This thesis addresses a hypothetical scenario in which all flights within most of Europe are substituted by rail and estimates the resulting additional rail demand, with a focus on night train demand. Routes, cities and countries with high potential for (night) trains under maximum air-rail substitution are highlighted, and rolling stock requirements are estimated. Several research gaps regarding long-distance travel behaviour and mode choice are shown to require assumptions in the modelling approach; the results are indicative of areas with higher demand potential for night trains than others.

On railway lines with scarce infrastructure capacity, allocation methods allow for the assessment of the capacity requests from railway undertakings. The European Commission introduced a regulation to ensure a uniform approach to capacity allocation across all member states through socioeconomic and environmental criteria. However, relevant experiences and methods related to such criteria are hard to come by across Europe, meaning that such criteria' impact on capacity allocation is unknown. In this paper, we provide a method that applies the European criteria and can give insight into the possible impact of this new way of allocating capacity. The railway line Deventer – Bad Bentheim case study allows for the application of the method and defines its possible impacts. Compared to the current Dutch approach of distributing capacity scarcity, open-access train services have higher chances of receiving capacity access. The result shows how railway undertakings will behave more beneficially to society during capacity allocation procedures following the implementation of the method. Recommendations to improve the method are to reduce the number of assumptions, provide an inflation correction across all monetary parameters and consider using minimal frequencies.

With the ambition of policy makers to encourage a modal shift to rail, an increase in the demand for running trains can be expected. Capacity wise, this increase in demand could be facilitated by applying moving-block signalling. If railway traffic increases however, so does the difficulty of managing it. Rescheduling systems are currently being developed to help traffic managers with this task. An increase in traffic does however increase the computation time needed for solving the conflict resolution optimization problem tackled by these systems. This could pose a problem since traffic management is a task performed in real-time. An often proposed technique to reduce computation time for conflict resolution is decomposing the problem into multiple coordinated sub-problems. Until now, no research has been performed combining moving-block signalling with decomposition of the conflict resolution problem. This research addresses this gap by developing and testing a distributed moving-block conflict resolution model. The effect of the model on computation time and solution quality in comparison to a centralized model is investigated through a case study of the rail network of the Dutch province Noord-Brabant. The results show a clear improvement in computation time for the distributed model while the solution quality improves or remains the same in the majority of tested scenarios.

Virtual Coupling (VC) has the potential to significantly enhance railway infrastructure capacity and enable more flexible scheduling by introducing vehicle-to-vehicle (V2V) communication between consecutive trains. Inspired by platooning implementations in truck and bus platooning, VC could reduce operational costs and improve passenger satisfaction. However, its effective implementation in real-time railway operations remains uncertain, particularly regarding its impact on passengers, infrastructure managers, and railway undertakings. This research explores how VC can be implemented as an operational concept for mainline railway services, leveraging its potential to enhance flexibility and frequency to benefit passengers, railway operators, and infrastructure managers., identifying four operational concepts based on similar transport applications. Four operational concepts were identified, inspired by similar implementations in other transport modes. A SWOT analysis was conducted to qualitatively select the most promising options for further evaluation. For these concepts, running times and headways were calculated and tested within a real-world case study, focusing on key performance metrics such as generalized travel times, capacity utilization, and maximum frequency. The analysis revealed that a skip-stop pattern with synchronized arrivals at major stations is the most effective concept, facilitating efficient transfers and enhancing operational flexibility. Key recommendations include integrating real passenger demand data to better assess the absolute impact of operational variations and refining strategies for effective train coupling to advance VC implementation.

The Ministry of Infrastructure and Water Management set up the ERTMS Programme and commissioned ProRail to realize the rollout. In Dutch vernacular, ERTMS is often used, but the system that replaces the legacy dutch system is ETCS Level 2. This rollout of ETCS Level 2 in infrastructure is currently being done on 7 sections of track in collaboration with 5 engineering firms that form the knowledge alliance. Although the rollout of ETCS Level 2 in the Netherlands is still in progress, research into new ETCS variants is ongoing. One of these variants is ETCS Hybrid Level 3. In ETCS Hybrid Level 3, Virtual Sub-Section (VSS) are introduced, allowing smaller track sections without additional investment in Trackside Train Detection (TTD). The use of VSS requires a Train Integrity Monitoring (TIM) system in the train that monitors train integrity. Out of the analysis of this paper, it can be concluded that the implementation of ETCS Hybrid Level 3 will results in changes within the Radio Block Center (RBC), Interlocking (IXL), rolling stock and user processes. The scale of the challenges that will occur by the implementation of ETCS Hybrid Level 3 depends on the implementation strategy that will be applied. Within the rail sector, there is much uncertainty as to what benefits are desired and feasible, and what implementation steps are required to achieve them. Analysis have shown that an potential implementation of ETCS Hybrid Level 3 will arise a significant challenge on an organisational level within the Dutch rail sector.

