Personalized route planning in road networks

Abstract

Computing shortest paths in road networks with millions of nodes and edges is challenging on its own. In the last few years, several preprocessing-based acceleration techniques have been developed to enable query answering orders of magnitudes faster than a plain Dijkstra computation. But most of these techniques work only if the metric which determines the optimal path is static or rarely changes. In contrast to that, we aim at answering personalized route planning queries. Here, every single query comes with a specification of its very own metric. This increases the combinatorial complexity of the problem significantly. We develop new preprocessing schemes that allow for real-time personalized route planning in huge road networks while keeping the memory footprint of the preprocessed data and subsequent queries small.