  The prediction of traffic flow is a challenging yet crucial problem in
spatial-temporal analysis, which has recently gained increasing interest. In
addition to spatial-temporal correlations, the functionality of urban areas
also plays a crucial role in traffic flow prediction. However, the exploration
of regional functional attributes mainly focuses on adding additional
topological structures, ignoring the influence of functional attributes on
regional traffic patterns. Different from the existing works, we propose a
novel module named POI-MetaBlock, which utilizes the functionality of each
region (represented by Point of Interest distribution) as metadata to further
mine different traffic characteristics in areas with different functions.
Specifically, the proposed POI-MetaBlock employs a self-attention architecture
and incorporates POI and time information to generate dynamic attention
parameters for each region, which enables the model to fit different traffic
patterns of various areas at different times. Furthermore, our lightweight
POI-MetaBlock can be easily integrated into conventional traffic flow
prediction models. Extensive experiments demonstrate that our module
significantly improves the performance of traffic flow prediction and
outperforms state-of-the-art methods that use metadata.

Computer Vision and Pattern Recognition;Artificial Intelligence