Capacity and performance (evaluated through delay propagation) analysis methods have mostly focused on railway line track sections, but less attention has been given to nodes. Still, a few analytical methods for the capacity and performance assessment of the switch areas between station platform tracks and line tracks can be classified upon their reliance on a timetable (“timetable-based” methods) or not (“timetable-free” methods). The relation between capacity utilisation and performance has rarely been tested for railway nodes, and critical capacity utilisation thresholds remain to be investigated. The comparison of timetable-based and timetable-free methods also needs to be conducted. Filling these knowledge gaps will help the French infrastructure manager SNCF Réseau improve its analyses of nodes capacity utilisation and performance in the long-term planning stages. This paper investigates a small set of timetable-based and timetable-free methods either taken and adapted from the literature, such as the Potthoff and UIC 406 methods, or developed for the need of this research, such as adaptations of the UIC method and a method developed from SNCF Réseau’s previous works. The methods are applied on a case study on the French network, first evaluating their indicators’ magnitude and trends with artificial traffic data, and then comparing their outputs to real data. It is found that the Potthoff method and a timetable-free UIC-adapted method for capacity utilisation evaluation and an SNCF-adapted method for delay propagation provided results that are relevant in terms of magnitude and trends for long-term assessment. The timetable-based UIC 406 method for node capacity utilisation assessment can be used to study specific timetables. No satisfactory timetable-based delay propagation method was found in this paper. The timetable-free methods are further used to study the capacity utilisation – delay propagation relationship, which takes the form of an exponential function. Attempts to determine capacity utilisation thresholds are also conducted. It is recommended to perform further research with extended traffic data on different node layouts to consolidate these preliminary findings before applying them in real studies.

The planning of railway operations is a very complex process, in which timetables and other logistical plans need to be both reliable and robust to effectively prevent and cope with disturbances. However, research in the robustness of initial stabling plans, designed in an earlier planning phase, during the following planning phases, has been lacking, with the models created in research in the Train Unit Shunting Problem (TUSP) being generally deterministic in nature, even though stabling plans could quickly become infeasible when the stabling demand in the form of arriving and departing trains changes.
This thesis therefore proposes a definition of the robustness of an initial stabling plan to changes in the stabling demand, such as changes in train lengths, as well as provide an assessment method of said robustness. The created Robustness Assessment Model (RAM) first generates an initial stabling demand and stabling plan, then performs a Monte Carlo simulation to generate a set of stabling plans created for variations of the initial stabling demand, based on changes in stabling demand such as train length. Finally, the RAM estimates the robustness of the initial stabling plan by analysing the differences between the initial and these generated stabling plan variations and how efficiently the initial plan is able to change to these plans to optimally facilitate the variations of the initial stabling demand.
Running this model across three locations, each with three capacity utilisation scenarios, has shown that the model is able to estimate the robustness of an initial stabling with acceptable confidence, and furthermore is able to give insight into how capable the stabling plan creation method is in generating a robust solution. Furthermore, the RAM can also be used to investigate patterns in stabling plans which could predict the robustness of said stabling plans. Current shortcomings to the RAM are its relatively long runtime, the simple stabling plan creation process used in the RAM, and that only one-sided stabling yards without servicing scheduling have been incorporated. Further research is therefore recommended for extending the RAM with other stabling yard layouts and the scheduling of services, as well as improve the stabling plan generation process to take more constraints into account.

In order to track the speed profile, a case study using a linear quadratic regulator (LQR) is addressed in this paper. A time-invariant nonlinear train motion model is constructed. To apply LQR, the quadratic term of the aerodynamic drag and the rolling mechanical resistance is linearized. Gradient resistance, curve resistance, and other environmental disturbance are not considered in this paper. An LQR algorithm with constraints is proposed to track a given reference speed profile. Maximum error and cumulative error are used in the evaluation of the tracking performance. The tracking results in plots, parameters tuning, and performance analysis are shown. The results show that the proposed LQR algorithm is able to track the given 1200 seconds speed profile, the cumulative error can be controlled within 4.7478m/s, and the maximum absolute error is 0.90839m/s. The output of the control variable u is also given.

