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

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.

Automated summary

This study aims to predict regional ambulance demand by mining collective daily routines in human mobility, modeling profiled mobility groups as multiple random walkers and proposing a novel neural network structure. Experimental results validate the effectiveness of this approach.