We test the effect of a variety of feature sets representing passenger volumes, weather conditions and train interactions, when defined as features and used in a gradient boosting model to predict passenger train delays 20 minutes to the future from the last registration point. Effects of the features and their combinations on the prediction quality are analyzed and the best performing feature sets selected. The results showed that the passenger volumes features (in the form as defined in our work) do not have any prediction power and rather introduced noise in the predictions. The weather features resulted in reduced expected delay change with a slight positive effect on precision of the classification task while worsening the recall. The largest positive effect was observed when train interaction features were introduced despite their highly simplified form. Considering the low computational efforts necessary to retrieve the features, we conclude there is a potential for application of similarly defined train interactions features in other models.

The construction of cyclic railway timetables is an important task for Netherlands Railways (NS).This construction can be formulated as a Periodic Event Scheduling Problem (PESP). The most powerful technique for solving cyclic railway timetabling problems is constraint programming, especially via SAT solvers when PESP instances are encoded as SAT instances. SAT solvers can determine the feasibility of problem instances of NS quickly and reliably. However, in previous implementations the problem specification must explicitly indicate the track use within stations and on four-track sections. As a result, the solver also reports infeasibility if a small adjustment of the track allocation could lead to a feasible timetable. In this thesis, the Open Periodic Event Scheduling Problem (OPESP) is introduced, which is used in a new method to incorporate flexible track use in the SAT formulation. This method yields promising results that could help improve the timetabling process at NS.