scholarly journals Understanding Traffic Accidents among Young Drivers in Qatar

2022 ◽  
Vol 19 (1) ◽  
pp. 514
Author(s):  
Faris Tarlochan ◽  
Mohamed Izham Mohamed Ibrahim ◽  
Batool Gaben
Keyword(s):  
Traffic Accidents ◽  
Corrective Feedback ◽  
Risky Behaviors ◽  
Driving Behavior ◽  
Self Report ◽  
Measurement Scales ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Young Drivers ◽  
Risky Driving Behaviors

Young drivers are generally associated with risky driving behaviors that can lead to crash involvement. Many self-report measurement scales are used to assess such risky behaviors. This study is aimed to understand the risky driving behaviors of young adults in Qatar and how such behaviors are associated with crash involvement. This was achieved through the usage of validated self-report measurement scales adopted for the Arabic context. A nationwide cross-sectional and exploratory study was conducted in Qatar from January to April 2021. Due to the Covid-19 pandemic, the survey was conducted online. Therefore, respondents were selected conveniently. Hence, the study adopted a non-probability sampling method in which convenience and snowball sampling were used. A total of 253 completed questionnaires were received, of which 57.3% were female, and 42.7% were male. Approximately 55.8% of these young drivers were involved in traffic accidents after obtaining their driving license. On average, most young drivers do have some risky driving behavior accompanied by a low tendency to violate traffic laws, and their driving style is not significantly controlled by their personality on the road. The older young drivers are more involved in traffic accidents than the younger drivers, i.e., around 1.5 times more likely. Moreover, a young male driver is 3.2 times less likely to be involved in traffic accidents than a female driver. In addition, males are only 0.309 times as likely as females to be involved in an accident and have approximately a 70% lower likelihood of having an accident versus females. The analysis is complemented with the association between young drivers’ demographic background and psychosocial-behavioral parameters (linking risky driving behavior, personality, and obligation effects on crash involvement). Some interventions are required to improve driving behavior, such as driving apps that are able to monitor and provide corrective feedback.

Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

2018 ◽  
Vol 2672 (38) ◽  
pp. 233-244 ◽  
Author(s):  
Chaopeng Tan ◽  
Nan Zhou ◽  
Fen Wang ◽  
Keshuang Tang ◽  
Yangbeibei Ji
Keyword(s):  
Phase Transition ◽  
High Speed ◽  
Red Light ◽  
Risky Behaviors ◽  
Risky Driving ◽  
Red Light Running ◽  
Driving Behaviors ◽  
Vehicle Trajectories ◽  
Single Vehicle ◽  
Risky Driving Behaviors

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02 m for single vehicles and 2.33 m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

scholarly journals Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Charles Marks ◽  
Arash Jahangiri ◽  
Sahar Ghanipoor Machiani
Keyword(s):  
Real World ◽  
Traffic Accidents ◽  
Network Models ◽  
Multistage Process ◽  
Neural Network Models ◽  
Risky Driving ◽  
Vehicle Accidents ◽  
Driving Behaviors ◽  
Novel Approach ◽  
Risky Driving Behaviors

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, we describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, we identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

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 Driving Behaviors in Iran: Comparison of Impulsivity, Attentional Bias, and Decision-Making Styles in Safe and High-Risk Drivers

2020 ◽  
Author(s):  
Fatemeh Barati ◽  
Abas Pourshahbaz ◽  
Masode Nosratabadi ◽  
Yasaman Shiasy
Keyword(s):  
Decision Making ◽  
Attentional Bias ◽  
Traffic Accidents ◽  
Function Analysis ◽  
Economic Losses ◽  
Risky Driving ◽  
Safe Driving ◽  
Driving Behaviors ◽  
Decision Making Styles ◽  
Risky Driving Behaviors

Objective: Road traffic injuries are leading cause of death and economic losses, particularly in developing countries such as Iran. Thus, increased understanding of the causes of traffic accidents can help solve this problem. The primary goal of this study was to examine attentional bias, decision-making styles, and impulsiveness in drivers with safe or risky driving behaviors. The secondary purpose was to determine the variance of each variable among 2 groups of drivers. Method: This was a cross sectional design study, in which 120 male drivers aged 20-30 years (60 males with risky driving behaviors and 60 with safe driving behaviors) were recruited from Tehran using sampling technique. Barratt Impulsiveness Scale (BIS), Decision-Making Style Scale (DMSQ), Manchester Driver Behavior Questionnaire (MDBQ), Self-Assessment Manikin Scale (SAM), and Dot Probe Task were used. The analyses were performed using IBM SPSS version 22. Results: The mean age of participants was 26 years. Significant differences were found between impulsiveness (attentional, motor, and non planning impulsiveness) and decision-making styles (spontaneous and avoidant) between the 2 groups. Also, based on the results of discriminant function analysis (DFS), the subscales of impulsiveness and 2 decision-making styles explained 25% of the variance in the 2 groups of risky and safe drivers. Conclusion: Findings of this study indicated that impulsiveness and 2 decision-making styles were predominant factors. Therefore, not only is there a need for research to reduce traffic accidents, but studies can also be helpful in issuing driving licenses to individuals.

