Punctuality in railway transport is a critical concern for passengers, especially during unavoidable disturbances. Therefore, a sufficiently robust timetable is necessary. This thesis proposes a new timetabling method that minimizes passenger delay as the optimization objective, aiming to create a passenger-centric robust timetable. The model is implemented in the Dutch railway network in the Eindhoven-Den Bosch-Tilburg area to verify and validate its correctness and functionality. Experimental results demonstrate that the proposed model significantly reduces passenger delays when dealing with specific disturbances, and the improvement in robustness becomes more pronounced as the severity of the disturbances increases.

This paper proposes an innovative macroscopic timetabling model aiming to optimize railway infrastructure to accommodate the future increasing demand. The model applies the PESP formulation to railway operations and formulates it as a MILP model. The number of tracks on each portion of the infrastructure is flexible, and so are the conflicts between different trains. The model minimizes the overall construction and operation costs while ensuring a conflict-free timetable for the long-term demand. A case study on the Swedish Mälarbanan line demonstrates the model’s application and validates the microscopic feasibility of the produced timetables. Model shows promising results by suggesting low infrastructure upgrades while offering total travel times almost equivalent to the case where the entire line is upgraded.

When trains are not actively traveling on the main rail network, they to be parked and prepared for their next journey. This is a complex problem, involving several interconnected subproblems. Additionally, there is uncertainty in this environment which can render initial plans infeasible during their execution. To ensure trains are able to depart in time, having finished all their required service tasks, a schedule is created in advance.

The focus of this thesis is to address the challenges associated with generating robust initial shunting plans in an uncertain environment. This thesis focuses on a sequential problem formulation, modeled as a Markov Decision Process (MDP) and uses a policy optimized for this environment. The goal is to design a method capable of deriving robust initial shunting plans from the policy that are likely to remain feasible for a large number of possible plan executions.

The limitation addressed in this thesis, is that conventional policy-rollout techniques generate action sequences that overlook most alternative outcomes, thereby making the overall plan not feasible for a large number of plan realizations. To address this issue, the thesis proposes two distinct solution methods, aimed to consider every possible state that might be encountered, either directly or indirectly.

Through experimentation on realistically generated problem instances, the research concludes that both proposed methods significantly outperform the baseline approach, demonstrating the possibility of extracting robust initial shunting plans from a given policy that was not explicitly designed for this purpose.

Over 700 trains in the Netherlands are used daily for passenger transportation. Train operations involve tasks like parking, recombination, cleaning, and maintenance, which take place in shunting yards. The train unit shunting problem (TUSP) is a complex planning problem made more difficult by uncertainties such as delays. Most existing approaches overlook these disturbances and the approaches that consider them incorporate heuristics to enhance the robustness of their solutions to disturbances. This thesis proposes an alternative approach: utilizing probabilistic programming to turn an existing planning algorithm and simulator into a generative model of the TUSP. The model introduces disturbances without the need to modify the planning algorithm or simulator. Through two types of inference, we infer a distribution of robust solutions for the TUSP. Empirical results demonstrate the effectiveness of our approach for inferring robust plans in small-scale scenarios.

To develop a demand-oriented train service plan, the railway passenger demand characteristics are needed to be analyzed first. The analysis mainly starts with the volume and structure of the demand, and based on these characteristics, periods that have similar characteristics are classified into the same pattern. Thus a period of time, for example, a week, can be divided into several periods with different demand patterns. One period is considered as the base demand period among all those periods, and the demand contained in this period is always present during the week, its fluctuations are small and occupy the vast majority of the time except for the peak period.

Unlike the existing method that the train service is designed according to the peak hour demand, the train plan will first be designed based on the base demand. This base service will exist all through the period (e.g. in one week) and provide service for all base demand services. The service adaption will be made and added to the base service for all other periods. According to the different demand patterns, those adaptions can be add-on or subtracted services. Those adaptions can adjust the service to match the changed demand over time.

The Dutch railway network is a dense network with 7000 kilometers of railway tracks spread over only 42 000 square kilometers [1, 2]. Additionally, the railways are responsible for 1.4 million passengers every working day [1]. These factors make both the infrastructure and timetable of the Dutch railway network extremely intricate. On top of that, the amount of train trips is expected to grow with 40 % by 2040 [3] and with that the complexity of managing the railway network will increase even more. In the case of conflict in the timetable, action must be taken to resolve this conflict in order for the train operations to continue. In current Dutch practice this task resides with the train dispatchers. However it can be difficult for the dispatcher to oversee the situation when multiple trains get involved and taken actions will have consequences on yet another selection of trains. This increasingly complex task has resulted in a grown interest in so-called Traffic Management Systems (TMS), which are an intelligent systems that use a conflict resolution algorithm to find a solution to a conflict. A TMS can be used to support the train dispatcher...

