Journal
ACCIDENT ANALYSIS AND PREVENTION
Volume 90, Issue -, Pages 128-139Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.aap.2016.02.011
Keywords
Driver injury severity; Rollover crash; Support vector machine model; Kernel function; Traffic safety
Categories
Funding
- National Natural Science Foundation of China [51138003, 51329801]
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Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns. (C) 2016 Elsevier Ltd. All rights reserved.
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