Multi-Criteria Route Planning with Little Regret

Abstract

Multi-criteria route planning arises naturally in real-world navigation scenarios where users care about more than just one objective - such as minimizing travel time while also avoiding steep inclines or unpaved surfaces or toll routes. To capture the possible trade-offs between competing criteria, many algorithms compute the set of Pareto-optimal paths, which are paths that are not dominated by others with respect to the considered cost vectors. However, the number of Pareto-optimal paths can grow exponentially with the size of the input graph. This leads to significant computational overhead and results in large output sets that overwhelm users with too many alternatives. In this work, we present a technique based on the notion of regret minimization that efficiently filters the Pareto set during or after the search to a subset of specified size. Regret minimizing algorithms identify such a representative solution subset by considering how any possible user values any subset with respect to the objectives. We prove that regret-based filtering provides us with quality guarantees for the two main query types that are considered in the context of multi-criteria route planning, namely constrained shortest path queries and personalized path queries. Furthermore, we design a novel regret minimization algorithm that works for any number of criteria, is easy to implement and produces solutions with much smaller regret value than the most commonly used baseline algorithm. We carefully describe how to incorporate our regret minimization algorithm into existing route planning techniques to drastically reduce their running times and space consumption, while still returning paths that are close-to-optimal.