With the strong backing and advocacy from the EU for railway transport, it is crucial to focus on innovative and efficient technologies to maintain service quality. In daily train operations, traffic uncertainties lead to perturbations, which may result in two types of track conflicts: disturbances and disruptions.

In the current research, we aim to add and optimize timetable flexibility in traffic disturbance management for railway transport. A new definition is proposed for timetable flexibility. Timetable flexibility is defined as the ability of a timetable to be easily modified to withstand small disturbances and absorb delays, as well as to offer a larger solution space in the application of dispatching measures (retiming, reordering, rerouting) to solve larger disturbances without changing the given (re)scheduled timetable.
In order to minimize the deviation of the rescheduling plan from the rigid timetable, and maximize the timetable flexibility, the conflict resolution problem is modeled using an Alternative Graph (AG)-based Mixed Integer Linear Programming (MILP) model.

In order to investigate the impacts of different parameter inputs on timetable flexibility, a case study is conducted on a part of the Dutch railway network. To investigate the influencing factors of timetable flexibility, one illustrative application and two sensitivity analyses are conducted. Based on the results, practical implications on train dispatchers and signalers, as well as on railway passengers are concluded.

The Dutch railway transport system is a system of systems and also a sociotechnical system that will migrate to a radio-based signalling standard ERTMS (European Rail Traffic Management System). ERTMS will influence the train drivers and dispatchers the most, especially due to the introduction of the signalling and control element of ERTMS: the European train control system (ETCS). A reliability requirement for the migration towards ERTMS obligates to demonstrate that the reliability of the system stays the same or improves. Reliability can be quantified if all the possible risks are known, but identifying risks with traditional models is insufficient, because they do not capture the complexities and dynamics of socio-technical systems.

The hazard analysis technique ‘systems theoretic process analysis’ (STPA) is a promising technique to sufficiently identify hazards that models the system in a control structure and searches systematically for hazards. The main research question of this thesis is: ‘To what extent can STPA be applied to identify risks and determine the system reliability of interactions between ETCS, train drivers and dispatchers?’ What are the risks caused by those interactions and how can STPA be applied for an effective risk assessment are the two research objectives.

STPA consists of 4 structured steps. First the analysed system is described and the purpose of the analysis is set. The system is modelled in a control structure in the second step. The third step of STPA is to identify unsafe control actions with guided words. 27 unsafe control actions are identified for the 8 control actions that are present in the control structure. The last step of STPA is to identify loss scenarios that could lead to the unsafe control actions, those were formulated with system experts.

The desk research and this research demonstrates that STPA is completer and more thorough in identifying hazards than the tradition hazard analysis technique ‘failure mode effect and criticality analysis’ (FMECA). In this research, STPA identified 70 loss scenarios in the analysed procedure (compared to 4 issues identified with FMECA), those hazards ranged from missing or inadequate feedback mechanisms to inconsistent process models of the train drivers or dispatchers. STPA identified besides technical failures also design flaws in the procedure and unsafe interaction between the ETCS, train drivers and dispatchers.

Besides the conclusion that STPA turned out to be more complete and thorough in identifying hazards, another advantages of STPA is that performing STPA is very structured and not superficial. An identified disadvantage of STPA is that the method stops immediately after the hazards are defined. Determining the probability of occurrence and the impact expressed in train delay minutes can result in prioritization of the hazards and a better risk assessment.

To conclude, this research recommends applying STPA for complex systems where multiple controllers are involved. An STPA expert, someone who has experience with applying STPA in different projects, is a key to successfully implement STPA in an organisation.

A heuristic method for the operation of shunting yards is created that considers the sorting process of wagons with dangerous goods. This concerns shunting yards where the wagons are distributed manually, and communication takes place by telephone. The first problem is that an entire train is considered dangerous if there is a wagon with dangerous goods on the accompanying wagon list. The wagon list shows where each wagon is positioned within a train composition. Due to safety regulations, a certain distance must be kept between wagons for certain dangerous goods classes. Because entire trains are considered dangerous, adjacent tracks must be left empty. This is at the expense of the capacity of the yard.

