  Shared mobility services require accurate demand models for effective service
planning. On the one hand, modeling the full probability distribution of demand
is advantageous because the entire uncertainty structure preserves valuable
information for decision-making. On the other hand, demand is often observed
through the usage of the service itself, so that the observations are censored,
as they are inherently limited by available supply. Since the 1980s, various
works on Censored Quantile Regression models have performed well under such
conditions. Further, in the last two decades, several papers have proposed to
implement these models flexibly through Neural Networks. However, the models in
current works estimate the quantiles individually, thus incurring a
computational overhead and ignoring valuable relationships between the
quantiles. We address this gap by extending current Censored Quantile
Regression models to learn multiple quantiles at once and apply these to
synthetic baseline datasets and datasets from two shared mobility providers in
the Copenhagen metropolitan area in Denmark. The results show that our extended
models yield fewer quantile crossings and less computational overhead without
compromising model performance.
