A correlated random parameters with heterogeneity in means approach of deer-vehicle collisions and resulting injury-severities

2021 ◽  
Vol 30 ◽  
pp. 100160 ◽  
Author(s):  
Sheikh Shahriar Ahmed ◽  
Jessica Cohen ◽  
Panagiotis Ch. Anastasopoulos
Keyword(s):  
Random Parameters ◽  
Vehicle Collisions ◽  
Injury Severities

scholarly journals Investigating The Temporal Trends And Risks Imposed By Different Driver Groups On Other Drivers

2021 ◽  
Author(s):  
Thomas Thanuvelil Philip
Keyword(s):  
Logistic Regression ◽  
Temporal Trends ◽  
Decision Makers ◽  
The Other ◽  
Visible Injury ◽  
Vehicle Collisions ◽  
Logistic Regression Models ◽  
Accident Data ◽  
Precautionary Measures ◽  
Injury Severities

Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.

scholarly journals Investigating The Temporal Trends And Risks Imposed By Different Driver Groups On Other Drivers

2021 ◽  
Author(s):  
Thomas Thanuvelil Philip
Keyword(s):  
Logistic Regression ◽  
Temporal Trends ◽  
Decision Makers ◽  
The Other ◽  
Visible Injury ◽  
Vehicle Collisions ◽  
Logistic Regression Models ◽  
Accident Data ◽  
Precautionary Measures ◽  
Injury Severities

Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.

Occupant injury severities in hybrid-vehicle involved crashes: A random parameters approach with heterogeneity in means and variances

2017 ◽  
Vol 15 ◽  
pp. 41-55 ◽  
Author(s):  
Puttipan Seraneeprakarn ◽  
Shuaiqi Huang ◽  
Venkataraman Shankar ◽  
Fred Mannering ◽  
Narayan Venkataraman ◽  
...  
Keyword(s):  
Hybrid Vehicle ◽  
Random Parameters ◽  
Occupant Injury ◽  
Injury Severities
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