Forecasting ambulance demand with profiled human mobility via heterogeneous multi-graph neural networks

Wang，Zhaonan1,2; Xia，Tianqi1,2; Jiang，Renhe1,4; Liu，Xin2,3; Kim，Kyoung Sook2,3; Song，Xuan1,4; Shibasaki，Ryosuke1

10.1109/ICDE51399.2021.00154

2021-04-01

1084-4627

978-1-7281-9185-0

Proceedings - International Conference on Data Engineering

2021-April

1751-1762

19-22 April 2021

Chania, Greece

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.

Ambulance Demand Prediction Heterogeneous Multi-Graph Convolution Profiled Human Mobility Spatio-Temporal Attention

通讯

英语

CPCI-S ; EI

WOS:000687830800146

20213410801177

Ambulances ; Emergency services ; Forecasting

Biomedical Equipment, General:462.1 ; Accidents and Accident Prevention:914.1

2-s2.0-85112864170

Scopus

1.The University of Tokyo,Center for Spatial Information Science,Japan
2.National Institute of Advanced Industrial Science and Technology,Artificial Intelligence Research Center,
3.AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory,
4.SUSTech-UTokyo Joint Research Center on Super Smart City,Southern University of Science and Technology,

Wang，Zhaonan,Xia，Tianqi,Jiang，Renhe,et al. Forecasting ambulance demand with profiled human mobility via heterogeneous multi-graph neural networks[C],2021:1751-1762.