  Automatic passenger counting (APC) in public transport has been introduced in
the 1970s and has been rapidly emerging in recent years. APC systems, like all
other measurement devices, are susceptible to error, which is treated as random
noise and is required to not exceed certain bounds. The demand for very low
errors is especially fueld by applications like revenue sharing, which is in
the billions, annually. As a result, both the requirements as well as the costs
heavily increased. In this work, we address the latter problem and present a
solution to increase the efficiency of initial or recurrent (e.g. yearly or
more frequent) APC validation. Our new approach, the partitioned equivalence
test, is an extension to this widely used statistic hypothesis test and
guarantees the same bounded, low user risk while reducing effort. This can be
used to either cut costs or to extend validation without cost increase. It
involves a pre-classification step, which itsself can be arbitrary, so we
evaluated several use cases: entirely manual and algorithmic, artificial
intelligence assisted workflows. For former, by restructuring the evaluation of
manual counts, our new statistical test can be used as a drop-in replacement
for existing test procedures. The largest savings, however, result from latter
algorithmic use cases: Due to the user risk being as bounded as in the original
equivalence test, no additional requirements are introduced. Algorithms are
allowed to be failable and thus, our test does not require the availability of
general artificial intelligence. All in all, automatic passenger counting as
well as the equivalence test itself can both benefit from our new extension.
