Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.

中文翻译：

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3DGCN：用于全市人群流量预测的 3 维动态图卷积网络

人流预测是一项重要任务，有利于交通系统和公共安全的广泛应用。然而，由于复杂的时空依赖性和城市结构对人群流动模式的复杂影响，这是一个具有挑战性的问题。在本文中，我们提出了一个新颖的框架，3-D宏大的G拉夫C卷积ñ网络（3DGCN），预测全市人群流量。我们首先将其建模为一个动态时空图预测问题，其中每个节点代表一个具有时变流的区域，每个边代表其对应区域之间的起点-终点（OD）流。因此，区域之间的 OD 流被视为区域之间空间相互作用的代表。为了解决复杂的时空依赖性，我们提出的 3DGCN 可以同时对图空间和时间邻居之间的相关性进行建模。为了在人群流量预测中学习和整合城市结构，我们设计了 GCN 聚合器，以同时从人群流量预测和区域功能推断中学习。在两个城市对真实世界数据集进行的广泛实验表明，我们的模型比最先进的基线高 9.6%~19。