We present a novel exact journey planning approach to computing a reasonable subset of multi-criteria Pareto sets in public transit networks. Our restriction is well defined and independent of the choice of algorithm. In order to compute the restricted Pareto set efficiently, we present Bounded McRAPTOR, a new set of algorithms that extend the well-known McRAPTOR algorithm. The fastest variant employs a novel pruning scheme based on carefully computed bounds. Experiments on large metropolitan networks show that a four-criteria restricted Pareto set can be computed faster by a factor of up to 65, while retaining the important journeys of the full Pareto set. This easily enables interactive applications in practice, making multi-criteria Pareto-optimal journey planning scalable without the need of a preprocessing-based speedup technique.
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