Selection Bias And Heterogeneity In Severity Models

The original contribution of this thesis is two-fold: a) it addresses a gap in the published literature on the accommodation of potential information from segments that are observed to have not crashes, which as a result can affect the estimated severity distributions; and b) by accounting for such selection effects, the thesis also makes original contributions in the area of the nature of the impact of selection effects on parameters associated with severity models. In particular, the severity models are formulated as two-stage models where information on both geometrics and collision type is incorporated to provide for comprehensive analysis of segment level severity distributions.