  Given a public transportation network of stations and connections, we want to
find a minimum subset of stations such that each connection runs through a
selected station. Although this problem is NP-hard in general, real-world
instances are regularly solved almost completely by a set of simple reduction
rules. To explain this behavior, we view transportation networks as hitting set
instances and identify two characteristic properties, locality and
heterogeneity. We then devise a randomized model to generate hitting set
instances with adjustable properties. While the heterogeneity does influence
the effectiveness of the reduction rules, the generated instances show that
locality is the significant factor. Beyond that, we prove that the
effectiveness of the reduction rules is independent of the underlying graph
structure. Finally, we show that high locality is also prevalent in instances
from other domains, facilitating a fast computation of minimum hitting sets.
