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U.S. Department of Transportation U.S. Department of Transportation Icon United States Department of Transportation United States Department of Transportation

Machine Learning-based Analysis for Railway Track Maintenance

Document Series:
Technical Reports
Author:
  • Federal Railroad Administration
Report Number
DOT/FRA/ORD-26/28
Office
RDI-20
Subject:
Track
Keywords:
Railroad, machine learning, large data, drainage condition, GPR, LiDAR
Report Number
DOT/FRA/ORD-26/28
Office
RDI-20

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.
 


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Last updated: Wednesday, September 30, 2026