Ambulance Demand Prediction via Convolutional Neural Networks

TL;DR

This paper introduces a novel CNN architecture that transforms time series ambulance demand data into heatmaps, effectively incorporating external features for improved demand prediction, demonstrated through a case study in Seattle.

Contribution

The paper presents a flexible CNN model that integrates external features and employs Bayesian optimization for demand forecasting, outperforming existing methods.

Findings

CNN outperforms state-of-the-art methods by over 9%

Inclusion of external features improves prediction accuracy

Case study on Seattle data validates model effectiveness

Abstract

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%.