  For bike sharing systems, demand prediction is crucial to ensure the timely
re-balancing of available bikes according to predicted demand. Existing methods
for bike sharing demand prediction are mostly based on its own historical
demand variation, essentially regarding it as a closed system and neglecting
the interaction between different transportation modes. This is particularly
important for bike sharing because it is often used to complement travel
through other modes (e.g., public transit). Despite some recent progress, no
existing method is capable of leveraging spatiotemporal information from
multiple modes and explicitly considers the distribution discrepancy between
them, which can easily lead to negative transfer. To address these challenges,
this study proposes a domain-adversarial multi-relational graph neural network
(DA-MRGNN) for bike sharing demand prediction with multimodal historical data
as input. A temporal adversarial adaptation network is introduced to extract
shareable features from demand patterns of different modes. To capture
correlations between spatial units across modes, we adapt a multi-relational
graph neural network (MRGNN) considering both cross-mode similarity and
difference. In addition, an explainable GNN technique is developed to
understand how our proposed model makes predictions. Extensive experiments are
conducted using real-world bike sharing, subway and ride-hailing data from New
York City. The results demonstrate the superior performance of our proposed
approach compared to existing methods and the effectiveness of different model
components.
