Crash Prediction Models and Methodological Issues

Abstract

The conducted literature review aimed to provide an overall perspective on the significant findings of past research works related to vehicle crashes and prediction models. The literature review also provided information concerning past road safety research methodology and viable statistical analysis and computing tools. Though the selection of a specific model hinges on the objective of the research and nature of the response, when compared to statistical modeling techniques, Artificial Neural Networks (ANNs), which can model complex nonlinear relationships among dependent and independent parameters, have been witnessed to be very powerful.

Generalized Linear Modeling of Crashes on Urban Road Links

“…Though the selection of a specific model hinges on the research's objective and the nature of the response compared to statistical modeling techniques (Mekonnen and Sipos, 2022) many studies have been carried out over the course of years using statistical modeling techniques to solve problems related to road traffic accidents. Lord et al (2008) applied Conway-Maxwell-Poisson generalized linear model to analyze motor vehicle crashes; Prieto et al (2014) analyzed accident blackspots with discrete generalized pareto distribution; Geedipally et al (2012) employed negative binomial-Lindley generalized linear model to analyze crash data.…”