Grade Crossing Accident Risk Modeling

Utilizing the national grade crossing inventory database and other readily available demographic data, a Bayesian Network will be developed to predict optimal crossing protection and accident/collision risk. Data will be assembled and distilled and an exploratory data analysis performed to identify critical variables in grade crossing accident risk. A literature search will be performed and an exposure metric employed based on train and highway traffic density. This metric along with other variables will be used to develop a Bayesian Network that defines the protection level required for an individual crossing based on historic performance of similar crossings, and predicts the probability of train/road vehicle collision.

Language

• English

Project

• Funding: $50000
• Contract Numbers:

  69A3551747132

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Delaware, Newark

  College of Engineering
  Newark, DE  United States  19711
• Project Managers:

  Zarembski, Allan

• Start Date: 20190901
• Expected Completion Date: 20230930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Bayes' theorem; Crash risk forecasting; Data analysis; Demographics; Highway traffic; Mathematical prediction; Metrics (Quantitative assessment); Railroad crashes; Railroad grade crossings; Railroad traffic; Traffic density
• Subject Areas: Highways; Planning and Forecasting; Railroads; Safety and Human Factors;

Filing Info

• Accession Number: 01787403
• Record Type: Research project
• Source Agency: University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
• Contract Numbers: 69A3551747132
• Files: UTC, RIP
• Created Date: Nov 6 2021 2:32AM