Multi-View TRGRU: Transformer based Spatiotemporal Model for Short-Term
  Metro Origin-Destination Matrix Prediction
  Accurate prediction of short-term OD Matrix (i.e. the distribution of
passenger flows from various origins to destinations) is a crucial task in
metro systems. It is highly challenging due to the constantly changing nature
of many impacting factors and the real-time delayed data collection problem.
Recently, some deep learning-based models have been proposed for OD Matrix
forecasting in ride-hailing and high way traffic scenarios. However, these
models can not sufficiently capture the complex spatiotemporal correlation
between stations in metro networks due to their different prior knowledge and
contextual settings. In this paper we propose a hybrid framework Multi-view
TRGRU to address OD metro matrix prediction. In particular, it uses three
modules to model three flow change patterns: recent trend, daily trend, weekly
trend. In each module, a multi-view representation based on embedding for each
station is constructed and fed into a transformer based gated recurrent
structure so as to capture the dynamic spatial dependency in OD flows of
different stations by a global self-attention mechanism. Extensive experiments
on three large-scale, real-world metro datasets demonstrate the superiority of
our Multi-view TRGRU over other competitors.
