State-of-the-Art Review and Comparative Experimentation of Emergency Call Prediction Models
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".