COMPARATIVE ANALYSIS OF TRACK QUALITY INDEX CALCULATION METHODOLOGIES USING REAL GEOMETRY DATA

Abstract

In modern railway systems, maintaining a high track quality is a key prerequisite for safe and efficient railway traffic operations. The Track Quality Index (TQI) serves as a quantitative indicator reflecting the technical and geometric properties of the track, forming the foundation for maintenance planning and intervention activities. This paper presents a comparative analysis of several TQI calculation methods to determine their advantages, limitations, and applicability across various contexts of railway infrastructure. The research evaluates three widely used methods for determining the track quality index: the Polish quality indicator (J), the multi-parameter unified index, and the percentage-based index. For the purpose of this analysis, all three methods were applied to a specific 5-kilometer railway section, utilizing available track geometry measurement data (longitudinal profile, gauge, alignment, and twist). The results demonstrate that while all three approaches show a high correlation in the overall assessment, they exhibit varying sensitivity to specific types of defects. The weighted-factor method (unified index) allows for a more precise identification of critical track locations, whereas the standard deviation-based approach is easier to apply and interpret but proves less informative in complex geometric deformation scenarios. The statistical approach provides a solid foundation for automated analysis, though it requires precise calibration of threshold values according to national or operator standards. The study concludes that there is no universally superior method for calculating the track quality index; rather, the selection of an appropriate methodology should depend on specific infrastructure management goals, data availability, and the required balance between accuracy and simplicity. The combined use of multiple methods is recommended for long-term planning, alongside the future development of a hybrid index that integrates the strengths of existing approaches for real-time track condition assessment.