Crash Risk and Risky Driving Behavior Among Adolescents During Learner and Independent Driving Periods

2018 ◽  
Vol 63 (5) ◽  
pp. 568-574 ◽  
Author(s):  
Pnina Gershon ◽  
Johnathon P. Ehsani ◽  
Chunming Zhu ◽  
Kellienne R. Sita ◽  
Sheila Klauer ◽  
...  
2021 ◽  
Vol 160 ◽  
pp. 106328
Author(s):  
Miao Guo ◽  
Xiaohua Zhao ◽  
Ying Yao ◽  
Pengwei Yan ◽  
Yuelong Su ◽  
...  

2009 ◽  
Vol 99 (9) ◽  
pp. 1638-1644 ◽  
Author(s):  
Rebecca Ivers ◽  
Teresa Senserrick ◽  
Soufiane Boufous ◽  
Mark Stevenson ◽  
Huei-Yang Chen ◽  
...  

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 465-465
Author(s):  
Jennifer Zakrajsek ◽  
Lisa Molnar ◽  
David Eby ◽  
David LeBlanc ◽  
Lidia Kostyniuk ◽  
...  

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.


Author(s):  
Melissa R. Freire ◽  
Cassandra Gauld ◽  
Angus McKerral ◽  
Kristen Pammer

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.


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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jinshuan Peng ◽  
Yiming Shao

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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