Intelligent Transportation Systems Approach to Railroad Infrastructure Performance Evaluation: Track Surface Abnormality Identification with Smartphone-Based App

Federal track safety regulations require railroads to inspect all tracks in operation as often as twice weekly. Railroad companies deploy expensive and relatively slow methods using human inspectors and expensive automated inspection vehicles to inspect and monitor their rail tracks. The current practices are not only expensive and decrease rail productivity by taking away track time to perform inspection, but also increase the safety risk for railway inspection workers. Sensors, such as inertial sensors, accelerometers, gyroscopic sensors, and global positioning system (GPS), are carried on a railway vehicle to continually monitor and inspect rail assets to meet the growing safety improvement needs for reliable and low-cost rail operations. Smartphones use such sensor networks, including wireless communication microchips. In this research, smartphone-based signaling data collection applications, data fusion algorithms, and data processing algorithms to detect a wide variety of possible track surface abnormalities are developed and validated. The research methods will not rely on adapting sensor configurations, and will require only a data upload capability. The new sensors will compress and upload their geo-tagged inertial data periodically to a centralized processor. Remote algorithms will combine and process the data from multiple train traversals to identify abnormal track surface symptoms, and localize their positions. Track surface abnormality identification will enable asset managers to allocate the appropriate specialists to scrutinize the abnormality location.

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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    North Dakota State University, Fargo

    Transportation, Logistics and Finance Department
    Fargo, ND  United States 

    Mountain-Plains Consortium

    North Dakota State University
    Fargo, ND  United States  58108

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Lu, Pan
    • Bridgelall, Raj
    • Tolliver, Denver
    • Chia, Leonard
    • Bhardwaj, Bhavana
  • Publication Date: 2019-7


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References; Tables;
  • Pagination: 41p

Subject/Index Terms

Filing Info

  • Accession Number: 01715250
  • Record Type: Publication
  • Report/Paper Numbers: MPC 19-384
  • Created Date: Aug 29 2019 5:28PM