Urban regional function guided traffic flow prediction
Urban regional function guided traffic flow prediction
复制标题
DOI：
10.1016/j.ins.2023.03.109
复制
发表时间：
2023-03-23
影响因子：
8.1
通讯作者：
Lin, Liang
中科院分区：
文献类型：
--
作者：
Wang, Kuo;Liu, LingBo;Lin, Liang
交通流预测是时空分析中一个具有挑战性但又至关重要的问题，最近引起了越来越多的关注。除了时空相关性之外，城市地区的功能在交通流预测中也起着至关重要的作用。然而，区域功能属性的探索主要集中于添加额外的拓扑结构，忽视了功能属性对区域交通格局的影响。与现有的工作不同，我们提出了一种名为 POI-MetaBlock 的新颖模块，该模块利用每个区域的功能（以兴趣点分布表示）作为元数据，以进一步挖掘不同功能区域中的不同流量特征。具体来说，所提出的 POI-MetaBlock 采用自注意力架构，并结合 POI 和时间信息为每个区域生成动态注意力参数，这使得模型能够适应不同区域在不同时间的不同流量模式。此外，我们的轻量级 POI-MetaBlock 可以轻松集成到传统的交通流预测模型中。大量的实验表明，我们的模块显着提高了交通流预测的性能，并且优于使用元数据的最先进的方法。
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.