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.

    Project

    • Funding: $50000
    • 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: 20220830
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01787403
    • Record Type: Research project
    • Source Agency: University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
    • Files: UTC, RIP
    • Created Date: Nov 6 2021 2:23AM