Risky driving behaviour in young people: prevalence, personal characteristics and traffic accidents

The present research aimed to assess some predictors for experiencing traveling in a car with a driver who has consumed alcohol, in a group of young people, aged between 18 and 20 years, residents of Timis County, Romania. The study group of 1606 young subjects, 18-20 years of age, 51.4% pupils and 48.6% students, with girls being significantly better represented, was applied a transversal population study. Percents of 29.8% of boys and 28.4% of girls got 1-3 times in a car with a drunk driver, and 10.1% of boys and 6.5% of girls traveled in such circumstances more than 4 times. Boys tend to accept the risks of traveling in a car with a drunk driver significantly more frequently than girls. We identified some predictors for traveling with a driver who has consumed alcohol, such as the binge drinking model and the model of mixed alcohol and drugs consumption, the practice of alcohol consumption associated with vehicle driving by the father, as well as by friends.

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Effect of High-Altitude Environment on Driving Safety: A Study on Drivers’ Mental Workload, Situation Awareness, and Driving Behaviour

Journal of Advanced Transportation ◽

10.1155/2020/7283025 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xinyan Wang ◽

Wu Bo ◽

Weihua Yang ◽

Suping Cui ◽

Pengzi Chu

Keyword(s):

High Altitude ◽

Situation Awareness ◽

Mental Workload ◽

Mediating Effect ◽

Driving Safety ◽

Risky Driving ◽

Driving Behaviour ◽

Driving Risk ◽

Low Altitude ◽

The Relationship

This study aims to analyze the effect of high-altitude environment on drivers’ mental workload (MW), situation awareness (SA), and driving behaviour (DB), and to explore the relationship among those driving performances. Based on a survey, the data of 356 lowlanders engaging in driving activities at Tibetan Plateau (high-altitude group) and 341 lowlanders engaging in driving activities at low altitudes (low-altitude group) were compared and analyzed. The results suggest that the differences between the two groups are noteworthy. Mental workload of high-altitude group is significantly higher than that of low-altitude group, and their situation awareness is lower significantly. The possibility of risky driving behaviours for high-altitude group, especially aggressive violations, is higher. For the high-altitude group, the increase of mental workload can lead to an increase on aggressive violations, and the situation understanding plays a full mediating effect between mental workload and aggressive violations. Measures aiming at the improvement of situation awareness and the reduction of mental workload can effectively reduce the driving risk from high-altitude environment for lowlanders.

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

Complexity ◽

10.1155/2018/1801273 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 5

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.

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

Journal of Advanced Transportation ◽

10.1155/2021/8819094 ◽

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.

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Identifying the Characteristics of Risky Driving Behaviour

Driver Behaviour and Training: Volume VI ◽

10.4324/9781315257372-14 ◽

2016 ◽

pp. 83-94

Keyword(s):

Risky Driving ◽

Driving Behaviour

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Research on Risky Driving Behavior of Novice Drivers

Sustainability ◽

10.3390/su11205556 ◽

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.

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