scholarly journals Type A Behavior Pattern, Risky Driving Behaviors, and Serious Road Traffic Accidents: A Prospective Study of the GAZEL Cohort

2005 ◽  
Vol 161 (9) ◽  
pp. 864-870 ◽  
Author(s):  
Hermann Nabi ◽  
Silla M. Consoli ◽  
Jean-François Chastang ◽  
Mireille Chiron ◽  
Sylviane Lafont ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Behavior Pattern ◽  
Type A Behavior ◽  
Road Traffic Accidents ◽  
Risky Driving ◽  
Type A Behavior Pattern ◽  
Driving Behaviors ◽  
A Prospective Study ◽  
Risky Driving Behaviors

scholarly journals Profiles of Risky Driving Behaviors in Adolescent Drivers: A Cluster Analysis of a Representative Sample from Tuscany Region (Italy)

2021 ◽  
Vol 18 (12) ◽  
pp. 6362
Author(s):  
Vieri Lastrucci ◽  
Francesco Innocenti ◽  
Chiara Lorini ◽  
Alice Berti ◽  
Caterina Silvestri ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
High Risk ◽  
Representative Sample ◽  
Road Traffic ◽  
Risky Behaviors ◽  
Cross Sectional ◽  
Risky Driving ◽  
Cluster Membership ◽  
Driving Behaviors ◽  
Risky Driving Behaviors

(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14–19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters—the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)—showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation.

Association of Environmental Risk Factors and Personality Traits With Risky Driving Behaviors in a Sample of Young Adults From Saudi Arabia

2021 ◽  
pp. 0272684X2110049
Author(s):  
Kevin M. Beaver ◽  
Mohammed Said Al-Ghamdi ◽  
Ahmed Nezar Kobeisy
Keyword(s):  
Risk Factors ◽  
Saudi Arabia ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Self Control ◽  
Risky Driving ◽  
Male And Female ◽  
Driving Behaviors ◽  
Saudi Arabians ◽  
Risky Driving Behaviors

Road traffic accidents represent a serious problem in the Kingdom of Saudi Arabia (KSA), with rates of such accidents far exceeding the rates in developed nations. Even so, there remains relatively little knowledge regarding the driving behaviors among Saudi Arabians. The current study sought to address this gap in the literature by examining the environmental and trait-based contributors to risky driving behaviors among male and female drivers in the KSA. To do so, a sample of college students from a large university in the KSA was analyzed. The results revealed that delinquent peers, low levels of self-control, and higher levels of driving anger were associated with involvement in risky driving behaviors for both male and female drivers. Understanding the interconnections among peers, self-control, anger, and risky driving behaviors may provide some insight into how to reduce risky driving behaviors. Focusing on ways to reduce exposure to risk factors for risky driving behaviors may be one strategy for reducing these types of driving behaviors.

Risky Driving Behaviors of Drivers Who Use Alcohol and Cannabis

2021 ◽  
pp. 036119812198972
Author(s):  
Tara Kelley-Baker ◽  
Leon Villavicencio ◽  
Lindsay S. Arnold ◽  
Aaron J. Benson ◽  
Victoria Anorve ◽  
...  
Keyword(s):  
Prescription Drugs ◽  
Traffic Safety ◽  
Risky Behaviors ◽  
The United States ◽  
Driving Under The Influence ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Effective Interventions ◽  
Nationally Representative ◽  
Risky Driving Behaviors

Many drivers in the United States use alcohol or cannabis, including some who co-use both substances. Using data from a nationally representative survey, self-reported engagement in various risky driving behaviors is examined among drivers who co-use alcohol and cannabis, those who use alcohol but not cannabis, those who use cannabis but not alcohol, and those who use neither. Results were adjusted for age, gender, education, and race. Co-users, compared with those who use only alcohol, were more likely to engage in nearly all of the risky behaviors measured in the survey, including driving under the influence of alcohol. Compared with those who neither drink nor use cannabis, those who use only cannabis were more likely to drive under the influence of prescription drugs, engage in aggressive driving, and ride with an intoxicated driver. Results of this and future related research will assist with understanding the differences in driving behavior among users of alcohol, cannabis, or both, so more effective interventions can be developed to improve traffic safety.

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