Crowd flow prediction has become a strategically important task in urban computing, which is the prerequisite for traffic management, urban planning and public safety. However, due to variousness of crowd flows, multiple hidden correlations among urban regions affect the flows. Besides, crowd flows are also influenced by the distribution of Points-of-Interests (POIs), transitional functional zones, environmental climate, and different time slots of the dynamic urban environment. Thus, we exploit multiple correlations between urban regions by considering the mentioned factors comprehensively rather than the geographical distance and propose multi-graph convolution gated recurrent units (MGCGRU) for capturing these multiple spatial correlations. For adapting to the dynamic mobile data, we leverage multiple spatial correlations and the temporal dependency to build an urban flow prediction framework that uses only a little recent data as the input but can mine rich internal modes. Hence, the framework can mitigate the influence of the instability of data distributions in highly dynamic environments for prediction. The experimental results on two real-world datasets in Shanghai show that our model is superior to state-of-the-art methods for crowd flow prediction.

中文翻译：

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利用城市区域之间的多重相关性进行人流预测

人流预测已成为城市计算中一项具有战略意义的重要任务，它是交通管理、城市规划和公共安全的前提。然而，由于人群流动的多样性，城市区域之间存在多种隐藏的相关性影响人群流动。此外，人流还受到兴趣点（POI）分布、过渡功能区、环境气候以及动态城市环境的不同时间段的影响。因此，我们通过综合考虑上述因素而不是地理距离来利用城市区域之间的多重相关性，并提出多图卷积门控循环单元（MGCGRU）来捕获这些多重空间相关性。为适应动态移动数据，我们利用多个空间相关性和时间依赖性来构建城市流量预测框架，该框架仅使用少量近期数据作为输入，但可以挖掘丰富的内部模式。因此，该框架可以减轻高动态环境中数据分布不稳定性的影响以进行预测。上海两个真实世界数据集的实验结果表明，我们的模型优于最先进的人群流量预测方法。