Graduation Semester and Year
Fall 2025
Language
English
Document Type
Thesis
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Yuan Zhou
Abstract
Crime reduction remains a global priority, demanding both accurate modeling of criminal dynamics and efficient allocation of scarce policing resources. To address these needs, this study presents a two‐fold framework that (1) simulates street‐level crime patterns using an agent‐based model (ABM) grounded in Routine Activity Theory (RAT), Rational Choice Theory (RCT), and Crime Pattern Theory (CPT), and (2) optimizes patrol routing through a time-dependent, multi‐visit mixed‐integer linear programming (MILP) formulation.
In the first component, we integrate real‐world crime, environmental, and census data to reproduce realistic offender, citizen, and Police behaviors, capturing where and when robbery, burglary, and larceny occur across an urban landscape. The ABM’s outputs, spatial‐temporal crime distributions, are then used to evaluate two policing strategies: targeted hotspot patrolling at peak crime times and a randomized patrol protocol in which officers allocate a fraction of non‐hotspot duties to hotspot coverage. Results demonstrate that focused hotspot deployments yield significant reductions across all three crime types, affirming the value of data‐driven patrol allocation. Building on these insights, the second component introduces a MILP enhancement of the classical vehicle routing problem. By embedding multiple visit indices per hotspot and linking activation decisions directly to routing constraints, the model assigns patrol durations and schedules revisits in response to changing crime intensities. Tested on real‐time, time‐dependent crime datasets, this optimization yields actionable, multi‐visit patrol routes that balance coverage demand with operational feasibility.
Together, these contributions offer a unified simulation‐optimization toolkit: the ABM evaluates “what if” patrol scenarios in a criminologically sound environment, and the MILP model prescribes “how” to deploy officers most effectively over time. This integrated approach equips urban law‐enforcement agencies with actionable insights for both strategy evaluation and real‐time patrol scheduling, enhancing the efficiency and responsiveness of crime prevention efforts.
Disciplines
Industrial Engineering | Operational Research
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Moradi, Shohreh, "Crime Theory Informed Agent-Based Modeling for Crime Prediction and Patrolling Route Optimization" (2025). Industrial, Manufacturing, and Systems Engineering Dissertations. 250.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/250