Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Understand

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.