title: Investigating driver injury severity patterns in rollover crashes using support vector machine models.
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.
authors.affiliation: Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Cong
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: guohui@unm.edu.
authors.forename: Guohui
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Department of Civil and Environmental Engineering and Heinz College, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Qian
authors.affiliation: Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Rafiqul A
authors.initials: RA
authors.lastname: Tarefder
authors.affiliation: Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA.
authors.forename: Zong
authors.initials: Z
authors.lastname: Tian
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D000368:Aged; D001334:Automobile Driving / Q000523:psychology*; D004779:Environment Design; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009516:New Mexico; D012622:Seat Belts / Q000706:statistics & numerical data; D060388:Support Vector Machine; D015599:Trauma Severity Indices; D055815:Young Adult
keywords: Driver injury severity; Kernel function; Rollover crash; Support vector machine model; Traffic safety