Track geometry models using “small data” algorithm

Start: 6/2019, End: 4/2019

Principal Investigator/Researcher

Professor Ni Attoh-Okine and Grace Ashley

Project Description

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.

Implementation of Research Outcomes

The approach will provide a framework for working with small data set to generate an efficient geometry models

Impacts/Benefits of Implementation

The approach is being developed.