In this thesis Axle Box Acceleration (ABA) data is used to determine the health condition of Insulated Rail Joints (IRJs) to investigate the potential of ABA data in predictive maintenance. Using frequency characteristics from the Continuous Wavelet Transformation (CWT), the main research question “How can Axle Box Acceleration data be used in automatic classification of specific defect types at Insulated Rail Joints?” is investigated and answered.

With the increasing demand for public transport systems worldwide and also a lot of these systems running at their maximum capacity, there is a strong need for finding ways for these systems to operate in a more efficient way. In recent transportation research there is an increasing attention for operational conditions and the impact of passengervehicles interaction on the timetables of urban rail networks. Passengervehicle interaction can have a strong impact on the operational conditions of an urban rail line as a large part of the dwell time of a vehicle can be explained by the number of boarding and alighting passengers.

Disruptions occur frequently in railway networks, requiring adjustments to the timetable, rolling stock planning and crew planning while causing delays and cancellations. Although the evolu­tion of system performance during a disruption can be visualized in the resilience curve, not much is known about performance during disruptions or the extent to which the curve applies in practice. The limited quantitative knowledge about the resilience of railway networks makes it hard to design appropriate recovery measures. In this thesis, a data-driven evaluation approach is presented to make an ex post assessment of the resilience of railway networks. Several resilience metrics are extracted from literature and two new resilience metrics are introduced. Using historical traffic realization data, resilience curves are reconstructed for a large and heterogeneous set of single disruptions and are quantified in terms of the resilience metrics. Among others, the values of the resilience metrics are compared across disruptions of different causes using Welch’s ANOVA and the Games-Howell test. The approach is applied to a case study of the Dutch railway network, with a focus on the five most common disruption causes. The results of the case study show that there is significant heterogeneity in the shape of the resilience curve, even within disruptions of the same cause. Train defects are found to be the least impactful disruptions on multiple resilience metrics, while collisions are found to be the most impactful disruptions on multiple resilience metrics. The successful application of the approach shows that it can be used by practitioners to assess which types and which parts of disruptions deserve attention to improve disruption management practices, and thus, improve resilience.

To better govern inventory control of spare parts in tandem with scheduling of maintenance operations in a railway setting, a Railway Spare Inventory & Maintenance Scheduling (R-SIMS) model is developed. To suit the railway industry setting the model includes an inventory control strategy which envokes continuous monitoring and a reorder level as well as an order up-to level. Moreover a condition based maintenance (CBM) strategy is used with periodical inspections. These strategies are combined in a simulation model which assumes stochastic step-wise deterioration of parts as well as stochastic lifetime lengths to predict the expected cost per part per period. The three maintenance scheduling and spare inventory decisions to be minimised in terms of their resulting costs are 1) when to replace parts based on their condition, 2) when to buy new spare parts and 3) how many spare parts to procure at each order. These decisions are represented by values of the three decision variables: the CBM threshold L_p, the reorder level s and the order up-to level S. The minimisation of these decision variables is performed through surrogate modelling, a branch of machine learning optimisation techniques which deals with complex black-box functions. It does so by running experiments and estimating a surrogate function which in turn is optimised mathematically. Multiple surrogate modelling methods are compared in experiments to test their performance, speed and stability. These experiments showed that the Tree-structured Parzen Estimator algorithm as used in the HyperOpt Python library was one of the best performing methods (only rivaled by the MVRSM algorithm), moreover it was shown to be the fastest as well as the most stable of the tested algorithms for this particular problem application. This research contributes to the existing theory by creating a modelling framework for the joint optimisation of spare inventory and maintenance scheduling decisions which is specifically tailored to the railway context and includes possibilities for minimal maintenance. In practice this model can be used by maintenance providers to increase the financial success of their maintenance operations and by infrastructure managers to increase insight into their maintenance providers' operations.

Unreliability is a major source of discontent for passengers using public transport. Real-time rescheduling is one of the tools available to deal with it. Although passenger movements have an impact on the development of a disturbance in public transport operations, this effect is rarely accounted for in rescheduling models for metro networks. The aim of this thesis is to develop a rescheduling framework for metro networks that mitigate the impact of disturbances on passengers whilst considering the dynamic variation of demand and its effect on train operations. To achieve this, the Simulation-Based Traffic Management for Metro Network (SBTM-MN) framework is developed. This comprises two models that interact iteratively, the Transport Simulation Model (TSM), and the Train Rescheduling Model (TRM). The TSM integrates a train simulation in the microscopic train simulation tool OpenTrack, with a passenger module constructed to keep track of passenger allocation and compute train dwell times according to the passenger exchange. The TRM modifies arrival and departure times of trains according to a base schedule and the corresponding passenger allocation provided by the TSM. A series of experiments are carried out on the SBTM-MN, and on each of its components, the TSM and the TRM. These experiments provide insights into the effect of passenger allocation on the rescheduling measure, the trade-off between passenger and operators perspective, and the impact of demand on the development of disturbances. The case study consisted of lines D and E of the Rotterdam metro network, with northerly direction.

