  Shared mobility services require accurate demand models for effective service
planning. On one hand, modeling the full probability distribution of demand is
advantageous, because the full uncertainty structure preserves valuable
information for decision making. On the other hand, demand is often observed
through 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 shown them to perform well under
such conditions, and in the last two decades, several works have proposed to
implement them flexibly through Neural Networks (CQRNN). However, apparently no
works have yet applied CQRNN in the Transport domain. We address this gap by
applying CQRNN to datasets from two shared mobility providers in the Copenhagen
metropolitan area in Denmark, as well as common synthetic baseline datasets.
The results show that CQRNN can estimate the intended distributions better than
both censorship-unaware models and parametric censored models.
