A Comparison between Parametric and Nonparametric Approaches for Road Safety Analysis – A Case Study of Winter Road Safety

摘要

In road safety research, a parametric approach is commonly applied in modeling road collisions, whichhave resulted in many different types of models such as Poisson, Negative Binomial and Poissonlognormal. While easy to apply and interpret, a parametric approach has several critical limitations due tothe modeling requirement of assuming a specific probability distribution form for each model variable(e.g. collision frequency) and a pre-specified functional relationship between each model parameter andthe predictors. These assumptions, if violated, could lead to biased and/or erroneous inferences on theeffect of these predictors on the dependent variable. This paper introduces a data-driven, nonparametricalternative called Kernel regression, which circumvents the need for the aforementioned assumptions.This paper compares the parametric and nonparametric approaches through an empirical study using alarge dataset consisting of hourly observations of collisions, road weather and surface conditions, andtraffic counts from highways in Ontario, Canada, over six winter seasons. It is shown that thenonparametric approach has the advantage of being able to capture the significant nonlinear andinteracting effects of some condition factors. The paper also illustrate the practical implications of thedifferences between the two approaches, including evaluation of the risk levels of road surface conditionsfor the road users and quantification of safety benefits of maintenance operations for transportationauthorities.