  We study the journey planning problem for multimodal networks consisting of
public transit and a non-schedule-based transfer mode (e.g., walking, bicycle,
e-scooter). So far, all efficient algorithms for this problem either restrict
usage of the transfer mode or Pareto-optimize only two criteria: arrival time
and the number of used public transit trips. However, we show that both
limitations must be lifted in order to obtain high-quality solutions. In
particular, the time spent using the (unrestricted) transfer mode must be
optimized as a third criterion. We present McTB, the first algorithm that
optimizes three criteria efficiently by avoiding costly data structures for
maintaining Pareto sets. To enable unlimited transfers, we combine it with a
three-criteria extension of the ULTRA preprocessing technique. Furthermore,
since full Pareto sets become impractically large for more than two criteria,
we adapt an approach by Delling et al. to restrict the Pareto set in a
methodical manner. Extensive experiments on real-world data show that our
algorithms are fast enough for interactive queries even on large country-sized
networks. Compared to the state of the art for multicriteria multimodal journey
planning, MCR, we achieve a speedup of up to 80.
