Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder

Origin-destination (OD) crowd flow, if more accurately inferred at a fine-grained level, can potentially enhance the efficacy of various urban applications. In practice, for mining OD crowd flow with effect, the problem of spatially interpolating OD crowd flow occurs because of the ineluctable missing values. This problem is further complicated by the inherent scarcity and noise nature of OD crowd flow data. In this paper, we propose an uncertainty-aware interpolative and explainable framework, namely UApex, for realizing reliable and trustworthy OD crowd flow interpolation. Specifically, we first design a Variational Multi-modal Recurrent Graph Auto-Encoder (VMR-GAE) for uncertainty-aware OD crowd flow interpolation. A key idea here is to formulate the problem as semi-supervised learning on directed graphs. Next, to mitigate the data scarcity, we incorporate a distribution alignment mechanism to introduce supplementary modals into variational inference. Then, a dedicated decoder with a Poisson prior is proposed for the task. Moreover, to make VMR-GAE more trustworthy, we develop an efficient and uncertainty-aware explainer that can explain spatiotemporal topology via the Shapley value. Extensive experiments on two real-world datasets validate that VMR-GAE outperforms the state-of-the-art baselines. Also, an exploratory empirical study shows that the proposed explainer can generate meaningful spatiotemporal explanations.