The Development of an Integrated Computing Platform for Measuring, Predicting and Analyzing Profile-specific Fixity of Railway Tracks

The current measures for railway track fixity in the UK’s railway remain at a relatively low level of granularity. This paper presents a pilot proof-of-concept study on the development of an integrated computing framework for improving the measurement, prediction, and analysis of profile-specific track fixity in the context of the UK’s rail network. The framework is aimed to produce a data integration and mining tool, which can determine track fixity parameters for any given section of track. In this study, we propose to measure track movement based on point cloud data and assess the track fixity by a set of parameters such as the direction and rate of the track movement relative to the plane of rail within a certain period. We seek to integrate a data mining algorithm into the framework to predict these parameters, given vast amounts of disparate and heterogeneous data of potential influencing factors in the area. From the study, we have developed a prototype framework, which allows the rapid implementation of data workflows with the necessary functionality. The feasibility of the prototype was demonstrated by training a random forest model on real data from an approximately 80-kilometer section of the East Coast Main Line, southeast of Edinburgh, Scotland. The modelling results indicate that curvature, cant, and maximum speed of trains are among the key factors that impact on, and are critical for predicting and analyzing, the profile-specific track fixity.