  Bike Sharing Systems (BSSs) are emerging as an innovative transportation
service. Ensuring the proper functioning of a BSS is crucial given that these
systems are committed to eradicating many of the current global concerns, by
promoting environmental and economic sustainability and contributing to
improving the life quality of the population. Good knowledge of users'
transition patterns is a decisive contribution to the quality and operability
of the service. The analogous and unbalanced users' transition patterns cause
these systems to suffer from bicycle imbalance, leading to a drastic customer
loss in the long term. Strategies for bicycle rebalancing become important to
tackle this problem and for this, bicycle traffic prediction is essential, as
it allows to operate more efficiently and to react in advance. In this work, we
propose a bicycle trips predictor based on Graph Neural Network embeddings,
taking into consideration station groupings, meteorology conditions,
geographical distances, and trip patterns. We evaluated our approach in the New
York City BSS (CitiBike) data and compared it with four baselines, including
the non-clustered approach. To address our problem's specificities, we
developed the Adaptive Transition Constraint Clustering Plus (AdaTC+)
algorithm, eliminating shortcomings of previous work. Our experiments evidence
the clustering pertinence (88% accuracy compared with 83% without clustering)
and which clustering technique best suits this problem. Accuracy on the Link
Prediction task is always higher for AdaTC+ than benchmark clustering methods
when the stations are the same, while not degrading performance when the
network is upgraded, in a mismatch with the trained model.
