A GIS-integrated agent-based simulation framework for modeling and evaluation of police patrol operations

This study presents a Geographic Information Systems (GIS)-integrated agent-based simulation (ABS) framework designed to evaluate police patrol deployment and shift scheduling under realistic operational constraints. The model integrates empirical Computer-Aided Dispatch (CAD) data, GIS-based travel-time routing, and shift-level scheduling logic within a unified ABS environment. It captures dynamic interactions among patrol units, incident locations, and time-varying service demand. A series of scenario-based experiments investigate the effects of key operational parameters, shift length (8-hour vs. 10-hour), patrol force size, and routing logic (shortest vs. fastest path) on system performance indicators such as response time, officer utilization, and workload balance. Results show that 10-hour shifts consistently improve response efficiency compared to 8-hour shifts, while larger patrol sizes enhance workload equity without significantly reducing delays. The model also quantifies the trade-offs between workforce expansion and scheduling strategy. The simulation is calibrated using real-world patrol data from Arlington Police Department, Texas, and validated through both historical benchmarks and synthetic call-arrival profiles. The model offers a configurable and adaptable simulation-based planning framework for urban public-service operations. The proposed framework demonstrates how agent-based simulation, enriched with spatial routing and empirical scheduling data, can support tactical decision-making in complex, service-driven systems.