  Urban flow forecasting is a challenging task, given the inherent periodic
characteristics of urban flow patterns. To capture the periodicity, existing
urban flow prediction approaches are often designed with closeness, period, and
trend components extracted from the urban flow sequence. However, these three
components are often considered separately in the prediction model. These
components have not been fully explored together and simultaneously
incorporated in urban flow forecasting models. We introduce a novel urban flow
forecasting architecture, TERMCast. A Transformer based long-term relation
prediction module is explicitly designed to discover the periodicity and enable
the three components to be jointly modeled This module predicts the periodic
relation which is then used to yield the predicted urban flow tensor. To
measure the consistency of the predicted periodic relation vector and the
relation vector inferred from the predicted urban flow tensor, we propose a
consistency module. A consistency loss is introduced in the training process to
further improve the prediction performance. Through extensive experiments on
three real-world datasets, we demonstrate that TERMCast outperforms multiple
state-of-the-art methods. The effectiveness of each module in TERMCast has also
been investigated.
