  We study the problem of computing all Pareto-optimal journeys in a public
transit network regarding the two criteria of arrival time and number of
transfers taken. In recent years, great advances have been made in making
public transit network routing more scalable to larger networks. However, most
approaches are silent on scalability in another dimension: Time. Experimental
evaluations are often done on slices of timetables spanning a couple of days,
when in reality, the planning horizon is much longer. We introduce an extension
to trip-based public transit routing, proposed in [12], that allows efficient
handling of arbitrarily long timetables. Our experimental evaluation shows that
the resulting algorithm achieves fast queries on year-spanning timetables, and
can incorporate updates such as delays or changed routes quickly even on large
networks.
