Data-Driven Prediction of Track Quality Index (TQI): A Comparative Study of Statistical and Ensemble Learning Models—A Case Study of the Kolashin–Podgorica Railway

Maintaining the geometric quality of railway tracks is essential for safe and efficient operations. This study examines the prediction of the Track Quality Index (TQI) using Multiple Linear Regression (MLR) and Random Forest (RF) models, based on inspection data from the mountainous Kolashin–Podgorica railway section. Data collected from 2017 to 2022 were used for model development, while 2024 data served as an independent validation set. The primary contribution is a high-resolution analysis of 20-m homogeneous track units across multiple years, combined with independent validation. The dataset includes precise measurements segmented into 20-m units, covering infrastructure, geometric, operational, and maintenance variables. Model variables were selected for their relevance to track quality and data completeness. Both models were trained on scaled input data and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The RF model outperformed MLR, achieving a higher R2 (0.69 versus 0.57) and an MAE approximately 15% lower. Furthermore, the RF model identified severe localized degradation trends and demonstrated robust screening capability, achieving 86.4% sensitivity in detecting high-risk track segments requiring urgent intervention. These findings highlight the effectiveness of ensemble machine learning methods in reducing prediction errors and enabling proactive, data-driven track maintenance in complex railway networks. This methodology allows railway engineers to identify segments most susceptible to rapid deterioration, supporting more precise scheduling of tamping, renewals, or other targeted interventions. Model outputs can assist managers in prioritizing maintenance activities, optimizing resource allocation, and minimizing unexpected failures, thereby enhancing safety and cost efficiency in daily operations. However, the study is limited by its focus on a single railway corridor and a relatively short observation period with few maintenance events, which may restrict the generalizability of the findings to other lines or maintenance regimes.