  We study a multimodal journey planning scenario consisting of a public
transit network and a transfer graph which represents a secondary
transportation mode (e.g., walking, cycling, e-scooter). The objective is to
compute Pareto-optimal journeys with respect to arrival time and the number of
used public transit trips. While various existing algorithms can efficiently
compute optimal journeys in either a pure public transit network or a pure
transfer graph, combining the two increases running times significantly.
Existing approaches therefore typically only support limited walking between
stops, either by imposing a maximum transfer distance or by requiring the
transfer graph to be transitively closed. To overcome these shortcomings, we
propose a novel preprocessing technique called ULTRA (UnLimited TRAnsfers):
Given an unlimited transfer graph, which may represent any non-schedule-based
transportation mode, ULTRA computes a small number of transfer shortcuts that
are provably sufficient for computing a Pareto set of optimal journeys. These
transfer shortcuts can be integrated into a variety of state-of-the-art public
transit algorithms, establishing the ULTRA-Query algorithm family. Our
extensive experimental evaluation shows that ULTRA improves these algorithms
from limited to unlimited transfers without sacrificing query speed. This is
true not just for walking, but also for faster transfer modes such as bicycle
or car. Compared to the state of the art for multimodal journey planning, the
fastest ULTRA-based algorithm achieves a speedup of an order of magnitude.
