Browse Publications Technical Papers 2019-01-1219
2019-04-02

Estimation of the Relative Roles of Belt-wearing Rate, Crash Speed Change, and Several Occupant Variables in Frontal Impacts for Two Levels of Injury 2019-01-1219

Driver injury probabilities in real-world frontal crashes were statistically modeled to estimate the relative roles of five variables of topical interest. One of those variables pertained to behavior (belt-wearing rate, or BWR), one pertained to crash circumstances (speed change, or ΔV), and three pertained to occupant demographics (Gender, Age, and Body Mass Index). The attendant analysis was composed of two parts: (1) baseline statistical modeling to help recover the past, and (2) sensitivity analyses to help consider the future. In Part 1, risk functions were generated from statistical analysis of real-world data, from the National Automotive Sampling System (NASS). The study started with data from NASS (1995-2014 years), subject to specific selection criteria: 1998-2014 model-year light passenger cars/trucks, in 11-1 o’clock fullengagement frontal crashes. Those criteria yielded N=1,269,178 crash-involved drivers. Those data were parsed for four subpopulations: two levels of belt use (properly-belted vs. unbelted) and two levels of driver injury (moderate-to-fatal, MAIS2+ vs. serious-to-fatal, MAIS3+). For each subpopulation, a baseline statistical model was generated via logistic regression involving the studied variables. Each risk function was assessed for associativity (Goodman-Kruskal Gamma). The four resulting risk functions demonstrated fair fidelity, with the Gammas ranging from 0.49 to 0.71. However, the risk functions demonstrated excellent fidelity for estimating aggregate injury rates of the subpopulations (function-estimated vs. directly-estimated). They were accordingly applied in Part 2. In Part 2, sensitivity studies were conducted by (a) perturbing the studied variables in the NASS dataset to generate thousands of hypothetical NASS files, (b) applying the risk functions to estimate attendant net injury rates, and (c) relating the net injury rates to the variations. Specifically, net injury rates and mean statistics were generated for 15,552 hypothetical NASS datasets involving both belted and unbelted drivers. Those data were then normalized by the means of the baseline NASS file. Finally, power functions were developed to relate the resulting dimensionless net injury-rate data to the five dimensionless predictor variables. Those functions demonstrated excellent fidelity (R2≥0.95), and their exponents helped quantity the relative role of the five studied variables. Belt-wearing rate and speed change were determined to be the most influential, followed by age, body mass index, and gender. These findings might help guide engineers and regulators.

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