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Efficient vehicle patrol scheduling for urban safety: Optimization and heuristic approaches
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DOI：10.1016/j.future.2026.108756.png)
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Abstract
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Efficient Vehicle Patrol Scheduling (VPS) is essential for improving urban safety, ensuring security, and optimizing operational performance. Traditional scheduling methods often struggle to balance multiple objectives and constraints, such as limited vehicles, mandatory rest periods, and strict revisit cadences under dynamic traffic conditions. To address these challenges, this study proposes a dual methodological approach: first, a formalized optimization model is introduced to obtain exact solutions for small-scale instances; second, two novel scalable heuristics are developed, Adaptive Hill-Climbing-Based Patrol Scheduling (AHBPS) and Genetic-Based Dynamic Vehicle Patrol Scheduling (GDVPS), designed to handle large-scale and dynamic urban networks. Extensive simulations using real-world urban maps demonstrate that GDVPS consistently outperforms AHBPS in both solution quality and scalability, achieving up to 80% coverage in networks with 1000 locations, while maintaining real-time feasibility. The results confirm that GDVPS provides a robust, dynamic, and scalable scheduling solution, making it a promising candidate for deployment in modern urban safety operations.
Keywords:
Vehicle Patrol Scheduling
Vehicle routing
Dynamic scheduling
Operational efficiency
Security coverage
Adaptive scheduling
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