In the upcoming years more train passengers are expected and with more trains on the tracks railway systems require greater resilience. Switches can play a major role during disruptions as they enable trains to be rerouted onto other tracks, allowing them to bypass the disruption but switches also facilitate overtaking, meet-pass operations at stations as well as diverging and merging at junctions. Since switches consist of numerous parts and the fact these are moving infrastructure elements they are subject to failure itself. Maintenance is expensive and ProRail, the Dutch infrastructure manager, only has a limited budget from the government and if certain switches are only used during disruptions it sounds logical to remove those switches. With on average 50 disruptions per day in the Netherlands with both small and huge impact, it is important to get insights into the relationship between resilience and the location of the switches.

In this thesis a model is constructed that evaluates the impact of a set of disruption scenarios on different switch configurations. Four key performance indicators are found in literature that can measure resilience quantitatively: costs (number of switches), rate of cancelled services, punctuality and time to recover. In interviews with rail experts, weightings for the four KPIs are derived which are used to calculate a score for all disruption scenarios and infrastructure layouts. A trade-off between costs and resilience is made in order to find the optimal switch configuration for a double track and four track layout which provides the highest capacity during disruptions. The set of disruptions including cause, location and duration come from an extensive analysis of disruption events of the past 6.5 years in the Netherlands.

Currently, NS is cancelling many trains since NS is examined on punctuality and not on the number of trains. This research aims to cancel as few trains as possible which is also examined in a case study where the model is validated. The Dutch railway line Utrecht Centraal – Arnhem Centraal appears to have a weak spot and by proposing new switch location configurations on a part of this line the score increased, despite the fact that more switches have been included in the proposed solution. Since the big renovation, 108 switches in Utrecht Centraal were removed by ProRail which improved punctuality, capacity and speed but decreased the flexibility: a disruption between Utrecht and Arnhem (or Eindhoven) currently has a lot of impact on the train service between Amsterdam and Utrecht, because short turning options in Utrecht are rare. This means that trains are now being cancelled completely or short turn already in Amsterdam. By using smart options with new rerouting strategies, capacity between Amsterdam and Utrecht can be kept high with the proposed solution during a disruption between Utrecht and Arnhem or Eindhoven.

This research aims to understand the influencing attributes of passengers and station layout elements on transfer walking times for metro transfers. Little research has been performed to include passenger-related attributes to model transfer walking time besides station layout elements. Through a literature review, the effect of gender, luggage size, group size and level of crowding are potential attributes. Furthermore, the vertical transport mode choice, including the lift, the waiting condition to board a vertical transport mode and the alighting location, are also part of the influential attributes besides the transfer length. The walking time and passenger characteristics have been collected through a covert observation. In the data analysis, the effect of group size, vertical transport mode choice, waiting condition to board and the alighting location significantly impact the transfer walking time and the walking time on a transfer segment level. These attributes have been captured in walking time and passing speed models for various transfer segment types. The walking time models can predict a lower, mean and upper bound of the walking time for each combination of attributes. The case study for the walking time collection was metro station Beurs, Rotterdam.