A systematic maintenance process is essential to keeping railway systems safe and reliable. However, performing such maintenance is costly and often results in system disruption. There is a tradeoff between system safety and budgetary constraints; understanding the condition of the track infrastructure is essential to find the balance between needs and costs for decisions about when to perform maintenance. In this study, the track quality index (TQI), which is commonly used to evaluate the status of tracks and to decide maintenance interventions, is reviewed, including 12 TQIs for superstructure and six for substructure. A literature review indicates that TQIs for sleepers and subgrade have not yet been developed. The differences between TQIs are compared using a set of hypothetical raw data. Their capabilities for identifying track irregularities are also investigated based on the EN 13848 regulations. To classify TQI characteristics in a systematic way, this study proposes four concepts: accuracy, sensitivity, data required, and specificity. Accuracy indicates a TQI’s capability of detecting defects; sensitivity indicates how TQIs change according to variations in the defects; specificity relates to the amount of parameters considered, and the ability to pinpoint root causes or global consequences of defects. The results suggest a tradeoff between the four concepts, where high sensitivity can increase the ability to detect the smallest defects but may be affected by bias; more parameters considered may indicate low accuracy when detecting a single type of defect. Therefore, this study suggests railway regulators use multiple TQIs with complementary characteristics for classifying track status.

A typical objective of a railway agency is to design an efficient and reliable service plan. Although enhancing the level of capacity utilization may increase efficiency, the stability of the service plan may also decrease due to stochastic railway disruptions. Past studies used recovery time to evaluate the stability of a service plan; however, the variation of recovery time was not fully examined. Therefore, we developed a Monte Carlo simulation framework to address this issue and proposed four capacity-based indices for the evaluation of service efficiency and stability. These indices are capacity utilization efficiency, mean recovery time, dispersion of recovery time, and probability of unacceptable recovery time. The latter two new indices explored the variation of recovery time and probability of unacceptable long recovery time, which is undesired for railway operation. We conducted a real-world case study on the Taiwan Railways Administration before and after the service plan revision. Consequently, the northbound service plan needed a deliberate review and revision before implementation, whereas the southbound plan successfully increased capacity usage while not necessarily worsening stability. This result demonstrated that our study introduced uncertainty analysis into the evaluation framework and provided flexible information on the service plan. These developments can provide improved support in the decision-making of railway agencies to strike a balance between service level and asset usage.

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Assigning inspection trains to monitor track quality is a standard procedure for maintaining railway system safety. The main challenges lie in lacking time and resources to perform the inspections because of the increasing traffic nowadays. To overcome these challenges, many consider adopting the on-board monitoring (OBM) technique for performing the inspections. This technique assigns commercial trains, instead of traditional track recording vehicles (TRVs), to monitor the track status, allowing railway operators to perform more inspections without affecting the traffic and using expensive inspection trains as well. However, compared with TRV data, the new OBM data are of lower data quality and have fewer features, although they can be recorded more frequently. Therefore, new methods should be developed for effectively applying the new data. This study develops four models, namely the linear regression model, Markov model, ordinary Kriging model, and Kalman filter model, for predicting the track status based on the OBM data. Data collected from the Switzerland railway network are used for verifying the models. Results show that the proposed models can effectively predict the degradation of the track status in different ways and, therefore, assist railway operators in scheduling maintenance tasks.