Principal components analysis and track quality index: A machine learning approach

Track geometry data is often combined into a single parameter index referred to as a Track Quality Index or TQI. TQIs exhibit classical big data attributes: value, volume, velocity, veracity and variety and are used to obtain average-based assessment of track segments and schedule track maintenance. Using track geometry data from a sample mile track, this activity examines how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led to a principal component analysis approach, with a two-phase approach used to validate the use of principal components. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance. .


    • English


    • Status: Completed
    • Funding: $50000
    • Contract Numbers:


    • 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

    • Performing Organizations:

      University of Delaware, Newark

      College of Engineering
      Newark, DE  United States  19711
    • Principal Investigators:

      Attoh-Okine, N

    • Start Date: 20170601
    • Expected Completion Date: 20210531
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01703372
    • 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: Apr 29 2019 5:21PM