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.

翻译：出租车服务需求与供给的预测对于提升用户体验和服务商利润至关重要。近年来，图神经网络（GNN）在此类应用中展现出巨大潜力。该方法将城市区域建模为交通图中的节点，将区域间关系建模为边。GNN利用局部节点特征和图结构进行预测。然而，通过两条主要途径仍可实现更高效的预测：扩大交通图的规模，以及同时利用图中不同类型的节点和边。但这两种方法均面临GNN可扩展性的挑战。解决可扩展性问题的直接方案是分散化GNN操作，但这会导致节点间通信量剧增。本文首先刻画了分散化GNN方法中过度通信需求的特征。随后，我们提出了一种半分散化方法，利用多个可与蜂窝基站集成的中等规模存储与计算设备——云盒。该方法通过最小化云盒间通信，减轻了分散化方法的通信开销，同时通过云盒级分散化提升了可扩展性。此外，我们提出了一种异构GNN-LSTM算法，用于处理以出租车为节点的动态交通图，从而改进出租车级需求与供给预测。基于真实数据的广泛实验表明，在我们的异构GNN-LSTM算法上测试的半分散化方法具有优势。同时，与集中式和分散式推理方案相比，所提出的半分散化GNN方法将整体推理时间降低了约一个数量级。