Paper Info
Title
Demand Forecast using Data Analytics for the Pre-allocation of Ambulances
Abstract
The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introduced in this study to manage and visualize the spatial distribution of demand data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user-desired sizes for the spatial grid and discretized temporal steps. We applied moving average, artificial neural network, sinusoidal regression, and support vector regression for the forecasting of prehospital emergency medical demand. The results from these approaches, as a reference, could be used for the preallocation of ambulances. A case study is conducted for the EMS in New Taipei City, where prehospital EMS data have been collected for three years. The model selection process has chosen different models with different input features for the forecast of different areas. The best daily mean absolute percentage error during testing of the EMS demand forecast is 23.01%, which is a reasonable forecast based on Lewis’ definition. With the acceptable prediction performance, the proposed approach has its potential to be applied to the current practice.
Year
DOI
Venue
2016
10.1109/JBHI.2015.2443799
Biomedical and Health Informatics, IEEE Journal of
Keywords
Field
DocType
Artificial Neural Network,Demand Forecast,Emergency Medical Services,Geographic Information System,Support Vector Machine
Mean absolute percentage error,Geographic information system,Data mining,Data analysis,Demand forecasting,Computer science,Model selection,Operational efficiency,Moving average,Grid
Journal
Volume
Issue
ISSN
PP
99
2168-2194
Citations 
PageRank 
References 
4
0.43
6
Authors
4
Name
Order
Citations
PageRank
Albert Chen1102.37
Lu, Tsung-Yu240.43
Matthew Huei-Ming Ma340.43
Sun, Wei-Zen440.43