Generation of classes of robust periodic railway timetables

In this paper we discuss the problem of randomly sampling classes of fixed-interval railway timetables from a so-called timetable structure. Using a standard model for the timetable structure, we introduce a natural partitioning of the set of feasible timetables into classes. We then define a new probability distribution where the probability of each class depends on the robustness of the timetables in that class. Due to the difficulty of sampling directly from this distribution, we propose a heuristic sampling method and illustrate using practical data that our method indeed favors classes containing robust timetables over others.