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
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