ABSTRACT

Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.

摘要

动态人类活动强度信息在许多基于位置的应用中非常重要。然而，人类活动强度的预测仍然存在两个局限性。首先，很难学习跨尺度的空间交互模式来预测人类活动。其次，社会互动可以帮助模拟活动强度变化，但在现有文献中很少考虑。为了减轻这些限制，我们提出了一种新颖的动态活动强度预测方法，该方法使用物理和社会空间中的交互对图形进行深度学习。在该方法中，空间单元之间的物理交互和社会交互被集成到融合图卷积网络中，以对多类型空间交互模式进行建模。结合空间相互作用模式和活动强度序列的时间模式，预测未来活动强度变化。该方法已通过国家范围的匿名手机数据集进行验证。结果表明，我们提出的结合图卷积网络和循环神经网络的深度学习方法优于其他基线方法。该方法可以从更加空间和社会整合的角度进行动态人类活动强度预测，这有助于提高人类动态建模的性能。结果表明，我们提出的结合图卷积网络和循环神经网络的深度学习方法优于其他基线方法。该方法可以从更加空间和社会整合的角度进行动态人类活动强度预测，这有助于提高人类动态建模的性能。结果表明，我们提出的结合图卷积网络和循环神经网络的深度学习方法优于其他基线方法。该方法可以从更加空间和社会整合的角度进行动态人类活动强度预测，这有助于提高人类动态建模的性能。