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.