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.

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 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.

In the 2020 timetable the Dutch legacy signalling and automatic train protection systems (ATB/NS’54) are operating at maximum capacity in the busiest parts of the network. With future expansions of services in mind, the capacity provided by the legacy systems could be considered insufficient. The planned implementation of the ERTMS/ETCS Level 2 system might still not provide sufficient capacity in these locations. This thesis investigates how the technological developments of the Hybrid Level 3 and ATO (GoA2) systems could contribute to the capacity of the railway network with the objective to provide a solution for the busiest part of the Dutch railway network. The capacity effects of these systems are analysed through a simulation case study on the SAAL corridor on the Dutch railway network, using a variant of the 2030 timetable. Through timetable compressions, the capacity that the different systems (ATB/NS’54, ERTMS/ETCS Level 2, Hybrid Level 3 and ATO) provide in different configurations. A further comparison between the human driver behaviour and automatic systems is made to determine where possible capacity benefits could be gained from. The thesis was partly used as an opportunity to further develop capacity modelling methods for ERTMS/ETCS and ATO based on the constraints provided by the systems and modelling software. The timetable compressions provided an overview of each separate system and configuration step and their contribution to the capacity. The results of the timetable compressions indicated that the ERTMS/ETCS Hybrid Level 3 and ATO systems provided 4 main steps for increasing capacity. 1. Improving braking behaviour through the use of ERTMS/ETCS braking curves instead of the legacy system 2. The use of shorter block sections through the ERTMS/ETCS Hybrid Level 3 system 3. An optimisation of driving/braking behaviour through the ATO system 4. The possibility to limit headway buffer times due to homogenisation of train movements brought by the ATO Sizable variations in the timetable compressions on the different part of the corridor caused by variations in the service pattern and infrastructure configuration indicated that the effectiveness of these systems will vary based on the location. This shows the importance for infrastructure managers of investigating these systems on a case by case basis when using them to increase capacity on their respective networks. The results from the case study indicate that the ERTMS/ETCS Hybrid Level 3 and ATO systems could provide a possible alternative for costly infrastructure expansion projects to increase capacity on the Dutch railway network. Trade-offs will need to be made between capacity, costs, and robustness to determine the optimal system configuration.

The increasing passenger demand for rail service in the Netherlands, urges the Dutch infrastructure manager (IM) ProRail to increase network capacity. Instead of building new infrastructure, ProRail promotes information and communications technology solutions which aim for a more efficient utilization of the existing infrastructure. One of the ways to achieve improved capacity utilization is through decreasing the variability of train runs, i.e. by attaining more uniform train driving profiles. A driver advisory system (DAS) constitutes such a solution and it serves as a support for the driver to perform the train driving tasks. Additionally, the rollout of the European Train Control System Level 2 (ETCS L2) on the first part of the mainline Dutch network is planned for 2030. Developing a DAS compatible with ETCS L2 operation would yield high quality advice. Still, until the complete roll out of ETCS L2, ProRail aims to improve capacity on given bottlenecks using existing systems. A DAS constitutes a nonsafety critical, Grade of Automation 1 system. Under DAS operation, the driver adjusts the train controls and he/she is responsible for the safety of operations. A frequently arising problem when using a DAS, is that its advice usually leads to conflicts due to poor or no consideration of the actual traffic. An approach to handle this, is to provide the speed profile calculation module of a DAS with a dynamic speed profile that considers static and temporary speed restrictions as well as speed restrictions originating from the signalling system. The latter approach increases a DAS’s awareness regarding the actual signal state. This study aims to tackle conflicts with the latter approach. This approach for coping with conflicts when using a DAS is mentioned by several publications or commercial DASs but none of them explicitly defines how this is achieved. The main aim of this study is to increase the awareness of a conceptual CDASOn board regarding the actual signal state in order to minimise conflicts in disturbed operations. The proposed framework addresses the a CDASOn board operating on top of the Dutch signalling system NS’54 and the ClassB automatic train protection system ATBEG. In order to increase the proposed model’s effectiveness, this information must be provided in real time. Realtime signalling information is delivered on board by the maximum allowed speed data stream (Figure 1). It is proven that the only missing function from existing ProRail systems to provide this realtime information flow, is to determine the red signal in real time. Thus, the initial objective of this study can be scoped down to determining the red signal. It is also proven that the goal of determining the red signal is equivalent to the goal of locating the predecessor train. This information is planned to be fed to the on board equipment of a DAS via a novel data stream. Additionally, this section explained how this data stream fits to the train control architecture using a CDASOn board.