01506509

Component

https://doi.org/10.3141/2432-07

http://scholar.google.com...hail&publication_year=2014

The main goal of this research was to develop models for crash severity prediction in a subject vehicle while taking into account the impact of both vehicles involved (Vehicles V1 and V2). Three binary targets were modeled: FatalSIK (to predict overall severity), FatalSIKV1 (to predict the probability of a serious injury, fatality, or both in Vehicle V1), and FatalSIKV2 (to predict the probability of a serious injury, fatality, or both in Vehicle V2). For the period from 2006 to 2010, 874 collisions involving injuries, fatalities, or both were analyzed. However, the crash sample included few severe events. Because imbalanced data introduce a bias toward the majority class (nonsevere crashes), in predictive modeling, they would result in less accurate predictions of the minority class (severe crashes). For the challenge imposed by small sample size and imbalanced data to be overcome, an important methodology was developed on the basis of a resampling strategy by using 10 stratified random samples for model evaluation. The effect of vehicle characteristics such as weight, engine size, wheelbase, and registration year (age of vehicle) were explored. Logistic regression analysis for FatalSIK suggested that the age of the vehicle and the type of collision were significant predictors (p < .0084 and .0346, respectively). Models FatalSIKV1 and FatalSIKV2 showed that the engine size of the opponent vehicle was statistically significant in predicting severity (p < .0762 and p < .03875, respectively). Models for FatalSIKV1 and FatalSIKV2 yielded satisfactory results when evaluated with the 10 stratified random samples: 61.2% [standard deviation (SD) = 2.4] and 61.4% (SD = 3.1), respectively.

01543149

14-2085

English

pp 53–64

2014

Transportation Research Record: Journal of the Transportation Research Board

9780309295208

Print

Figures (3) ; References (39) ; Tables (5)

Crash analysis; Crash severity; Crashes; Crashworthiness; Multiple vehicle crashes; Vehicle characteristics; Vehicle factors in crashes

Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment; I91: Vehicle Design and Safety

TRIS, TRB, ATRI

Jan 27 2014 2:44PM

• Advancing Safety Performance Monitoring at Signalized Intersections Through Use of Connected Vehicle Technology
• Analyzing the Maximum Abbreviated Injury Scale in Vehicle Crashes by Using a Logistic Normal Model
• Assessing the Effect of Weather States on Crash Severity and Type by Use of Full Bayesian Multivariate Safety Models
• Can Microsimulation Be Used to Estimate Intersection Safety? Case Studies Using VISSIM and Paramics
• Daily Collision Prediction with SARIMAX and Generalized Linear Models on the Basis of Temporal and Weather Variables
• Derivation of a New Surrogate Measure of Crash Severity
• Evaluating Spatial-Proximity Structures in Crash Prediction Models at the Level of Traffic Analysis Zones
• Geographic Information System–Based Community-Level Method to Evaluate the Influence of Built Environment on Traffic Crashes
• Identification of Secondary Crashes on a Large-Scale Highway System
• Limiting Driveway Access at Intersections: Sample Application of Value-of-Research Evaluation
• Linking Police and Hospital Road Accident Records: How Consistent Can It Be?
• Patterns of Single-Vehicle Crashes on Two-Lane Rural Highways in Granada Province, Spain: In-Depth Analysis Through Decision Rules
• Sampling Serious Injuries in Traffic Crashes at the State Level
• Use of Structural Equation Modeling to Measure Severity of Single-Vehicle Crashes
• Use of Support Vector Machine Models for Real-Time Prediction of Crash Risk on Urban Expressways
• Validation of the Surrogate Safety Assessment Model for Assessment of Intersection Safety