Investigating driver injury severity patterns in rollover crashes using support vector machine models |
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| Affiliation: | 1. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA;2. Department of Civil and Environmental Engineering and Heinz College, Carnegie Mellon University, Pittsburgh, PA 15213, USA;3. Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA;1. Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87133, USA;2. Traffic Operations Division, Texas Department of Transportation, Austin, TX 78717, USA;3. Department of Civil & Environmental Engineering, University of Cincinnati, Cincinnati, OH 45221, USA;4. Transportation Research Center, Beijing University of Technology, Beijing 100124, China;1. University of Virginia Center for Applied Biomechanics, VA, USA;2. Department of Mechanical Design Engineering, Korea Polytechnic University, 237, Sangidaehak-ro, Siheung-si, Gyeonggi-do, 15073, South Korea;3. Toyota Motor Engineering & Manufacturing North America, Inc., TTC, MI, USA;1. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street Honolulu, HI 96822, United States;2. Metropia, Inc., 1790 E.River Rd., Suite 140, Tucson, AZ 85718, United States;3. Department of Civil and Environmental Engineering, University of Utah, 110 Central Campus Drive, 2137 MCE, Salt Lake City, UT 84112, United States;4. Department of Civil Engineering, University of New Mexico, 210 University Blvd NE Albuquerque, NM 87106, United States;1. Civil and Environmental Engineering, California Polytechnic State University, United States;2. Wyoming Technology Transfer Center, Department of Civil & Architectural Engineering, University of Wyoming, United States;3. South Carolina Department of Transportation, Columbia, SC, United States |
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| Abstract: | 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. |
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| Keywords: | Driver injury severity Rollover crash Support vector machine model Kernel function Traffic safety |
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