Machine Learning and Railway Track Deterioration Part 2 Multiway Analytics Applied to Railway Track Geometry and Ballast Conditions
Railroad systems generate large amounts of data, and results from the analysis of the data could serve as the basis for maintenance to improve safety and system performance. Tensor (multi-dimensional) factorization and decomposition have become important data analysis tools. Tensor factorizations have several advantages over two-way matrix factorizations, such as the uniqueness of the optimal solution and component identification, even when most of the data is missing. This project aims to introduce the basic concepts of tensor decomposition and demonstrate some of the benefits of multiway analysis in railway track geometry and subsurface modeling. After explaining the fundamentals of tensors, a multi-dimensional data set will be used. Using the data, exploratory data analysis and tensor decomposition will be performed to provide analysis and interpretations of various aspects of the data.
Language
- English
Project
- Status: Active
- Funding: $50000
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
2400 6th Street, NW
Washington, DC United States 20059 -
Performing Organizations:
University of Maryland, College Park
Department of Civil and Environmental Engineering
College Park, MD United States 20742 -
Principal Investigators:
Attoh-Okine, Nii
- Start Date: 20231001
- Expected Completion Date: 0
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Ballast (Railroads); Data analysis; Machine learning; Maintenance of way; Railroad tracks; Tensor analysis
- Subject Areas: Data and Information Technology; Maintenance and Preservation; Railroads;
Filing Info
- Accession Number: 01931532
- Record Type: Research project
- Source Agency: Research and Education in Promoting Safety (REPS) University Transportation Center
- Files: UTC, RIP
- Created Date: Sep 20 2024 9:00PM