Our news journalists obtained a quote from the research, "However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contribute factors to crashes) established via a link function can be often violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions. In this research, a GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables using the rear end accident data collected from 10 highway routes from 2002 through 2006. The results show for example that truck percentage and grade have a parabolic impact: they increase crash risks initially, but decrease them after the certain thresholds. Such non-monotonic relationships cannot be captured by regular GLMs which further demonstrate the flexibility of GNM-based approaches in the nonlinear relationship among data and providing more reasonable explanations."
According to the news editors, the research concluded: "The superior GNM-based model interpretations help better understand the parabolic impacts of some specific contributing factors for selecting and evaluating rear-end crash safety improvement plans."
For more information on this research see: Generalized nonlinear models for rear-end crash risk analysis. Accident Analysis and Prevention, 2014;62():9-16. Accident Analysis and Prevention can be contacted at:
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