This paper presents a train robust control method to optimize train operation based on the concept virtual coupling on train platoon. This approach is inspired by the recent development of platoon control for autonomous vehicles, and it is hoped that this platoon control can be applied to railway transportation. We use a decentralized model predictive control (MPC) to control leading train and followers together. To solve the complexity minimax objective function, we reformulated objective function as a minimization problem subject to linear matrix inequalities (LMIs). We defined four weight parameters to evaluate the model. Simulation result indicated that based on the premise of platoon stability, increase the performance parameters to obtain an optimal solution. We show that after the virtual coupling of less than two minutes, the gap distance between two consecutive trains is reduced and the capacity is increased while ensuring safey.

Public transport is important for society, it provides accessibility to opportunities. Accessibility is not distributed evenly. Some inhabitants are disadvantaged, which has negative impacts on society. The distribution of accessibility between inhabitants can be measured with transport equity. The PT network should be improved in order to reduce the disadvantage of inhabitant groups. It is not defined how this could be done for regional PT networks. A six step assessment methodology is created for this purpose. The assessment method addresses what objective focus should be applied to, what improvements are possible in PT networks, what measures should be applied and what the equity effects of these measures are. Application of the assessment method yields that substantial equity improvements are possible within the Alkmaar – Den Helder railway corridor. Marginal equity improvements are achieved by changing rolling stock, significant improvements with local doubling of single track and substantial improvements when additional stations are opened. The assessment methodology is also able to identify the presence of trade-offs between inhabitants by mutual comparison.

The impact of delays and disturbances in railway traffic can be mitigated by advanced rail traffic rescheduling models (RTRMs) which make use of mathematical optimization models. In the past several researches have been carried out on the effectiveness of an RTRM in reducing delay and improving punctuality. However, in most of these researches only one case study is used to test the RTRM. Still, it is unclear whether the results found for the application of an RTRM in one particular situation, are also valid for other situations (with different infrastructural and operational characteristics).
With this research, the impact of different infrastructure layouts and traffic patterns on the effectiveness of rail traffic rescheduling models is investigated. It provides insight into whether the benefit of an RTRM depends on the infrastructure and timetable in the area where it is applied.
For this purpose, an evaluation framework has been developed in which an RTRM can be tested using different infrastructure, operational and disturbance scenarios. In this framework, the RTRM is used to generate a real-time traffic plan for each scenario. These traffic plans are compared with the traffic plans of simple dispatching rules, that can be used in practice by dispatchers. For this comparison, KPIs such as the sum of consecutive delay (amount of delay that propagates within the network) and punctuality are used. This framework is applied to an alternative graph-based RTRM, which is formulated as a MILP (mixed integer linear programming).
The results show that the improvement an RTRM can offer over simple dispatching rules, varies per infrastructure layout and traffic pattern. For some infrastructure and operational scenarios, the simple dispatching rules perform as well as the RTRM, which means that for these situations, implementing an advanced RTRM does have much added value. A trend has been observed that the effectiveness of the RTRM increases as more control options are available.

Despite advanced communication, monitoring, and control facilities, train operations are still subject to uncertainties that can disturb train services, cause delay to multiple trains, and propagate through the network. One option is to mitigate delay propagation in the timetable design by adding buffer time to the minimum difference between the time two successive train of either direction enter a section. It is still common practice to design buffer times based on a deterministic value, decreasing operational capacity and requiring large amount of manual checking by planners. Existing approaches to effectively allocate buffer time in timetables lack flexibility and require an initial timetable. In this paper, a data-driven approach for determination of buffer time planning rules suitable for usage in an initial timetable is presented. These planning rules are not necessarily generic, but rather depend on timetable characteristic. Two metrics that describe delay propagation, mean secondary delay and hindrance percentage, are extracted from literature and predicted in a regression analysis with the use of timetable characteristics related to headway situations of two succeeding trains. The results of the regression analysis on a case study of the Dutch railway network between Haarlem, Leiden Centraal and Schiphol Airport are used to determine the amount of scheduled buffer time that would ensure a certain amount of hindrance percentage given a specific headway situation. The results show that the mean secondary delay and hindrance percentage for various headway situations can both be predicted with an accuracy of 90.7\% based on timetable characteristics and is quite heterogeneous. Mean secondary delay appeared not significantly impacted by the scheduled buffer time, contrary to hindrance percentage which is significantly influenced by the scheduled buffer time.

