Track quality is key to ensuring the safety and comfort of passengers and freight in railway systems. However, continuous monitoring is rarely implemented due to its high cost and technical complexity. This paper introduces a methodological framework based on machine learning and optimization algorithms for developing onboard track quality monitoring systems using inertial measurements. The workflow addresses crucial, often overlooked aspects such as sensor location, integrating them with downstream processes. The methodology was validated through its application to longitudinal level quality estimation. Synthetic acceleration signals were generated using multibody simulations under parameter configurations defined through a Design of Experiments framework. A multi-objective optimization approach was applied to determine the optimal combination of sensors, balancing estimation accuracy and efficiency. Among the evaluated models, XGBoost achieved a root mean square error of 0.175 mm on the test set, requiring only two acceleration signals and vehicle speed. The use of features derived from wavelet spectra instead of traditional statistical descriptors reduced the estimation error by approximately 20%. These results demonstrate the feasibility of constructing low-cost, data-driven monitoring systems for track quality assessment and highlight the benefits of a structured methodology integrating data generation, sensor analysis, and learning algorithms.
Sansiñena et al. (Wed,) studied this question.