  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.
