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• W2158228991 abstract "In road safety research, it has long become a tradition to take a parametric approach to modeling road collisions, which has resulted in a variety of parametric approaches such as Negative Binomial, Poisson lognormal, zero-inflated Poisson, and random-effect models. While easy to apply and interpret, a parametric approach has several critical limitations due to the modeling requirement of assuming a specific distribution form for each model variable and a fixed functional relationship between each model parameter and the predictors. Violation of these assumptions could lead to biased and/or erroneous inferences on the effect of these predictors on the dependent variable (e.g. collision frequency). This paper introduces a data driven, nonparametric alternative - Kernel regression aiming at answering the question of whether or not it makes any meaningful differences as compared to the traditional parametric method. The proposed approach has been applied to model winter road collisions using a database containing hourly observations of collisions, road weather and surface conditions, and traffic counts on a set of highways in Ontario, Canada, over six winter seasons. The results from this approach have clearly shown that significantly nonlinear relationships exist between collision frequency and some condition factors, which have not been captured in the previous studies that use the parametric technique. Furthermore, the new approach also captures some moderating effects of several condition variables, which could be easily missed in the parametric" @default.
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• W2158228991 date "2013-06-11" @default.
• W2158228991 modified "2023-09-26" @default.
• W2158228991 title "A Nonparametric Approach to Road Safety Analysis - Does It Make a Difference?" @default.
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• W2158228991 doi "https://doi.org/10.1061/9780784413036.087" @default.
• W2158228991 hasPublicationYear "2013" @default.
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