Vehicle front-end geometry and in-depth pedestrian injury outcomes
CONCLUSIONS: The combination of vehicle height and a steep bumper lead angle may explain the elevated pedestrian crash severity typically observed among large vehicles.PMID:38578254 | DOI:10.1080/15389588.2024.2332513 (Source: Traffic Injury Prevention)
Source: Traffic Injury Prevention - April 5, 2024 Category: Accident Prevention Authors: Samuel S Monfort Wen Hu Becky C Mueller Source Type: research
Driving after substance use in Rhode Island adolescents: A cross-sectional analysis of surveillance data
CONCLUSIONS: Driving under the influence is a concern among adolescent substance users. Current substance use and perceived parental risk perception as negative are common risks and protective factors, respectively. Findings suggest substance-specific, heterogeneous interventions are needed. For example, interventions focusing on peer perceptions are most relevant for DAP, while shifting personal perceptions of harm are most relevant for DAC.PMID:38578273 | DOI:10.1080/15389588.2024.2335317 (Source: Traffic Injury Prevention)
Source: Traffic Injury Prevention - April 5, 2024 Category: Accident Prevention Authors: Jonathan K Noel Samantha R Rosenthal Jadyn N Torres Kelsey A Gately Samantha K Borden Source Type: research
Understanding nonuse of mandatory e-scooter helmets
CONCLUSIONS: Most nonuse of helmets in a mandatory context seems to be situational, rather than consistent. Many of the factors associated with nonuse of helmets for e-scooters are similar to those reported for bicycles. Nonuse of helmets appears to be one of a number of risky behaviors performed by riders, rather than being primarily an outcome that is specific to factors associated with helmets (e.g., concerns about hygiene, discomfort or availability).PMID:38578267 | DOI:10.1080/15389588.2024.2335677 (Source: Traffic Injury Prevention)
Source: Traffic Injury Prevention - April 5, 2024 Category: Accident Prevention Authors: Narelle Haworth Nathalie Ssi Yan Kai Amy Schramm Source Type: research
Validated numerical unrestrained occupant-seat crash scenarios for high-speed trains integrating experimental, computational, and inverse methods
CONCLUSIONS: This study demonstrates the value of experimental data for occupant-seat model interactions in train collisions and provides practical help for train interior safety design and formulation of standards for rolling stock interior passive safety.PMID:38578292 | DOI:10.1080/15389588.2024.2335558 (Source: Traffic Injury Prevention)
Source: Traffic Injury Prevention - April 5, 2024 Category: Accident Prevention Authors: Gongxun Deng Tuo Xu Yong Peng Dayan Sun Zhengsheng Hu Min Deng Kui Wang Ciaran Simms Source Type: research
Identification of the best machine learning model for the prediction of driver injury severity
Int J Inj Contr Saf Promot. 2024 Apr 4:1-16. doi: 10.1080/17457300.2024.2335478. Online ahead of print.ABSTRACTPredicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. These models were implemented using 0.70, 0.80, and 0.90 train ratios and 5-, 10- and 15-fold cross-validation. Ten years of accident data (from 2011 to 2020) was obtained from police department of Shillong, India. A total of 693 accidents were documented, with 68% being n...
Source: International Journal of Injury Control and Safety Promotion - April 4, 2024 Category: Accident Prevention Authors: Neero Gumsar Sorum Dibyendu Pal Source Type: research
Implementation of a realistic artificial data generator for crash data generation
Accid Anal Prev. 2024 Apr 2;200:107566. doi: 10.1016/j.aap.2024.107566. Online ahead of print.ABSTRACTIn this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between th...
Source: Accident; Analysis and Prevention. - April 4, 2024 Category: Accident Prevention Authors: Lauren Hoover Md Istiak Jahan Tanmoy Bhowmik Sudipta Dey Tirtha Karthik C Konduri John Ivan Kai Wang Shanshan Zhao Joshua Auld Naveen Eluru Source Type: research
Risk factors associated with driving after marijuana use among West Virginia college students during the COVID-19 pandemic
CONCLUSIONS: As DAMU was prevalent, future interventions that raise awareness of the danger and potential consequences of DAMU may be needed to reduce this risky behavior on college campuses.PMID:38572915 | DOI:10.1080/15389588.2024.2333906 (Source: Traffic Injury Prevention)
Source: Traffic Injury Prevention - April 4, 2024 Category: Accident Prevention Authors: Yuni Tang Christiaan G Abildso Christa L Lilly Erin L Winstanley Toni M Rudisill Source Type: research
Examining the impact of legalization on the prevalence of driving after using cannabis: A comparison of rural and non-rural parts of Canada
CONCLUSIONS: The finding of significantly higher risk of driving after use of cannabis soon after legalization in rural areas suggests a need for more attention to address immediate concerns for public safety. The increased potential for traffic injuries and deaths in other jurisdictions contemplating legalization supports the call for more and better targeted prevention efforts in rural communities that have far too often been overlooked and under-served.PMID:38572920 | DOI:10.1080/15389588.2024.2333908 (Source: Traffic Injury Prevention)
Source: Traffic Injury Prevention - April 4, 2024 Category: Accident Prevention Authors: Meghan Wrathall Nick Cristiano David Walters Greggory Cullen Andrew Hathaway Source Type: research
Identification of the best machine learning model for the prediction of driver injury severity
Int J Inj Contr Saf Promot. 2024 Apr 4:1-16. doi: 10.1080/17457300.2024.2335478. Online ahead of print.ABSTRACTPredicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. These models were implemented using 0.70, 0.80, and 0.90 train ratios and 5-, 10- and 15-fold cross-validation. Ten years of accident data (from 2011 to 2020) was obtained from police department of Shillong, India. A total of 693 accidents were documented, with 68% being n...
Source: International Journal of Injury Control and Safety Promotion - April 4, 2024 Category: Accident Prevention Authors: Neero Gumsar Sorum Dibyendu Pal Source Type: research
Implementation of a realistic artificial data generator for crash data generation
Accid Anal Prev. 2024 Apr 2;200:107566. doi: 10.1016/j.aap.2024.107566. Online ahead of print.ABSTRACTIn this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between th...
Source: Accident; Analysis and Prevention. - April 4, 2024 Category: Accident Prevention Authors: Lauren Hoover Md Istiak Jahan Tanmoy Bhowmik Sudipta Dey Tirtha Karthik C Konduri John Ivan Kai Wang Shanshan Zhao Joshua Auld Naveen Eluru Source Type: research
Identification of the best machine learning model for the prediction of driver injury severity
Int J Inj Contr Saf Promot. 2024 Apr 4:1-16. doi: 10.1080/17457300.2024.2335478. Online ahead of print.ABSTRACTPredicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. These models were implemented using 0.70, 0.80, and 0.90 train ratios and 5-, 10- and 15-fold cross-validation. Ten years of accident data (from 2011 to 2020) was obtained from police department of Shillong, India. A total of 693 accidents were documented, with 68% being n...
Source: International Journal of Injury Control and Safety Promotion - April 4, 2024 Category: Accident Prevention Authors: Neero Gumsar Sorum Dibyendu Pal Source Type: research
