title: Accident prediction model for public highway-rail grade crossings.
abstract: Considerable research has focused on roadway accident frequency analysis, but relatively little research has examined safety evaluation at highway-rail grade crossings. Highway-rail grade crossings are critical spatial locations of utmost importance for transportation safety because traffic crashes at highway-rail grade crossings are often catastrophic with serious consequences. The Poisson regression model has been employed to analyze vehicle accident frequency as a good starting point for many years. The most commonly applied variations of Poisson including negative binomial, and zero-inflated Poisson. These models are used to deal with common crash data issues such as over-dispersion (sample variance is larger than the sample mean) and preponderance of zeros (low sample mean and small sample size). On rare occasions traffic crash data have been shown to be under-dispersed (sample variance is smaller than the sample mean) and traditional distributions such as Poisson or negative binomial cannot handle under-dispersion well. The objective of this study is to investigate and compare various alternate highway-rail grade crossing accident frequency models that can handle the under-dispersion issue. The contributions of the paper are two-fold: (1) application of probability models to deal with under-dispersion issues and (2) obtain insights regarding to vehicle crashes at public highway-rail grade crossings.
authors.affiliation: Assistant Professor of Transportation, Upper Great Plains Transportation Institute, Dept. 2880, North Dakota State University, Fargo, ND 58108-6050, USA. Electronic address: pan.lu@ndsu.edu.
authors.forename: Pan
authors.initials: P
authors.lastname: Lu
authors.affiliation: Director and Professor of Transportation, Upper Great Plains Transportation Institute, Dept. 2880, North Dakota State University, Fargo, ND 58108-6050, USA. Electronic address: denver.tolliver@ndsu.edu.
authors.forename: Denver
authors.initials: D
authors.lastname: Tolliver
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D004779:Environment Design; D006801:Humans; D008962:Models, Theoretical; D016012:Poisson Distribution; D011336:Probability; D011890:Railroads; D012044:Regression Analysis; D012449:Safety
keywords: Accident prediction; Bernoulli; Binomial; Gamma; Poisson; Quasi-Poisson; Railroad grade crossing; Under-dispersion