A DATA ANALYTICS FRAMEWORK TO SUPPORT DECISION MAKING IN RAILWAY INFRASTRUCTURE ASSET MANAGEMENT

Abstract

The process of management of the assets of the railway infrastructure is becoming increasingly dependent on the big amounts of the condition-monitoring information produced by the recent inspection technologies. Although this type of data can give a detailed picture of the track condition, it also brings issues of interpretation, prioritisation and decision making. The current asset-management methods usually are based on evaluating thresholds and disjointed analysis tools, which restrict their strengths in promoting proactive and data-driven maintenance practices. The study creates and assesses a combined visual analytics system in order to aid decision making in the management of railway infrastructure assets. The framework integrates data pre-processing, analytical intelligence, machine-learning, and interactive visual analytics to convert raw track geometry data into actionable decision-support products. The research design was a mixed-methods research design comprising of two large-scale case studies, one of them on the basis of the UK and Saudi Arabian railway networks, and the other one on the basis of expert validation. The data of track geometry measured by Network Measurement Trains and Track Geometry Inspection Vehicles was analysed to prove the relevance of the framework to the different operational and environmental conditions. The case study of the UK is a fully developed, regulation-based data environment whereas the Saudi Arabian case study is a developing network that is functioning in the harsh desert conditions. Findings indicate that the suggested framework improves the interpretability of complex condition data using integrated 2D, 3D, and GIS-based visual analytics. The unsupervised and supervised methods were combined to form machine-learning techniques which enhanced the performance of fault detection and classification and led to quantifiable reductions in false positive alerts compared with the baseline threshold-based methods. A comparative analysis shows that the framework can be adjusted to differences in data maturity, regulatory environment, and operational issues. The study brings on board a transferable and validated visual analytics model that provides the balance between advanced data analytics and feasible decision support in the management of railway infrastructure assets.