A GIS-Based Demand Forecast Using Machine Learning for Emergency Medical Services

The objective for pre-hospital Emergency Medical Service (EMS) is to reach to, pick up, and deliver patients efficiently. By increasing the operational efficiency, the survival rate of major trauma patients could potentially be improved. In this research, the authors applied Moving Average, Artificial Neural Network, Linear Regression, and Support Vector Machine for the forecast of pre-hospital emergency medical demand. The results from these approaches, as a reference, could be used for pre-allocation of ambulances. The training of the models as well as the validation is conducted with data collected from the New Taipei City EMS. In order to represent the performance of forecast, the authors introduced the Geographic Information System (GIS) to manage and visualize the spatial distribution of forecast demand and error. Moreover, the authors built a flexible model which organizes demand data with the user given size of area and size of time step. With the easy use of model and acceptable prediction performance, the research outcome has its potential to be applied to the current practice.