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Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks

机译：通过异构多图形神经网络预测救护车需求与异质多图形神经网络

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Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment. This topic has been gaining increasing significance, as virtually every country is experiencing an aging population, with generally higher level of vulnerability and demand for the emergency medical service (EMS). Although exploring the spatial and temporal correlations in EMS historical records, the existing methods principally consider the former time-invariant, which does not necessarily hold in reality. Moreover, this assumption ignores the fact that the behind-the-scenes dynamics are people, whose demographic profiles and activity patterns could be determinants of regional EMS demands. In this paper, we are therefore motivated to mine the collective daily routines in human mobility, to further represent the evolving spatial correlations. Particularly, we model profiled mobility groups as multiple random walkers and propose a novel bicomponent neural network, including a heterogeneous multi-graph convolution layer and spatio-temporal interlacing attention module, to perform the prediction task. Experimental results on the real-world data verify the effectiveness of introducing dynamic human mobility and the advantage of our approach over the state-of-the-art models.
机译：预测区域救护车需求在动态舰队配置和重新部署中发挥着基本的一部分。本议程越来越重要，实际上每个国家都在经历老龄化人口，易受伤害和对紧急医疗服务（EMS）的脆弱性和需求普及。虽然探索EMS历史记录中的空间和时间相关性，但现有方法主要考虑以前的时间不变，这不一定存在于现实中。此外，这种假设忽略了幕后动态是人的人口概况和活动模式可能是区域EMS要求的决定因素。在本文中，我们是有动力在人类流动中的集体日常生中挖掘，以进一步代表不断发展的空间相关性。特别是，我们将分类的移动组模型为多个随机步行者，并提出了一种新颖的双组分神经网络，包括异构多图卷积层和时空交织注意力模块，以执行预测任务。实验结果对现实世界数据验证了引入动态人类流动性的有效性以及我们对最先进的模型的方法的优势。

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