Evaluating temporal variability of exogenous variable impacts over 25 years: An application of scaled generalized ordered logit model for driver injury severity

Abstract

The current study undertakes a unique research effort to quantify the impact of various exogenous factors on crash severity over time. Specifically, we examine if over time, the impact of exogenous variables has changed and if so what is the magnitude of the change. The research contributes to driver injury severity analysis both methodologically and empirically by proposing a framework that addresses the challenges associated with pooled (or pseudo-panel) data. For our analysis, we draw data from the General Estimates System (GES) over a span of twenty-five years. The data is compiled for driver injury severity in single or two vehicle crashes from 1989 through 2014 in 5-year increments (1989, 1994, 1999, 2004, 2009 and 2014). The alternative econometric frameworks considered for the analysis include ordered logit, generalized ordered logit, scaled generalized ordered logit and mixed generalized ordered logit models. A host of comparison metrics are computed to evaluate the performance of these alternative models in examining the pooled data. The model development exercise is conducted with a host of exogenous variables including driver characteristics, vehicle characteristics, roadway attributes, environmental factors, crash characteristics and temporal attributes. The model estimation results are further augmented by performing a detailed policy scenario analysis, probability profile representations and elasticity effects for different driving and situational conditions across different years.