Counselor dies in traffic crash in Wyoming.

Keyword(s):  
2010 ◽  
Author(s):  
Jason Wyatt ◽  
Michael Alexander
Keyword(s):  

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.


2016 ◽  
Vol 22 (Suppl 2) ◽  
pp. A62.2-A62
Author(s):  
Audrey Luxcey ◽  
Emmanuel Lagarde ◽  
Sylviane Lafont ◽  
Marie Zins ◽  
Benjamin Contrand ◽  
...  

Author(s):  
Jelena Kovacevic ◽  
Ivica Fotez ◽  
Ivan Miskulin ◽  
Davor Lesic ◽  
Maja Miskulin ◽  
...  

This study aimed to investigate factors associated with the symptoms of mental disorders following a road traffic crash (RTC). A prospective cohort of 200 people was followed for 6 months after experiencing an RTC. The cohort was comprised of uninjured survivors and injured victims with all levels of road traffic injury (RTI) severity. Multivariable logistic regression analyses were performed to evaluate the associations between the symptoms of depression, posttraumatic stress disorder and anxiety one and six months after the RTC, along with sociodemographic factors, health status before and after the RTC, factors related to the RTI and factors related to the RTC. The results showed associations of depression, anxiety, and posttraumatic stress disorder symptoms with sociodemographic factors, factors related to the health status before and after the RTC and factors related to the RTC. Factors related to the RTI showed associations only with depression and posttraumatic stress disorder symptoms. Identifying factors associated with mental disorders following an RTC is essential for establishing screening of vulnerable individuals at risk of poor mental health outcomes after an RTC. All RTC survivors, regardless of their RTI status, should be screened for factors associated with mental disorders in order to successfully prevent them.


2013 ◽  
Vol 52 ◽  
pp. 162-170 ◽  
Author(s):  
Hong Son Nghiem ◽  
Luke B. Connelly ◽  
Susan Gargett

2014 ◽  
Vol 186 (15) ◽  
pp. 1169.1-1169
Author(s):  
Jeffrey M. McKillop
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document