Passengers often complain about dirty trains indicating the relevance of interior cleaning of rolling stock (RS). Servicing tasks (i.e. interior and exterior cleaning and smaller technical checks) are executed on a daily basis at service locations (SLs). Currently, due to train operations during daytime, the current focus lies on night servicing. In this thesis daytime servicing is considered in order to tackle the capacity shortages at SLs. Therefore, the Rolling Stock Servicing Scheduling Problem (RS-SSP) is developed comprising a Mixed Integer Linear Programming (MILP) model. By complying with the planned timetable, the RS-SSP maximises the RS units being serviced during daytime. The RS-SSP allows RS exchanges between RS units having completed servicing and operating RS units requiring servicing. Due to this RS Exchange Concept, the number of RS units visiting the SL during daytime can be increased. Within the thesis three RS-SSP model versions have been developed: the RS-SSP Base Model and two model extensions. The RS-SSP Base Model considers trains running with a single RS unit per train and RS units to be immediately serviced when entering the SL. The first extension (RS-SSP-MU) considers multiple unit trains and the second extension (RS-SSP-MU-W) allows RS units to wait for servicing. The proposed RS-SSP models have been tested on a real-life case from the Dutch railways. The RS-SSP-MU-W yielded the most feasible and improved solutions as compared to the other two model variants. For multiple scenarios, the model was able to exchange all running RS. As a conclusion, the capacity usage at SLs can be increased by the RS-SSP by shifting the excessive workload to daytime, and thus solving the capacity shortages. As the RS-SSP model is a generic model, it may not only be applied to other railway operators, but also to other public transport companies. Further extensions on the model are suggested for an appropriate applicability on large scale.

Computers are preferred above humans when dealing with information processing because of the great memory and fast computing times. The use of algorithms within complex planning processes is an interesting research field. These algorithms should help human planners with the planning process and therefore are called Planning Assistance Tools (PAT). However, the implementation of a PAT often fails because of the lack of acceptance by human planners. The lack of acceptance is often linked to the fact of human aspects that are missing within the PAT. This research provides a methodology to identify the missing aspects using a developed PAT, real-life planning assignments and experienced planners within the railway node planning. Based upon empirical experiments, a list of aspects is formulated that are translated to extensions that are implemented in the PAT. The application of such a method to a real-life case has not yet been mentioned in literature before. The research proves that the implementations have a positive effect on the quality of the solution provided by the PAT, while not harming the performance of the solution. Planners seem to better understand the solution of the PAT and the decisions made by the PAT, which increases the level of trust and the level of acceptance. The method used to identify aspects and implement them might be applicable within the industry for planning tools that are already developed, but not yet implemented because of missing acceptance of human planners.

This MSc thesis research has the objective to empirically determine the perceived stochastic nature of the deceleration regimes and to determine the impact it has on infrastructure occupation within the network corridor. This research expands on the data-driven reconstruction model to estimate the speed profiles of realised train runs developed by N. Besinovic et al., calling it the 'Deceleraton Reconstruction' (DR) model. This model elaborates on the deceleration regimes of the speed profiles in a more dynamic and generalised manner to provide a more detailed description of the realised deceleration behaviour, through introducing the concept of sub-regimes to describe the deceleration regimes and the concept of a non-uniform braking rate in the braking regimes. This research expands on the number of data sources used for location tracking of the realised train runs and introduces the concept of 'Data Fusion' in which the different sources of location tracking are combined in order to provide a more reliable and more detailed input for the DR model. The results of the empirical and comparative analysis, provide interesting insights to the correlations between the departure delays, realised running times and deceleration loss time performance, and provides insights to the impact the deceleration behaviour has on infrastructure occupation. The DR model provides interesting results regarding the quality and detail of the estimated deceleration behaviour (e.g. braking rate, deceleration regime profile) and compares these in relation to the nominal characteristics used in timetabling tools and simulation models.

As the demand for the railways is expected to raise in the future, researchers are looking for ways to improve the railway capacity to increase the transport ability. Virtual coupling is a new solution based on the moving block technology, which further shortens train separation from absolute braking distance to relative braking distance. This will also affect the train service schedules for optimal operation under virtual coupling. In this study, a mixed integer quadratic programming method has been proposed to schedule the train services under virtual coupling on a network. Train operation as well as the headway and capacity benefit have been analyzed. In comparison to moving block, virtual coupling will change train operation on shared routes, and the capacity will be improved by different percentages for different timetable patterns and different service braking rates with or without speed limit restrictions.