Moving block signalling promises a significant reduction of the infrastructure occupation compared to a fixed block system, such as NS’54/ATB. This is mainly caused by a strong reduction of the approach and running time. However, to assess the capacity gains track occupation data, such as blocking times are needed. ETCS L3 Moving Block is still in development, so gathering data out of daily operations is not possible. Also gathering moving block data out of simulation isn’t always a convenient solution. For example, in simulation software FRISO, used at ProRail, ETCS L3 Moving Block is not (yet) implemented.
With infrastructure data, rolling stock parameters and planned time-distance data, blocking times for a moving block signalling system can be estimated. The model presented in this thesis has an average error of 0.87s to the blocking time. In 95% of the cases the error is within a range of (-3,3) seconds. Given that ProRail plans with a precision of 6 seconds, it can be concluded that the model both precise as accurate. With the blocking times of all trains, bottlenecks in both railway corridors and complex nodes can be identified. One could sum all blocking times per block and consider blocks with the highest summed blocking times as bottleneck. However, this can only be applied for homogeneous traffic situations. Another approach is identifying bottlenecks by the shortest buffer time between two trains, also called a critical block. This can be applied for both homogeneous as heterogeneous traffic situations. An advantage is that it is not needed to split corridors into line sections. One could analyse a whole network at once and identify bottleneck at a microscopic level. By keeping the same timetable, the buffer time between two trains increases on average by 75 seconds (60%) using moving block over NS’54.

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.

Due to the continuous expansion of the urban scale, the urban metro transportation system also needs to be constantly updated, especially those cities that need to carry mega-events. Larger carrying capacity, better collaboration mode and more optimized operation mode are required to handle the sudden increase in travel demand. The ever-growing micromobility system and the multimodal transport system of micromobility and other public transportation methods can well meet this trend.

This thesis aims to develop a multimodal transport optimization model that takes full advantage of the micromobility mode. This thesis, from the perspective of the traffic system administrator, explores the influence of micromobility on the cost of the entire traffic system under the premise of maintaining the normal operation of the metro system after micromobility joins the traffic system at the same level as the metro.

Firstly, through literature review, the research object is determined to be a representative shared bike in micromobility mode. The way to deal with the excessive travel demand brought by mega-events is to add shared bikes to the transportation system with the same status as the metro on the basis of maintaining the normal operation of the metro system, and cooperate with the metro system to meet the demand.

Secondly, the research methods are qualitative research and quantitative research. In the qualitative research, model design, adjustment and optimization are carried out in combination with actual experience to realize the design of the multimodal transport system of shared bike and urban metro system. In quantitative research, this thesis describes a case study area based on real condition and proposes six scenarios. And through demand forecasting and parameter changes, this thesis explores the situation that only metro participates in the transportation system, and micromobility mode participates in the transportation system with metro with different parameters, etc., under the premise of meeting the excessive travel demand brought by mega-events, impact on total travel costs.

Through model optimization and scenario verification, it is concluded that when the micromobility mode participates in the transportation system in a reasonable way, the multimodal transport system will be able to reduce the total cost, and the degree of reduction is related to the characteristics and parameters of the mode.

Railways in Europe need to reduce CO₂ emissions and energy usage to contribute to sustainability. One of the measures that railway undertakings can apply with low investment cost and both high reductions in CO₂ emission and energy consumption, is energy-efficient train trajectory optimization. Another efficient measure is incorporating energy-efficiency in the timetable design. The aim of this thesis is to incorporate energy-efficient train trajectory optimization in the timetable design in order to improve the potential for energy-efficiency of railways. The thesis develops and applies models for the energy-efficient train
control problem for a single train over (multiple) stops with both mechanical and regenerative braking behavior. In addition, energy-efficient train trajectory optimization is incorporated in timetabling by formulating and developing algorithms for a multiple-objective optimization problem applied on a corridor with multiple interacting trains.

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.

