  Passenger request prediction is essential for operations planning, control,
and management in ride-sharing platforms. While the demand prediction problem
has been studied extensively, the Origin-Destination (OD) flow prediction of
passengers has received less attention from the research community. This paper
develops a Graph Neural Network framework along with the Attention Mechanism to
predict the OD flow of passengers. The proposed framework exploits various
linear and non-linear dependencies that arise among requests originating from
different locations and captures the repetition pattern and the contextual data
of that place. Moreover, the optimal size of the grid cell that covers the road
network and preserves the complexity and accuracy of the model is determined.
Extensive simulations are conducted to examine the characteristics of our
proposed approach and its various components. The results show the superior
performance of our proposed model compared to the existing baselines.
