Ambulance Demand Prediction via Convolutional Neural Networks
This work addresses the problem of improving emergency medical services' operational efficiency for better patient outcomes, though it is incremental as it builds on existing CNN methods with a new architecture and feature integration.
The paper tackles ambulance demand prediction by developing a novel convolutional neural network (CNN) architecture that transforms time series data into heatmaps, incorporating external features like weather and events, and shows it outperforms existing state-of-the-art methods and industry practice by over 9% in a case study using Seattle's 911 call data.
Minimizing response times is crucial for emergency medical services to reduce patients' waiting times and to increase their survival rates. Many models exist to optimize operational tasks such as ambulance allocation and dispatching. Including accurate demand forecasts in such models can improve operational decision-making. Against this background, we present a novel convolutional neural network (CNN) architecture that transforms time series data into heatmaps to predict ambulance demand. Applying such predictions requires incorporating external features that influence ambulance demands. We contribute to the existing literature by providing a flexible, generic CNN architecture, allowing for the inclusion of external features with varying dimensions. Additionally, we provide a feature selection and hyperparameter optimization framework utilizing Bayesian optimization. We integrate historical ambulance demand and external information such as weather, events, holidays, and time. To show the superiority of the developed CNN architecture over existing approaches, we conduct a case study for Seattle's 911 call data and include external information. We show that the developed CNN architecture outperforms existing state-of-the-art methods and industry practice by more than 9%.