  Transport demand is highly dependent on supply, especially for shared
transport services where availability is often limited. As observed demand
cannot be higher than available supply, historical transport data typically
represents a biased, or censored, version of the true underlying demand
pattern. Without explicitly accounting for this inherent distinction,
predictive models of demand would necessarily represent a biased version of
true demand, thus less effectively predicting the needs of service users. To
counter this problem, we propose a general method for censorship-aware demand
modeling, for which we devise a censored likelihood function. We apply this
method to the task of shared mobility demand prediction by incorporating the
censored likelihood within a Gaussian Process model, which can flexibly
approximate arbitrary functional forms. Experiments on artificial and
real-world datasets show how taking into account the limiting effect of supply
on demand is essential in the process of obtaining an unbiased predictive model
of user demand behavior.

Machine Learning;Machine Learning