The second problem is that the wagon list is not 100% correct due to human errors. Random checks show that the wagon list does not always turn out to be correct and that mistakes are made in creating the list. Therefore, smart camera techniques have been developed to detect which wagon contains which dangerous goods on arrival to confirm the wagon list. This opens up new possibilities concerning the sorting strategy of wagons from the arrival track onto the classification tracks in accordance with their destination.

Research is done to find out what this new sorting strategy should look like. Therefore, the following research question is formulated: ‘What kind of method can distribute wagons, including wagons carrying dangerous goods, on a rail yard, considering safety?’. In order to answer the research question, qualitative research was carried out into the various existing strategies and methods that could be applied. A model is created in which wagons are sorted over classification tracks. The model chooses the track on which the wagons should be placed based on the destination. Specific attention is given to wagons with dangerous goods, for which the model ensures that a specific distance is kept from other wagons on the (neighboring) classification tracks. The model then determines the most appropriate departure time, and the train departs. Different scenarios are tested that the model has to deal with. A distinction is made here between whether the number of classification tracks is equal or not equal to the number of destinations, and whether the rate of dangerous goods over the destinations is equal or not. This makes a total of four scenarios that have been tested.

The effect of using a buffer on the track is examined so that a wagon carrying dangerous goods never stands next to another wagon carrying dangerous goods of a different class if they are not allowed to stand together. This is tested in the model, and the output showed the placement of the wagons and the processing times.

Railway networks are to play an increasingly large role in European transportation. This has boosted the urgency of railway innovations, of which the development of decision support systems for conflict resolution is an important aspect. This research contributes to this development by formulating a suitable mathematical approach for railway networks equipped with moving block signalling systems. Two dispatching actions to reschedule trains are applied, namely retiming and reordering. The designed approach is an extension to an existing method, based on graph theory, that is able to reschedule trains in case of conflict. The novel method uses additional node- and arc types in order to ensure moving block suitability. The new node type enables the possibility to create nodes that are related to trains, rather than infrastructure. The new arc type ensures a continuously safe time interval between two trains in the absence of trackside signals. An optimization problem, with the objective of minimizing the maximum propagated delay, is formulated. Hereafter, the performance is evaluated by a case study in the Rotterdam-The Hague corridor. According to the experimental results, the designed model is able to reduce delay propagation up to 50% for the majority of input situations within 10 seconds of computation time. Overall, the designed method shows promising results, but further research will be necessary to make it applicable in practice.

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.

The current research addresses a problem found in the area of railway operations regarding the maintenance of rolling stock units. It focuses on the situation in The Netherlands and approaches the problem from the perspective of its main railway operator N.V. Nederlandse Spoorwegen (NS).
The increasing use of the capacity of the railway network leads to two issues. First, the complexity of the scheduling process is increasing, raising the need for tools that automate this process. Second, since NS is considering to perform more maintenance activities during daytime, raising the question at which locations maintenance teams needs to be stationed to perform daytime maintenance. These issues are interrelated.
The model development in the current research, tackling the aforementioned issues, can be understood as a three-stage framework. Assuming a given rolling stock circulation, the first stage aims to find the maintenance schedule and maintenance location choice minimizing the total number of nighttime maintenance activities. The second stage introduces a model to compute the required capacity. The third stage integrates the first and second stage, aiming to find a solution to the first-stage model that satisfies some predetermined maintenance location capacity constraints that can be determined by the second-stage model.
First, it is shown that, for a scenario with 20 maintenance locations for daytime maintenance, up to 42.0% of the work can be performed during daytime. Also, the second model can be used to efficiently (i.e. within seconds) compute required capacity and an accurate maintenance activity planning. Moreover, results for the third model have been generated showing, for example, that in one considered problem instance, the number of maintenance shifts for which the required capacity exceeds the available capacity can be reduced from 21 to 5 in 7.6 minutes. In addition to the aforementioned experimental results, a more practical approach is taken as well by constructing a small use case, demonstrating how the current research can be applied in practical situations. To this end, planning software Viriato is used, by which various visualizations of maintenance schedules can be provided.

Seven disrupted scenarios have been simulated on the railway corridor Utrecht-Den Bosch, each over three signalling configurations to find the effect of in-cab signalling and reduced block section lengths on resilience. Furthermore, the place of resilience in decision making and design of railway signalling has been investigated. Although resilience is only one of the factors in the decision making and design of ETCS-projects, besides factors such as capacity, safety and interoperability, a quantification of resilience can help to either compare alternatives on all factors including resilience, to compare alternatives on resilience only, or to find the relation between design choices and resilience. Simulation of seven disrupted traffic scenarios has shown that using in-cab signalling with ETCS L2 compared to line-side signalling with NS'54/ATB has saved 10% of the delay in the scenarios on average. By using reduced block sections lengths in combination with in-cab signalling, 20% of the delay has been saved on average. For absolute resilience, short block sections should be placed along the whole track. As this is not realistic from cost perspective, decreased block sections are in each case advised nearby yards and switches.

