## 6 Conclusions  
In this study, accurate HRGCs data are used to highlight the importance of data quality on crash model estimation and crash predictions. The conclusions are as follows:  
* Erroneous and missing data in the unaltered FRA HRGCs inventory database led to statistically different crash predictions (expected crashes) compared to corrected and complete (field-validated) HRGCs inventory data. These predictions also affected the crash hazard ranking of crossings based on the two datasets.
* Estimated crash prediction model parameters and their corresponding marginal values appeared to differ when comparing models based on the unaltered FRA HRGCs inventory database and the corrected and complete (field-validated) HRGCs inventory data. However, from a statistical standpoint, they did not exhibit significant differences. Nevertheless, the Zero-inflated Negative Binomial (ZINB) crash prediction model, utilizing the accurate inventory dataset, demonstrated superior performance according to fitness criteria such as the Akaike information criterion (AIC) and BIC (Bayesian information criterion).  
This study accentuates the need for a correct and complete HRGCs inventory database because critical safety-related decisions are based on the modeling results of this database. The limitations of this study include the inability to verify the dynamic characteristics of HRGCs, such as daily train traffic and AADT. These traffic-related variables change more frequently compared to physical characteristics of HRGCs, and therefore, this may have more profound impacts on model estimation and predictions. There is a need for a more comprehensive future study on how to account for their effects on crash predictions as well as on developing methods for the routine update of these dynamic variables in the database. This study was based on visits to 560 HRGCs in the field for corrections to the original FRA HRGCs inventory. Such an effort is resource-intensive, so there is a need for a more efficient way of verifying HRGCs inventory data. Many important variables mentioned in the literature were unavailable in the HRGCs inventory data (e.g., clear sight distance availability, presence of humps, roadway grade information, presence of nearby buildings). Future research should address the nationwide collection of such data and its incorporation in the existing FRA HRGCs database.  
Conceptualization, M.U.F. and A.J.K.; methodology, M.U.F. and A.J.K.; software, M.U.F.; validation, M.U.F. and A.J.K.; formal analysis, M.U.F. and A.J.K.; investigation, M.U.F. and A.J.K.; resources, M.U.F. and A.J.K.; data curation, M.U.F. and A.J.K.; writing--original draft preparation, M.U.F. and A.J.K.; writing--review and editing, M.U.F. and A.J.K.; visualization, M.U.F.; supervision, A.J.K.; project administration, M.U.F. and A.J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to express their gratitude to the Mid-America Transportation Center for providing the resources necessary to conduct this research. Additionally, the authors extend their appreciation to [PERSON] and [PERSON] for their valuable contributions to the data collection process of this study.  
The authors declare no conflict of interest.