Artificial Intelligence-Aided Rail Transit Infrastructure Data Mining

The primary goal of this proposal is to develop a pilot, proof-of-concept study in collaboration with the MTA to explore the use of Artificial Intelligence (AI) for analyzing infrastructure big data to predict track degradation and future condition. The intended outcome of the project is a novel AI model for track infrastructure data mining in rail transit. This tool aims to forecast track infrastructure condition and to address track degradation before failures occur. This will lead to better life cycle performance and save the total life cycle cost while ensuring infrastructure safety and durability. MTA and other agencies could use the approach to prioritize their infrastructure capital planning, inspection and maintenance.

Project

  • Status: Active
  • Funding: $49888
  • Contract Numbers:

    69A3551847102

    CAIT-UTC-REG43

  • 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:

    Metropolitan Transportation Authority

    347 Madison Avenue
    New York, NY  United States  10017-3739

    Center for Advanced Infrastructure and Transportation

    Rutgers University
    100 Brett Road
    Piscataway, NJ  United States  08854-8058
  • Project Managers:

    Kraft, David

    Szary, Patrick

  • Performing Organizations:

    Center for Advanced Infrastructure and Transportation

    Rutgers University
    100 Brett Road
    Piscataway, NJ  United States  08854-8058
  • Principal Investigators:

    Liu, Xiang

  • Start Date: 20210201
  • Expected Completion Date: 20220131
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01771379
  • Record Type: Research project
  • Source Agency: Center for Advanced Infrastructure and Transportation
  • Contract Numbers: 69A3551847102, CAIT-UTC-REG43
  • Files: UTC, RIP
  • Created Date: May 6 2021 11:15AM