Filip Stavrov and Luka Stopar

ABSTRACT
This paper solves the problem of estimating the number and
type of required resources for pickup and delivery of
passengers at some time in the future. By combining
optimization and sampling methods, as well as making plans
based on several statistical samples, we estimate the real
values for the required resources and show how the sample
values converge towards the real values. Our approach
combines machine-learning based demand predictions, for
the number of passengers, and a route optimization engine
that assigns the passengers into shared shuttle vehicles. We
test our approach using a baseline data, and we obtain
statistically significant results.