Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Prediction
Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Prediction
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DOI：
10.48550/arxiv.2203.00489
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Yuki Kubota;Y. Ohira;Tetsuo Shimizu
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文献类型：
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作者：
Yuki Kubota;Y. Ohira;Tetsuo Shimizu
未来短期人口预测是城市计算中的一个关键问题。准确的未来人口预测可以为城市规划者或开发者提供丰富的见解。然而，由于其复杂的时空依赖性，预测未来人口是一项具有挑战性的任务。许多现有的作品试图通过将城市划分为网格并使用卷积神经网络（CNN）来捕获空间相关性。然而，CNN仅仅通过使用矩形滤波器来捕获空间相关性;它忽略了城市环境信息，如铁路分布和POI位置。此外，这些信息对人口预测的重要性在每个地区都有所不同，并受到天气条件和星期几等背景情况的影响。为了解决这个问题，我们提出了一种新的深度学习模型，称为基于注意力的上下文多视图图卷积网络（ACMV-GCN）。我们首先基于城市环境信息构建多个图，然后ACMV-GCN使用图卷积网络从各种视图中捕获空间相关性。此外，我们增加了一个注意力模块，考虑上下文的情况下，利用城市环境信息对未来的人口预测。使用通过移动的电话收集的统计人口计数数据，我们证明了我们提出的模型优于基线方法。此外，通过可视化由注意力模块计算的权重，我们证明了我们的模型可以在没有任何先验知识的情况下有效地利用城市环境信息。
Short-term future population prediction is a crucial problem in urban computing. Accurate future population prediction can provide rich insights for urban planners or developers. However, predicting the future population is a challenging task due to its complex spatiotemporal dependencies. Many existing works have attempted to capture spatial correlations by partitioning a city into grids and using Convolutional Neural Networks (CNN). However, CNN merely captures spatial correlations by using a rectangle filter; it ignores urban environmental information such as distribution of railroads and location of POI. Moreover, the importance of those kinds of information for population prediction differs in each region and is affected by contextual situations such as weather conditions and day of the week. To tackle this problem, we propose a novel deep learning model called Attention-based Contextual Multi-View Graph Convolutional Networks (ACMV-GCNs). We first construct multiple graphs based on urban environmental information, and then ACMV-GCNs captures spatial correlations from various views with graph convolutional networks. Further, we add an attention module to consider the contextual situations when leveraging urban environmental information for future population prediction. Using statistics population count data collected through mobile phones, we demonstrate that our proposed model outperforms baseline methods. In addition, by visualizing weights calculated by an attention module, we show that our model learns an efficient way to utilize urban environment information without any prior knowledge.