Abstract

Timely and efficient emergency response is essential for public safety, particularly in high- risk urban environments. However, existing dispatch systems often rely on reactive decision- making, leading to delays and inefficient resource allocation. This thesis explores how artificial intelligence (AI) and data modeling can enhance police response times through predictive analytics. A systematic review of 17 peer-reviewed studies identified key predictors such as dispatch behavior, crime history, temporal characteristics, and geographic location. Using real-world emergency response and crime data, predictive models were developed and evaluated through algorithms including XGBoost, Linear Support Vector Machine (LSVM), Generalized Linear Model (GLM), and Neural Networks. The XGBoost model achieved the highest performance, with a correlation coefficient of 0.954, mean relative error (MRE) of 0.027, and R² of 0.89, outperforming other models in both accuracy and reliability. These findings demonstrate that AI-driven modeling can significantly improve the prediction of police response times and identify contributing patterns of delay. The research provides actionable recommendations for optimizing resource allocation, dispatch prioritization, and route planning. Overall, this work contributes to the growing intersection of AI and public safety by illustrating how data-driven methods can enhance operational efficiency and urban crime response outcomes.

Publication Date

12-25-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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