  Short-term OD flow (i.e. the number of passenger traveling between stations)
prediction is crucial to traffic management in metro systems. Due to the
delayed effect in latest complete OD flow collection, complex spatiotemporal
correlations of OD flows in high dimension, it is more challengeable than other
traffic prediction tasks of time series. Existing methods need to be improved
due to not fully utilizing the real-time passenger mobility data and not
sufficiently modeling the implicit correlation of the mobility patterns between
stations. In this paper, we propose a Completion based Adaptive Heterogeneous
Graph Convolution Spatiotemporal Predictor. The novelty is mainly reflected in
two aspects. The first is to model real-time mobility evolution by establishing
the implicit correlation between observed OD flows and the prediction target OD
flows in high dimension based on a key data-driven insight: the destination
distributions of the passengers departing from a station are correlated with
other stations sharing similar attributes (e.g. geographical location, region
function). The second is to complete the latest incomplete OD flows by
estimating the destination distribution of unfinished trips through considering
the real-time mobility evolution and the time cost between stations, which is
the base of time series prediction and can improve the model's dynamic
adaptability. Extensive experiments on two real world metro datasets
demonstrate the superiority of our model over other competitors with the
biggest model performance improvement being nearly 4\%. In addition, the data
complete framework we propose can be integrated into other models to improve
their performance up to 2.1\%.
