As a result of the considerable differences in mass between vehicles and trains, accidences at highway-rail grade crossings (HRGCs) may result in severe injuries and fatalities. Therefore, HRGCs safety is considered one of the crucial transportation safety issues. Transportation decision makers and agencies need an efficient safety decision-making framework which is bale to consider crash occurrence and severity likelihood simultaneously. This study proposed a novel methodology and a statistical approach for HRGC crash analysis. The proposed method is competing risk model and the approach is Cox proportional hazard regression. This predictive method was well established in bioscience area but never been utilized in transportation area. Competing Risk Model (CRM) is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest, in transportation safety analysis the competing multiple outcomes are accident severity levels while the event of interest is accident occurrence. Transportation decision makers need a prioritization system to categorize crossings’ risk level based on their expected crash frequency and crash severity simultaneously. Therefore, with a hazard-ranking approach which considers crossings’ crash severity and frequency output, transportation decision makers are able to ensure that federal and state funds for grade crossing improvement projects are spent at the crossings that are considered the most in need of improvement. In this study two hazard-ranking models are proposed, the first one is based on the crash likelihood resulted by the proposed CRM output, and the second one is a hybrid accident prediction model/hazard index based on crash severity likelihoods estimated by the same CRM. Finally, to integrate the results of both hazard-ranking approaches, and classify grade crossings and crossings’ location based on their crash frequency and severity likelihood simultaneously, the risk analysis is conducted by using the risk matrix and spatial risk analysis.