## 1 Introduction  
Highway-rail grade crossings (HRGCs) hold significance within the transportation network landscape due to their role in the intersection of rail and highway networks. Traffic crashes at HRGCs are often more severe compared to crashes at other locations, and they present the risk of multimodal traffic disruption (i.e., interruption of rail, motor vehicle, bicyclist, and pedestrian traffic). The involvement of hazardous materials may further aggravate crash consequences at HRGCs. The Federal Railroad Administration (FRA) reported 2195 crashes at HRGCs in 2022 across the US, including 274 fatalities and 810 injuries. Fatal crashes accounted for 12.48% of the total reported crashes [1]. Identification of the factors contributing to HRGCs crashes and their frequency are important aspects to improve safety at these locations. Different types of statistical and machine learning models are available for understanding crash phenomenon and to predict future crashes at HRGCs. Transportation agencies often use crash predictions from such models for the identification of problematic HRGCs, further investigation, and allocation of safety improvement resources. These models invariably utilize reported crashes and HRGCs inventory characteristics, such as roadway and track features, signage, train, and motor vehicle traffic volume. The main source of HRGCs inventory data in the US is the FRA's Office of Railroad Safety highway-rail crossing database files [1]. The accuracy and completeness of the HRGCs inventory data are important as they can affect the estimated model parameters and consequently crash predictions from the estimated model [2]. The purpose of this research was to assess the effects of errors and missing information in the FRA's HRGCs inventory data on crash frequency model estimation and HRGCs crash predictions. The research was based on the original (unaltered) FRA HRGCs inventory dataset for 560 HRGCs located in Nebraska, and a field/site verified the HRGCs inventory database for those 560 HRGCs obtained by comparing the unaltered FRA HRGCs inventory data to on-site conditions and correcting any erroneous and/or missing data. This research illustrates the importance of having accurate and complete HRGCs data by identifying differences in crash predictions using the two datasets in the newly published FRA's 2020 Accident Prediction (AP) model [3]. The two inventory datasets were utilized to estimate two comparative Zero-inflated Negative Binomial (ZINB) models, and an examination of model performance was conducted, considering fitness criteria such as Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The following two hypothesis were tested for this research: (1) there is no difference in the expected value of crashes (crash predictions) estimated by utilizing FRA and field-validated data (\(\alpha=5\%\)), and (2) there is no statistical difference in the estimated parameters' coefficients and performance of new crash frequency prediction models based on the two datasets (FRA vs field-validated) (\(\alpha=5\%\)). Figure 1 shows the approach used for this research and provides a framework to explain data collection, the methodology employed, and the analysis conducted. It is important to mention that this paper constitutes a segment of a doctoral dissertation [2], with its primary focus centered on the analysis of the impacts of inaccuracies and missing values within the HRGCs inventory data on the crash frequency and severity prediction models; the research utilized the Federal Railroad Administration (FRA) and the field-validated HRGCs inventory datasets. The remainder of this paper is organized as follows. The next section presents a review of the literature on crash frequency prediction models and methods to deal with over-dispersion issues regarding HRGCs crashes. A section describing the inventory data verification follows.