  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%.
