  Thanks to the diffusion of the Internet of Things, nowadays it is possible to
sense human mobility almost in real time using unconventional methods (e.g.,
number of bikes in a bike station). Due to the diffusion of such technologies,
the last years have witnessed a significant growth of human mobility studies,
motivated by their importance in a wide range of applications, from traffic
management to public security and computational epidemiology. A mobility task
that is becoming prominent is crowd flow prediction, i.e., forecasting
aggregated incoming and outgoing flows in the locations of a geographic region.
Although several deep learning approaches have been proposed to solve this
problem, their usage is limited to specific types of spatial tessellations and
cannot provide sufficient explanations of their predictions. We propose
CrowdNet, a solution to crowd flow prediction based on graph convolutional
networks. Compared with state-of-the-art solutions, CrowdNet can be used with
regions of irregular shapes and provide meaningful explanations of the
predicted crowd flows. We conduct experiments on public data varying the
spatio-temporal granularity of crowd flows to show the superiority of our model
with respect to existing methods, and we investigate CrowdNet's reliability to
missing or noisy input data. Our model is a step forward in the design of
reliable deep learning models to predict and explain human displacements in
urban environments.
