Track geometry models using “small data” algorithm

The quality of track geometry is directly linked to vehicle safety, reliability and ride quality. The performance of track is therefore considerably hindered when track geometry indicators deviate from the specified and approved limits due to loads and continuous usage. Information obtained from the analysis of track geometry data can inform the prompt application of preventive and corrective maintenance measures like tamping, to increase the lifespan of the track and provide higher train speeds, optimizing track performance. Recently, there has been the application of Bayesian statistical methods in track degradation models. However, most models rely heavily on likelihood functions which are intractable. The aim of this paper is to apply Approximate Bayesian Computation (ABC), also known as the likelihood-free method, in predicting Track Quality Indices (TQIs) which are essential for track degradation modeling. ABC is rooted in methods like the rejection algorithm and Markov Chain Monte Carlo (MCMC). In ABC, summary statistics are computed from the observed data followed by the simulation of summary statistics for different parameter values

Language

• English

Project

• Status: Active
• Funding: $60000
• Contract Numbers:

  69A3551747132

• 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:

  University of Delaware, Newark

  Department of Civil Engineering
  301 DuPont Hall
  Newark, DE  United States  19716
• Project Managers:

  Attoh-Okine, N

• Performing Organizations:

  University of Delaware, Newark

  Department of Civil Engineering
  301 DuPont Hall
  Newark, DE  United States  19716
• Principal Investigators:

  Attoh-Okine, N

• Start Date: 20190601
• Expected Completion Date: 20210930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Algorithms; Bayes' theorem; Deterioration; Geometry; Maintenance of way; Railroad tracks; Simulation
• Subject Areas: Maintenance and Preservation; Planning and Forecasting; Railroads;

Filing Info

• Accession Number: 01762029
• Record Type: Research project
• Source Agency: University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
• Contract Numbers: 69A3551747132
• Files: UTC, RIP
• Created Date: Jan 7 2021 11:21PM