Machine Learning-based Analysis for Railway Track Maintenance

Details

• Creators:
  Goodarzi, Saeed ; Ho, Carlton L. ; Kashani, Kevin ; Hyslip, James P.
• Corporate Creators:
  University of Massachusetts at Amherst ; Loram Technologies, Inc
• Corporate Contributors:
  United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology
• Subject/TRT Terms:
  • Railroads
  • Machine learning
  • Data
  • Drainage
  • Laser radar
  • Ground penetrating radar
• Publication/ Report Number:
  DOT/FRA/ORD-26/28
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  July 2022 - July 2024
• Contracting Officer:
  Thompson, Hugh
• Corporate Publisher:
  United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology
• Description:
  Understanding and mitigating track degradation rates (TDRs) is crucial for ensuring the safety and efficiency of railway systems, as well as for controlling maintenance costs and preserving ride quality. The Federal Railroad Administration (FRA) sponsored the University of Massachusetts Amherst and Loram Technologies, Inc. to extensively analyze TDR over 280 miles of passenger revenue track using data from 2011 to 2021. Using a large dataset was pivotal, as it enhanced the robustness of the analysis, allowing for more accurate and reliable conclusions. The researchers used ground penetrating radar (GPR) and light detection and ranging (LiDAR) along with geometry data to examine subsurface and drainage conditions of the tracks. The project employed two distinct methods to estimate TDR, each offering unique insights and applicability based on available historical data and desired precision. These complementary approaches provide the industry with versatile tools for estimating TDR and improving maintenance scheduling, emphasizing their reliability and adaptability.
• Format:
  PDF
• Funding:
  FR22RPD31000000010
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93749/dot_93749_DS1.pdf
• File Type:
  [PDF - 6.59 MB]
• Collection(s):
  Federal Railroad Administration
• Main Document Checksum:
  urn:sha-512:908deeb734727d85100d1deb4360ff44adacb2a0d9c1106dc8edc393a4f594fcea32890a4b8cef57f39bcc7da0a9495da8af299d1967433854b33b5de9b69cf9