01155563

Component

https://doi.org/10.3141/2165-01

http://scholar.google.com...Bhat&publication_year=2010

Research to date on crash injury severity has focused on the driver of the vehicle or the most severely injured occupant. Though useful, these studies have not provided injury profiles of all occupants in crash-involved vehicles. This lack of a comprehensive picture has limited the ability to devise measures that enhance the safety and reduce the severity of the injury sustained by all vehicular occupants. Moreover, such studies ignore the possible presence of correlated, unobserved factors that may simultaneously affect the injury severity levels of multiple occupants. This paper aims to fill the gap by presenting a simultaneous model of injury severity to apply to crashes that involve any number of occupants. A copula-based methodology, which could be used to estimate such complex model systems, was applied to a data set of crashes drawn from the 2007 General Estimates System in the United States. The model estimation results provide strong evidence of the presence of correlated unobserved factors that affect injury severity levels of vehicle occupants. The correlation exhibited heterogeneity across vehicle types, with a greater level of interoccupant dependency in heavy SUVs and pickup trucks. The study also sheds light on how numerous exogenous factors—including occupant, vehicle, and crash characteristics; environmental factors; and roadway attributes—affect the injury severity levels of occupants in different seat positions. The findings confirm that rear-seat passengers are less vulnerable to severe injuries than front-row passengers and point to the need to enhance vehicular design features that promote front-row occupant safety.

01220491

10-1807

English

0000-0002-0715-8121

pp 1-11

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309142977

Print

References (35) ; Tables (4)

Front seat occupants; High occupancy vehicles; Injury severity; Multivariate analysis; Rear seat occupants; Traffic crashes

Copula-based models

Data and Information Technology; Highways; Safety and Human Factors; I84: Personal Injuries

TRIS, TRB, ATRI

Jan 25 2010 10:50AM

• Combined Effect of Traffic and Geometrics on Rear-End Collision Risk: Driving Simulator Study
• Empirical Investigation of Continuous Logit for Departure Time Choice with Bayesian Methods
• Examination of Methods to Estimate Crash Counts by Collision Type
• Gaussian Processes for Short-Term Traffic Volume Forecasting
• Getis–Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data
• Integration of Traffic Simulation into Design Visualization: Case Study
• Investigating Regression to the Mean in Before-and-After Speed Data Analysis
• Leverage of Spiral Graph for Transportation System Data Visualization
• Multiple Applications of Multivariate Adaptive Regression Splines Technique to Predict Rear-End Crashes at Unsignalized Intersections
• Spatial Correlation in Multilevel Crash Frequency Models: Effects of Different Neighboring Structures