Thakali, L., Fu, L., & Chen, T. (2014). A Comparison between Parametric and Nonparametric Approaches for Road Safety Analysis – A Case Study of Winter Road Safety. Proceedings of the 93rd Annual TRB Conference, Washington D.C., Jan 12-16.
In road safety research, a parametric approach is commonly applied in modeling road collisions, which have resulted in many different types of models such as Poisson, Negative Binomial and Poisson lognormal. While easy to apply and interpret, a parametric approach has several critical limitations due to the 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 and the predictors. These assumptions, if violated, could lead to biased and/or erroneous inferences on the effect of these predictors on the dependent variable. This paper introduces a data-driven, nonparametric alternative called Kernel regression, which circumvents the need for the aforementioned assumptions. This paper compares the parametric and nonparametric approaches through an empirical study using a large dataset consisting of hourly observations of collisions, road weather and surface conditions, and traffic counts from highways in Ontario, Canada, over six winter seasons. It is shown that the nonparametric approach has the advantage of being able to capture the significant nonlinear and interacting effects of some condition factors. The paper also illustrate the practical implications of the differences between the two approaches, including evaluation of the risk levels of road surface conditions for the road users and quantification of safety benefits of maintenance operations for transportation authorities