Prediction of taxi service demand and supply is essential for improving
customer's experience and provider's profit. Recently, graph neural networks
(GNNs) have been shown promising for this application. This approach models
city regions as nodes in a transportation graph and their relations as edges.
GNNs utilize local node features and the graph structure in the prediction.
However, more efficient forecasting can still be achieved by following two main
routes; enlarging the scale of the transportation graph, and simultaneously
exploiting different types of nodes and edges in the graphs. However, both
approaches are challenged by the scalability of GNNs. An immediate remedy to
the scalability challenge is to decentralize the GNN operation. However, this
creates excessive node-to-node communication. In this paper, we first
characterize the excessive communication needs for the decentralized GNN
approach. Then, we propose a semi-decentralized approach utilizing multiple
cloudlets, moderately sized storage and computation devices, that can be
integrated with the cellular base stations. This approach minimizes
inter-cloudlet communication thereby alleviating the communication overhead of
the decentralized approach while promoting scalability due to cloudlet-level
decentralization. Also, we propose a heterogeneous GNN-LSTM algorithm for
improved taxi-level demand and supply forecasting for handling dynamic taxi
graphs where nodes are taxis. Extensive experiments over real data show the
advantage of the semi-decentralized approach as tested over our heterogeneous
GNN-LSTM algorithm. Also, the proposed semi-decentralized GNN approach is shown
to reduce the overall inference time by about an order of magnitude compared to
centralized and decentralized inference schemes.Comment: 13 pages, 10 figures, LaTeX; typos corrected, references added,
mathematical analysis adde