• DocumentCode
    1791758
  • Title

    Some examples of big data in railroad engineering

  • Author

    Zarembski, Allan M.

  • Author_Institution
    Dept. of Civil & Environ. Eng., Univ. of Delaware, Newark, DE, USA
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    96
  • Lastpage
    102
  • Abstract
    The railroad industry is an infrastructure intensive industry that relies on significant amounts of information and data to operate and maintain each railroad. Using the US railroad industry as a model, this data collection encompasses the full range of railroad activities from tracking of goods shipments and car locations to managing train crews to inspecting and maintaining the infrastructure. This paper will look at this last area, inspection and maintaining the infrastructure and in particular the 330,000 km (200,000 miles) of railroad track in active use in the US. Using a broad range of inspection vehicle to collect data and a new generation of maintenance management software systems to analyze and interpret this data, railroads represent an industry that is starting to make extensive use of its “big data” to optimize its capital infrastructure and safely manage its operations while keeping costs under control. This paper presents examples of collection, storage and use of “big data” in the railroad engineering environment.
  • Keywords
    Big Data; data acquisition; railways; storage management; Big Data; US railroad industry; capital infrastructure; car locations; data collection; data storage; data use; goods shipments tracking; infrastructure inspection; infrastructure intensive industry; infrastructure maintenance; inspection vehicle; maintenance management software systems; railroad activities; railroad engineering; railroad track; train crews management; Big data; Geometry; Industries; Inspection; Maintenance engineering; Rails; Vehicles; Railroad; maintenance planning; rail inspection; track; track geometry;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004437
  • Filename
    7004437