  In recent years, ride-hailing services have been increasingly prevalent as
they provide huge convenience for passengers. As a fundamental problem, the
timely prediction of passenger demands in different regions is vital for
effective traffic flow control and route planning. As both spatial and temporal
patterns are indispensable passenger demand prediction, relevant research has
evolved from pure time series to graph-structured data for modeling historical
passenger demand data, where a snapshot graph is constructed for each time slot
by connecting region nodes via different relational edges (e.g.,
origin-destination relationship, geographical distance, etc.). Consequently,
the spatiotemporal passenger demand records naturally carry dynamic patterns in
the constructed graphs, where the edges also encode important information about
the directions and volume (i.e., weights) of passenger demands between two
connected regions. However, existing graph-based solutions fail to
simultaneously consider those three crucial aspects of dynamic, directed, and
weighted (DDW) graphs, leading to limited expressiveness when learning graph
representations for passenger demand prediction. Therefore, we propose a novel
spatiotemporal graph attention network, namely Gallat (Graph prediction with
all attention) as a solution. In Gallat, by comprehensively incorporating those
three intrinsic properties of DDW graphs, we build three attention layers to
fully capture the spatiotemporal dependencies among different regions across
all historical time slots. Moreover, the model employs a subtask to conduct
pretraining so that it can obtain accurate results more quickly. We evaluate
the proposed model on real-world datasets, and our experimental results
demonstrate that Gallat outperforms the state-of-the-art approaches.
