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Partitioned equivalence test: reduction of the validation effort by using artificial intelligence to pre-classify the quality of automatic passenger counts

Year: 2021
Type: preprint
Abstract: 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. While the pre-classification itsself can be arbitrary, we evaluated multiple use cases: entirely manual and algorithmic resp. 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.
Authors: David Ellenberger, Michael Siebert
Language: English
Open Access status: green
OpenAlex ID: W3153876812
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