State-of-the-Art Review and Comparative Experimentation of Emergency Call Prediction Models

In this article, we present a comprehensive survey of emergency call volume prediction methods, along with a comparative experimental study of various models. We first outline the methods and their use cases, highlighting the key features leveraged in each state-of-the-art approach. Using real time series data on emergency calls, supplemented with meteorological, demographic, and event-related variables, we evaluate the existing models at two granularities: yearly and daily. In addition to applying the original methods, as they are proposed in the state of the art, we perform the variable selection through techniques like Lasso, Correlation Coefficients (CC), Recursive Feature Elimination (RFE), and Random Forest Feature Importance (RFFI). We then compare time series based models, regression models, neural networks, and non-parametric approaches. Performance is evaluated using metrics including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Residual Standard Deviation (RSD), and the Coefficient of Determination (R 2 ). The results show that Random Forest and feature-selection–based Lasso achieve the highest accuracy for predicting the total call volume for each hour of the day throughout the year. For daily call volume, time series–based methods perform best when using weather conditions and temporal variables selected by the RFFI method.