Speeding and Other Risky Driving Behavior among Young Drivers

2016 ◽  
pp. 145-165
Keyword(s):  
Driving Behavior ◽  
Risky Driving ◽  
Young Drivers

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.

scholarly journals Rethinking Cell Phone Use While Driving: Isolated Risk Behavior or a Pattern of Risk-Taking Associated with Impulsivity in Young Drivers?

2021 ◽  
Vol 18 (11) ◽  
pp. 5640
Author(s):  
Elizabeth A. Walshe ◽  
Flaura K. Winston ◽  
Dan Romer
Keyword(s):  
Personality Traits ◽  
Cell Phone ◽  
General Pattern ◽  
The United States ◽  
Equation Modeling ◽  
Risky Driving ◽  
Safe Driving ◽  
Cell Phone Use ◽  
Young Drivers ◽  
Related Personality

This study examines whether cell phone use stands apart from a general pattern of risky driving practices associated with crashes and impulsivity-related personality traits in young drivers. A retrospective online survey study recruited 384 young drivers from across the United States using Amazon’s Mechanical Turk to complete a survey measuring risky driving practices (including cell phone use), history of crashes, and impulsivity-related personality traits. Almost half (44.5%) of the drivers reported being involved in at least one crash, and the majority engaged in cell phone use while driving (up to 73%). Factor analysis and structural equation modeling found that cell phone use loaded highly on a latent factor with other risky driving practices that were associated with prior crashes (b = 0.15, [95% CI: 0.01, 0.29]). There was also an indirect relationship between one form of impulsivity and crashes through risky driving (b = 0.127, [95% CI: 0.01, 0.30]). Additional analyses did not find an independent contribution to crashes for frequent cell phone use. These results suggest a pattern of risky driving practices associated with impulsivity in young drivers, indicating the benefit of exploring a more comprehensive safe driving strategy that includes the avoidance of cell phone use as well as other risky practices, particularly for young drivers with greater impulsive tendencies.

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 Identifying Interactive Factors That May Increase Crash Risk Between Young Drivers and Trucks: A Narrative Review

2021 ◽  
Vol 18 (12) ◽  
pp. 6506
Author(s):  
Melissa R. Freire ◽  
Cassandra Gauld ◽  
Angus McKerral ◽  
Kristen Pammer
Keyword(s):  
Review Literature ◽  
Narrative Review ◽  
Crash Risk ◽  
Risky Driving ◽  
Vehicle Performance ◽  
Young Driver ◽  
Young Drivers ◽  
Safety Research ◽  
Increased Risk ◽  
Performance Limitations

Sharing the road with trucks is associated with increased risk of serious injury and death for passenger vehicle drivers. However, the onus for minimising risk lies not just with truck drivers; other drivers must understand the unique performance limitations of trucks associated with stopping distances, blind spots, and turning manoeuverability, so they can suitably act and react around trucks. Given the paucity of research aimed at understanding the specific crash risk vulnerability of young drivers around trucks, the authors employ a narrative review methodology that brings together evidence from both truck and young driver road safety research domains, as well as data regarding known crash risks for each driving cohort, to gain a comprehensive understanding of what young drivers are likely to know about heavy vehicle performance limitations, where there may be gaps in their understanding, and how this could potentially increase crash risk. We then review literature regarding the human factors affecting young drivers to understand how perceptual immaturity and engagement in risky driving behaviours are likely to compound risk regarding both the frequency and severity of collision between trucks and young drivers. Finally, we review current targeted educational initiatives and suggest that simply raising awareness of truck limitations is insufficient. We propose that further research is needed to ensure initiatives aimed at increasing young driver awareness of trucks and truck safety are evidence-based, undergo rigorous evaluation, and are delivered in a way that aims to (i) increase young driver risk perception skills, and (ii) reduce risky driving behaviour around trucks.

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

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.

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