With a smartphone, users nowadays can plan public transport journeys spontaneously and react to incidents in real-time by changing itineraries on the fly, even proactively. Algorithms and UIs however are still clinging to the notion of an upfront query and journey plan that the user is blindly following as long as is physically possible.

Similar to turn-by-turn directions for car drivers, we propose to focus on showing the user only the next best step they should take according to the real-time situation in order to eventually get to their destination, and not an entire, fixed journey plan. Instead of just the destination arrival time, we compute the probability distribution of destination arrival of the user, taking into account reliability of transfers and alternative continuations. Simulations show that on average, a user will arrive earlier than when following a classical journey planner, not only in the case of delays.

A prototype can be used at https://tespace.traines.eu/ with source code available at https://github.com/traines-source/time-space-train-planner. TeSpace relies on https://transitous.org/ and the https://github.com/motis-project/motis API for global public transport timetable coverage (where available). Let's also talk about how to advance these two beyond classical pareto-optimal journeys!