Abstract:
This paper investigates utilising machine learning (ML) techniques to predict the five major
parameters of track geometry in railway infrastructure. Track geometry and incurred
vehicle acceleration measurements were collected on a railway line and matched
according to their GPS coordinates. The data were then split into test/train and validation
datasets and processed using various ML methods. The predictive results of each ML
method were compared for each track geometry parameter and the best methods were
highlighted. The quality of the results was mixed with accurate results obtained for cant
and alignment but inaccurate results for gauge, profile and twist. Overall, this research
paper contributes to the field of railway engineering by demonstrating the potential to
utiliseML in the field by predicting track geometry parameters. The findings have practical
implications for improving track maintenance and ensuring passenger safety and comfort
in railway operations. The promising results of this paper warrant more research being
conducted and potential methods for improvement are highlighted.
