Crowd flow prediction is a vital problem for an intelligent transportation system construction in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it has raised great attention from many researchers. However, predicting crowd flows timely and accurately is a challenging task that is affected by many complex factors such as the dependencies of adjacent regions or recent crowd flows. Existing models mainly focus on capturing such dependencies in spatial or temporal domains and fail to model relations between crowd flows of distant regions. We notice that each region has a relatively fixed daily flow and some regions (even very far away from each other) may share similar flow patterns which show strong correlations among them. In this article, we propose a novel model named Double-Encoder which follows a general encoder–decoder framework for multi-step citywide crowd flow prediction. The model consists of two encoder modules named ST-Encoder and FR-Encoder to model spatial-temporal dependencies and daily flow correlations, respectively. We conduct extensive experiments on two real-world datasets to evaluate the performance of the proposed model and show that our model consistently outperforms state-of-the-art methods.

中文翻译：

---

联合建模时空相关性和每日流量相关性以进行人群流量预测

人流预测是智慧城市智能交通系统建设的重要问题。它在交通管理和行为分析中起着至关重要的作用，因此引起了许多研究人员的高度关注。然而，及时准确地预测人群流动是一项具有挑战性的任务，它受到许多复杂因素的影响，例如相邻区域的依赖性或最近的人群流动。现有模型主要侧重于捕捉空间或时间域中的这种依赖关系，而无法模拟遥远地区人群流动之间的关系。我们注意到每个区域都有相对固定的每日流量，并且一些区域（甚至彼此相距很远）可能共享相似的流量模式，这些模式之间显示出很强的相关性。在本文中，我们提出了一种名为双编码器的新模型，该模型遵循通用编码器-解码器框架，用于多步全市人群流量预测。该模型由名为 ST-Encoder 和 FR-Encoder 的两个编码器模块组成，分别用于对时空依赖性和每日流量相关性进行建模。我们对两个真实世界的数据集进行了广泛的实验，以评估所提出模型的性能，并表明我们的模型始终优于最先进的方法。