MPC Research Reports
Report Details

Title:	Safety Support System for Highway Rail Grade Crossings
Authors:	Pan Lu, Denver Tolliver, Amin Karamati, Yihao Ren, Zijian Zheng, and Xiaoyi Zhou
University:	North Dakota State University
Publication Date:	Feb 2023
Report #:	MPC-23-493
Project #:	MPC-550
TRID #:	01875821
Keywords:	crash risk forecasting, crash severity, decision support systems, highway safety, railroad grade crossings, railroad safety, risk analysis

Abstract

As a result of the considerable differences in mass between vehicles and trains, crashes at highway-rail grade crossings (HRGCs) often result in severe injuries and fatalities. Therefore, HRGC safety is considered a crucial transportation safety issue. Transportation decision makers and agencies need an efficient safety decision-making framework that is able to predict crash occurrence and severity likelihoods in the same prediction model, identify and quantify contributors and their marginal effects, quantify geometric and countermeasures' safety improvement effectiveness, and rank the priorities for the crossings in terms of their safety improvement needs. This study proposed 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 the bioscience area but never utilized in the 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 a crossing's 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 crossings that are considered the most in need of improvement. Moreover, significant contributors, including geometric contributors and countermeasures, are identified, and their marginal effectiveness is also summarized. In this study, the hazard ranking model based on crash likelihoods resulted by the proposed CRM method is used to classify grade crossings and crossing locations based on their crash frequency and severity likelihood simultaneously. The risk analysis is conducted by using the risk matrix and spatial risk analysis. Finally, an interactive app that incorporates all the findings of the research is developed for agencies to use as a simulation tool.

How to Cite

Lu, Pan, Denver Tolliver, Amin Karamati, Yihao Ren, Zijian Zheng, and Xiaoyi Zhou. Safety Support System for Highway Rail Grade Crossings, MPC-23-493. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2023.