Forecasting Hourly Ambulance Demand for Oslo, Norway: A Neuro-Symbolic Method


  • Erling Van De Weijer NTNU
  • Odd André Owren NTNU
  • Ole Jakob Mengshoel NTNU


Ambulance Demand Forecasting, Machine Learning, Artificial Neural Networks, Statistical Decomposition


Forecasting ambulance demand is critical for emergency medical services to allocate their resources as efficiently as possible. This work uses data from Norway's Oslo University Hospital (OUH) to forecast hourly ambulance demand in Oslo and Akershus. To forecast demand, we developed a neuro-symbolic method, DeANN. DeANN integrates statistical decomposition and artificial neural network methods. Statistical decomposition computes trend, seasonal, and residual components from the ambulance demand time series. Using these components, we apply a multilayer perceptron and regression to compute an overall ambulance demand forecast. Based on experimental results, we conclude that our proposed neuro-symbolic approach for ambulance demand forecasting outperforms several baseline models. Our best neuro-symbolic model has a mean squared error of 21.68 and improves on previous results for the OUH data set.


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