Quality and measurement approach of railway track geometry: a review

Purpose This article aims to systematically review the state-of-the-art approaches for railway track geometry quality measurement and assessment. It identifies and compares available measurement platforms, classifies existing track quality indices (TQIs), and highlights current limitations and future research needs. Design/methodology/approach The review follows a structured literature search on Web of Science (2016–2026) using keywords related to track geometry, measurement and assessment. It categorizes measurement equipment into five types: trolleys, track recording vehicles, in-service railway vehicles, smartphones and drones. Quality assessment methods are classified into index-based (single and combined TQIs), vehicle response-based and smart (probabilistic and machine learning-based) approaches. Their formulations, advantages and limitations are analyzed. Findings Each measurement platform offers distinct trade-offs among cost, speed, accuracy and coverage. Most existing TQIs treat measurements deterministically, ignore wavelength-dependent effects and assume parameter independence. Vehicle response-based methods and smart TQIs (e.g. stochastic indices, machine learning) show promise in addressing these limitations. Key gaps include the lack of real-time onboard processing, probabilistic and wavelength-aware indices, drone-based quantitative measurement and harmonized measurement standards. Originality/value This review provides the first systematic comparison of measurement platforms and quality indices within a unified framework. It critically evaluates the limitations of conventional TQIs and identifies emerging alternatives. The findings serve as a structured reference for researchers and practitioners aiming to improve track geometry assessment and guide future developments in sensor technology, edge computing and standardization.