Personal Routes with High-Dimensional Costs and Dynamic Approximation Guarantees

In a personalized route planning query, a user can specify how relevant different criteria as travel time, gas consumption, scenicness, etc. are for his individual definition of an optimal route. Recently developed acceleration schemes for personalized route planning, which rely on preprocessing, achieve a significant speed-up over the Dijkstra baseline for a small number of criteria. But for more than five criteria, either the preprocessing becomes too complicated or the query answering is slow. In this paper, we first present a new LP-based preprocessing technique which allows to deal with many criteria efficiently. In addition, we show how to further reduce query times for all known personalized route planning acceleration schemes by considering approximate queries. We design a data structure which allows not only to have personalized costs but also individual approximation guarantees per query, allowing to trade solution quality against query time at the user's discretion. This data structure is the first to enable a speed-up of more than 100 for ten criteria while accepting only 0.01% increased costs.