In busy passenger railway networks, a large amount of trains have to be parked in shunting yards off the mainline every night, where they will be cleaned, maintained, sorted and parked. This problem is known as the Train Unit Shunting Problem (TUSP), which is a hard combinatorial optimization problem faced by railway operators. The TUSP is currently solved using human heuristics, which is difficult and time-consuming. Reinforcement learning approaches have been developed in the last few years to efficiently approach this problem. In this research we develop, in a multi-agent deep reinforcement learning framework, an heuristic for random exploration to efficiently search the state-action space and two heuristics for train routing strategies, which aim at improving the performance and quality of the produced route plans. On one hand, we develop the Type-Based Routing Strategy, based on the idea of parking trains of the same rolling stock type on the same tracks. On the other hand, we develop the In-Residence Time Routing Strategy, based on parking trains ordered according to their departure time. Both routing strategies consists of four components: (1) standard parking rules, (2) combination and split rules, (3) conflict resolution rules and (4) unnecessary movements rules. The goal is to incorporate information from the logistics side of the problem into the framework. We demonstrated the performance of the heuristics developed in two real-life cases in the Dutch railway network. The results of the experiments carried out demonstrate the potential of the resulting framework to produce more efficient route plans and to adapt to different problems designs such as different matching problems types and different shunting yards.

As the demand for transport of goods and passengers increases, the capacity of railway networks is becoming more and more saturated. Railway infrastructure managers aim to increase the rail network capacity with the minimum possible investments in infrastructure. Next generation signalling concepts such as Moving Block (MB) and Virtual Coupling (VC) are expected to bring significant capacity benefits to railway corridors. The aim of this thesis is to identify capacity benefits of Virtual coupling over ETCS L3 MB considering a realistic operational setup, where different rolling stock characteristics, driving behavior and traffic information updates exist. Inspired by safety distance car-following models, the multi-state train-following model developed by Quaglietta et al. (2020) is enhanced with a dynamic term, called Dynamic Safety Margin (DSM). The DSM ensures that the following train can come to a standstill at any time applying service braking, even in the worst-case scenario when the leading train applies emergency braking. The DSM accounts for positioning errors, the Vehicle-to-Vehicle (V2V) communication update delay, different train control delay times, and braking capabilities of trains running as a convoy. An One-at-a-time (OAT) sensitivity analysis of the proposed model is conducted to evaluate the importance of selected model parameters in railway capacity. The model parameters under investigation concern the V2V update delay, the train control delay time, and the braking rate of the following train. The braking rate of the following train is proved to have a significant impact on railway capacity, implying that trains with better braking performance imply shorter time headways. To identify potential capacity benefits of the proposed model over ETCS L3 MB, operational scenarios are executed through microscopic simulation in the EGTRAIN tool. The scenarios consider three different rolling stock types, being suburban, regional, and intercity trains. For trains running on open track without stops, the proposed model promises the highest capacity benefits over ETCS L3 MB, for trains with improved braking and acceleration characteristics. For train services with stops, all the train convoy combinations show promising capacity benefits, which are higher for heterogeneous train convoys. Moreover, headways can be reduced to the minimum possible when faster trains follow slower trains. Finally, this model enables multiple train convoy formation based on a predecessor-follower communication topology, where a safety distance is always ensured between trains.

Technological innovations in the Dutch railway sector, such as ERTMS and the 3kV power supply system make it possible to increase the efficiency, effectivity and speed of the railway system in the upcoming decades. The implementation of systems such as ERTMS and 3kV is a lengthy process. The new systems will therefore operate alongside the old systems (ATB-EG and 1.5kV) for a long period of time. Between the old and the new system, operational transitions are required. The number of operational will therefore increase in the coming years. Some of these transitions from one system to another system turn out to be prone to failures and disruptions. When an operational transition fails, this can cause considerable delays on the railway network. Especially when multiple operational transitions are close together and occur nearly simultaneously, the risk of failures in one of these transitions is present. When operational transitions are combined, a failure in one transition can have large effects on another operational transition that is about to take place. Operational transitions are defined by two characteristics. Firstly, they are physical locations in the rail infrastructure. Secondly, operational transition require the driver to switch between two systems, or to change his/her behaviour significantly. The aim of this thesis is to investigate the effect of operational transitions on the reliability of train operations. The magnitude of the impact of operational transitions on the reliability of train operations is often unclear. This thesis contributes to scientific literature by examining a sizable number of operational transition types and providing an initial insight into the effect of these transitions on railway operations. For society in general and for ProRail in particular, the findings of this research can contribute to a better understanding of operational transitions. Using the acquired knowledge, ProRail can take measures that increase the reliability of operational transition passages, thereby increasing the punctuality of train services.