Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction

Large-scale commuting flow prediction is an essential task to estimate the commuting origin-destination (OD) demand within within a prefecture or the whole nation using multiple auxiliary data. Considering ranked structures of metropolitan areas and increased number of geographical units that need to be maintained, we develop a heterogeneous graph-based model to generate meaningful region embeddings at multiple spatial resolutions for predicting different types of inter-level OD flows. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted using real-world aggregated mobile phone datasets collected from Shizuoka Prefecture, Japan. The results indicate that our proposed model outperforms existing models in terms of a uniform urban structure. We extend the understanding of predicted results using reasonable explanations to enhance the credibility of the model.