Mass rapid transit plays an important role in providing a sustainable form of transport in densely populated areas. Public transport operators worldwide are experiencing an increase in popularity of their network and are experiencing ridership growth on their metro networks. To accommodate ridership growth, many transport operators are tasked with increasing the capacity of their metro network. While widening or elongating trains can be a too costly alternative to increase capacity, increasing frequencies can be interesting. However, the offered level of service might be jeopardised. Increasing frequencies might lead to higher delays and irregular services, resulting in a lower level of service than when lower frequencies are maintained. With the use of microscopic simulation tool OpenTrack, this research has given a quantitative analysis of the effects of increasing frequencies on timetable reliability. For a case study of the Rotterdam metro network, this research has developed four growth scenarios in which the frequencies are increased. It has found that a timetable with trunk section intervals up to 150 seconds still yield reliable services, however lower intervals yield more unreliable services. The implementation of moving block signaling can reduce delays of 40-50 percent, depending on the timetable. However, for irregularity, removing the driver from the front of the train, creating more buffer time at terminal stations, yields higher improvements than moving block does. This research has shown that moving block and metro automation can enable reliable services for timetables with very high frequencies. However, the operator is still faced with a choice to either strive for punctual trains, or to strive for regular trains.

In this thesis, the applicability of tram-train in an urban region is studied. A three-step top-down method is used, to narrow down the solution space and determine whether a tram-train solution can be applied, is suitable and technically feasible.

In this study an analytical model is created that estimates the capacity of a stabling yard with the implementation of 100% servicing. 100% servicing is a new concept developed by NS to increase the quality of trains by increasing the frequency of cleaning and maintenance of trains and improving planning of shunting movements at stabling yards by standardizing the service needs for al trains. It could potentially lead to more stabling capacity, of which a shortage exists in the Netherlands. The model is used in a case study for two stabling yards in the Netherlands: Zwolle and Eindhoven. The effect 100% servicing has on the stabling capacity appears strongly dependend on the layout characteristics of a specific stabling yard.

The introduction and obligation of tendering PT (concessions) in Europe has led to more focus on cost-efficiency and contracts. An additional effect in regional PT is the increase of undesirable boundaries between train and bus, e.g. in the form of non-aligned tariffs and bad interchange possibilities. The goal of a multimodal concession is to abolish these boundaries and establish an integrated PT system by tendering regional train and bus simultaneously. When the organisational part of integration is established this way, it becomes possible establish operational integration, which is defined as the alignment of the networks, schedules, information, tickets & tariffs, and Traffic Control of regional train and bus. Our analysis shows that internal coordination (in a multimodal concession), set against cases where integration is arranged by a contract or a partnership, indeed leads to more focus and better tuning of most of the five levels. In a particular case, on balance passengers are offered a better aligned PT system and the operator can operate more efficiently. However, not in each case a multimodal concession is most suitable and the risks for operators are large. Besides that, the implementation often is a difficult process. It is concluded that in cases it is possible to tender a multimodal concession, the pros often outweigh the cons.

Railway maintenance is currently planned according to a fixed schedule, mostly of one year. This is usually not optimal when considering the cost of maintenance, of possessions and of failure. However, an approach that minimizes the sum of these costs was not yet available. This paper presents a statistical analysis method and an optimization problem that aims to solve this problem. It is applied to fictitious problems and a realistic test case.

In a busy railway network such as the Netherlands, more and more maintenance activities are needed to be performed. These planned activities often lead to an infeasible timetable since infrastructure is temporary unavailable for operations. A macroscopic network model can roughly adjust the timetable for a complete network, while a microscopic model is needed to check for possessions in station areas and ensure feasibility of the timetable. In this thesis a microscopic routing model is proposed that adjusts the route plan in a station area while minimising passenger dissatisfaction. The model also finds new rolling stock connections, implements shunting movements in a station area and considers shorter rolling stock formations. To increase the quality of the route plan, its robustness is increased iteratively and small time shifts of arrivals and departures are applied to resolve small conflicts that would lead to cancellations. Several case studies demonstrate the high performance of the model. Finally, a feedback mechanism to a macroscopic model is proposed.