Improvement of railroad maintenance program: predicting the degradation level of railroad timber ties through the application of the random forest model

Railway infrastructure is one of the most significant pieces of the contemporary transportation sector, with railway ties being central components of railway tracks, whose deterioration poses substantial safety concerns. The main objective of this study is to find practical and optimal solutions to address the tie maintenance and replacement program by accurately estimating the proportions of defective and marginal ties that exceed or fall below certain thresholds for different classes of rail. As a result, machine learning (ML) methodologies are employed and applied to the most recent tie replacement data, alongside other influential input parameters. The random forest (RF) model demonstrated the highest accuracy in estimating the proportions of marginal and poor ties that either exceed or fall below predetermined thresholds over a defined timeframe following the last tie replacement. Although the results of two other models; decision tree (DT) and long-short term memory (LSTM), were also incorporated and displayed, the RF model consistently exhibited superior precision. When these thresholds are surpassed, it signifies the need to include the corresponding mileposts into the tie replacement program to ensure the safety and reliability of the operations within the railroad system. The data used in this study were obtained from the Canadian National (CN) Railway Company, spanning their entire rail network, integrating the data from inspection cars with some additional pertinent variables, totaling 45 parameters. The proposed approach has the potential to reshape the established practices and deliver a valuable improvement to current rail maintenance program.