Nearest-Neighbor Queries in Customizable Contraction Hierarchies and Applications

TL;DR

This paper introduces a new algorithm for k-nearest neighbor searches in customizable contraction hierarchies, significantly reducing preprocessing effort and enabling fast, practical applications like point-of-interest queries and travel demand generation.

Contribution

The paper presents a novel separator-based algorithm for k-nearest neighbors in customizable contraction hierarchies, improving efficiency over previous methods.

Findings

Query times of about 25 milliseconds for point-of-interest searches.

Travel demand generation accelerated by a factor of over 50.

Reduced target-dependent preprocessing effort.

Abstract

Customizable contraction hierarchies are one of the most popular route planning frameworks in practice, due to their simplicity and versatility. In this work, we present a novel algorithm for finding k-nearest neighbors in customizable contraction hierarchies by systematically exploring the associated separator decomposition tree. Compared to previous bucket-based approaches, our algorithm requires much less target-dependent preprocessing effort. Moreover, we use our novel approach in two concrete applications. The first application are online k-closest point-of-interest queries, where the points of interest are only revealed at query time. We achieve query times of about 25 milliseconds on a continental road network, which is fast enough for interactive systems. The second application is travel demand generation. We show how to accelerate a recently introduced travel demand generator by a factor of more than 50 using our novel nearest-neighbor algorithm.