Supervisor
David McQuaid
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study investigates hourly call volume forecasting for New Orleans police service data, comparing statistical, tree-based, and deep learning approaches. ARIMA models represent traditional methods, XGBoost and Random Forest serve as tree-based ensembles, and Gated Recurrent Units (GRUs) provide deep learning alternatives, with a naive seasonal model as a baseline. Models are evaluated using a practical, expanding time-block framework simulating real operational deployment. Results show GRUs achieve the highest accuracy (R² = 0.74, MAPE = 13.5%), with XGBoost performing similarly, while ARIMA underperforms. Additionally, a Random Forest model offers interpretability, identifying key factors that drive forecasting performance, providing actionable insights for demand management and resource planning.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Duggan, P.
(2025) Forecasting Hourly Police Call for Service Volumes: A Comparative Analysis of Statistical, Machine Learning and Neural Network Models for Operational Planning. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.103