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Short-term OD flow (i.e. the number of passenger traveling between stations)prediction is crucial to traffic management in metro systems. Due to thedelayed effect in latest complete OD flow collection, complex spatiotemporalcorrelations of OD flows in high dimension, it is more challengeable than othertraffic prediction tasks of time series. Existing methods need to be improveddue to not fully utilizing the real-time passenger mobility data and notsufficiently modeling the implicit correlation of the mobility patterns betweenstations. In this paper, we propose a Completion based Adaptive HeterogeneousGraph Convolution Spatiotemporal Predictor. The novelty is mainly reflected intwo aspects. The first is to model real-time mobility evolution by establishingthe implicit correlation between observed OD flows and the prediction target ODflows in high dimension based on a key data-driven insight: the destinationdistributions of the passengers departing from a station are correlated withother stations sharing similar attributes (e.g. geographical location, regionfunction). The second is to complete the latest incomplete OD flows byestimating the destination distribution of unfinished trips through consideringthe real-time mobility evolution and the time cost between stations, which isthe base of time series prediction and can improve the model's dynamicadaptability. Extensive experiments on two real world metro datasetsdemonstrate the superiority of our model over other competitors with thebiggest model performance improvement being nearly 4\%. In addition, the datacomplete framework we propose can be integrated into other models to improvetheir performance up to 2.1\%.

Completion and Augmentation based Spatiotemporal Deep Learning Approach  for Short-Term Metro Origin-Destination Matrix Prediction under Limited  Observable Data