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.

翻译：交通流预测是时空分析中一个具有挑战性却至关重要的问题，近年来受到越来越多的关注。除了时空相关性外，城市区域的功能性也在交通流预测中起着关键作用。然而，现有对区域功能属性的探索主要侧重于添加额外的拓扑结构，忽略了功能属性对区域交通模式的影响。不同于现有研究，我们提出了一种名为POI-MetaBlock的新型模块，该模块利用各区域的功能性（以兴趣点分布表示）作为元数据，进一步挖掘不同功能区域中的交通特征差异。具体而言，所提出的POI-MetaBlock采用自注意力架构，并融入POI与时间信息，为每个区域生成动态注意力参数，使模型能够拟合不同区域在不同时间段的多样交通模式。此外，我们的轻量级POI-MetaBlock可轻松集成到传统交通流预测模型中。大量实验表明，该模块显著提升了交通流预测性能，并超越了使用元数据的最先进方法。