The Federal Railroad Administration (FRA)'s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an interactive and user friendly tool used to make funding decisions. WBAPS is almost three decades old and involves a three-step approach making it difficult to interpret the contribution of the variables included in the model. It also does not account for regional / local developments and technological advancements pertaining to signals and signs implemented at rail-highway grade crossings. Further, characteristics of rail-highway grade crossing vary by track class which is not implicitly considered by WBAPS. This research, therefore, examines and develops a method and models to estimate crashes at rail-highway grade crossing by track class using regional / local level data. The methodology and models developed for each track class as well as considering all track classes together are based on data for the state of North Carolina. Linear as well as count models based on Poisson and Negative Binomial (NB) distributions were tested for applicability. Negative binomial models were found to be the best fit for the data used in this research. Models for each track class have better goodness of fit statistics compared to model considering data for all track classes together. This is primarily because traffic, design, and operational characteristics at rail-highway grade crossings are different for each track class. The findings from statistical models in this research are well supported by model validation.
