more Facebook friends, however, no significant predictors of number of friends were found for online students. There were no significant predictors of time spent using Facebook per day for either cohort. Contrary to expectations, boredom was not a significant predictor of time spent on Facebook for on-campus students, but the low participation and completion rate for this on-campus group suggests that students high on boredom proneness were unlikely to have completed the survey.

scholarly journals Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jinshuan Peng ◽  
Yiming Shao
Keyword(s):  
Neural Network ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Time Window ◽  
Driving Behavior ◽  
Practical Significance ◽  
Accurate Identification ◽  
Risky Driving ◽  
Bayesian Filter ◽  
Identification Rate

Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

scholarly journals Research on Risky Driving Behavior of Novice Drivers

2019 ◽  
Vol 11 (20) ◽  
pp. 5556
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Xiaoyan Zhu ◽  
Yule Luo ◽  
Yi Luo
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Risky Behaviors ◽  
Driving Behavior ◽  
Risky Driving ◽  
Novice Drivers ◽  
Driving Skills ◽  
Planned Behavior Theory

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.

scholarly journals A survey on driving behavior analysis in usage based insurance using big data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Subramanian Arumugam ◽  
R. Bhargavi
Keyword(s):  
Big Data ◽  
Insurance Industry ◽  
Building Blocks ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Road Rage ◽  
Auto Insurance ◽  
Emotional Factors ◽  
The Face ◽  
Pay How You Drive

Abstract The emergence and growth of connected technologies and the adaptation of big data are changing the face of all industries. In the insurance industry, Usage-Based Insurance (UBI) is the most popular use case of big data adaptation. Initially UBI is started as a simple unitary Pay-As-You-Drive (PAYD) model in which the classification of good and bad drivers is an unresolved task. PAYD is progressed towards Pay-How-You-Drive (PHYD) model in which the premium is charged for the personal auto insurance depending on the post-trip analysis. Providing proactive alerts to guide the driver during the trip is the drawback of the PHYD model. PHYD model is further progressed towards Manage-How-You-Drive (MHYD) model in which the proactive engagement in the form of alerts is provided to the drivers while they drive. The evolution of PAYD, PHYD and MHYD models serve as the building blocks of UBI and facilitates the insurance industry to bridge the gap between insurer and the customer with the introduction of MHYD model. Increasing number of insurers are starting to launch PHYD or MHYD models all over the world and widespread customer adaptation is seen to improve the driver safety by monitoring the driving behavior. Consequently, the data flow between an insurer and their customers is increasing exponentially, which makes the need for big data adaptation, a foundational brick in the technology landscape of insurers. The focus of this paper is to perform a detailed survey about the categories of MHYD. The survey results in the need to address the aggressive driving behavior and road rage incidents of the drivers during short-term and long-term driving. The exhaustive survey is also used to propose a solution that finds the risk posed by aggressive driving and road rage incidents by considering the behavioral and emotional factors of a driver. The outcome of this research would help the insurance industries to assess the driving risk more accurately and to propose a solution to calculate the personalized premium based on the driving behavior with most importance towards prevention of risk.

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