  Automatic passenger counting (APC) in public transportation has been
approached with various machine learning and artificial intelligence methods
since its introduction in the 1970s. While equivalence testing is becoming more
popular than difference detection (Student's t-test), the former is much more
difficult to pass to ensure low user risk. On the other hand, recent
developments in artificial intelligence have led to algorithms that promise
much higher counting quality (lower bias). However, gradient-based methods
(including Deep Learning) have one limitation: they typically run into local
optima. In this work, we explore and exploit various aspects of machine
learning to increase reliability, performance, and counting quality. We perform
a grid search with several fundamental parameters: the selection and size of
the training set, which is similar to cross-validation, and the initial network
weights and randomness during the training process. Using this experiment, we
show how aggregation techniques such as ensemble quantiles can reduce bias, and
we give an idea of the overall spread of the results. We utilize the test
success chance, a simulative metric based on the empirical distribution. We
also employ a post-training Monte Carlo quantization approach and introduce
cumulative summation to turn counting into a stationary method and allow
unbounded counts.
