Track geometry big data analysis: A machine learning approach

Track geometry has a considerable effect on rail travel comfort and safety and deteriorates with age and tonnage. In order to maintain the track geometry quality, maintenance activities such as tamping, stone blowing and ballast undercutting are usually employed. However, these activities are ineffective if the underlying cause of track deformation such as subgrade failure is not addressed. Geosyn-thetics such as geocells and geogrids can be placed in the subballast which strengthens the layer, lowers the stresses on the weak subgrade and invariably enhances track geometry quality. Machine learning techniques are becoming increasingly imperative in processing and analyzing of large volumes of track geometry data which exhibit the classical attributes of big data. Several unsupervised and supervised learning techniques were used to analyze the effect of geocell installation on track geometry quality. Cluster analysis was used to group the track geometry data with major clusters found to differ by surface and alignment features. Principal component analysis was employed as an effective dimension reduction tool to simplify the track geometry data based on the proportion of variance explained. Supervised learning techniques such as multiple linear regression, decision tree regression, random forest regression and support vector regression were subsequently used to estimate and predict the effect of geocell installation on the track geometry quality. Random forest regression was found to the best performing model for both the original and dimensionally-reduced data.