Applying the Generalized Waring model for investigating sources of variance in motor vehicle crash analysis
Abbreviated Journal Title
Accid. Anal. Prev.
Generalized Waring model; Negative binomial model; Over dispersion; Randomness; Liability; Proneness; Crash modeling; POISSON-GAMMA MODELS; DISPERSION PARAMETER; REGRESSION-MODELS; INJURY; SEVERITY; ACCIDENT THEORY; FINITE-MIXTURE; LINEAR-MODEL; COUNT DATA; SAFETY; BAYES; Ergonomics; Public, Environmental & Occupational Health; Social; Sciences, Interdisciplinary; Transportation
As one of the major analysis methods, statistical models play an important role in traffic safety analysis. They can be used for a wide variety of purposes, including establishing relationships between variables and understanding the characteristics of a system. The purpose of this paper is to document a new type of model that can help with the latter. This model is based on the Generalized Waring (GW) distribution. The GW model yields more information about the sources of the variance observed in datasets than other traditional models, such as the negative binomial (NB) model. In this regards, the GW model can separate the observed variability into three parts: (1) the randomness, which explains the model's uncertainty; (2) the proneness, which refers to the internal differences between entities or observations; and (3) the liability, which is defined as the variance caused-by other external factors that are difficult to be identified and have not been included as explanatory variables in the model. The study analyses were accomplished using two observed datasets to explore potential sources of variation. The results show that the GW model can provide meaningful information about sources of variance in crash data and also performs better than the NB model. (C) 2014 Elsevier Ltd. All rights reserved.
Accident Analysis and Prevention
"Applying the Generalized Waring model for investigating sources of variance in motor vehicle crash analysis" (2014). Faculty Bibliography 2010s. 5944.