## 1 Introduction  
Urban crime remains a persistent challenge for law enforcement agencies, characterized by its spatiotemporal volatility and dependence on complex sociocultural and economic factors. Crime patterns frequently shift across neighborhoods, hours, and days in response to socioeconomic triggers such as unemployment rates, school schedules, public events, and seasonal variations [1, 2]. These patterns are further complicated by factors such as repeat victimization, near-repeat phenomena, and opportunistic behaviors, which make effective policing a dynamic, data-intensive task. Despite this inherent variability, traditional patrol strategies remain largely static--built around fixed routes, rigid timetables, and reactive deployment tactics that fail to account for the temporal and spatial fluidity of crime hotspots [3, 4]. Such pre-defined strategies often result in inefficient resource allocation, diminished deterrence in emergent high-risk zones, and increased vulnerability to operational blind spots. The increasing volume of real-time data from public safety sensors, 911 dispatch logs, and community incident reports has exposed a critical gap: existing methods lack the computational flexibility and contextual intelligence to adapt patrols dynamically based on real-world indicators. As a result, law enforcement efforts are frequently misaligned with actual threat distributions, leading to both under-policing of critical areas and over-policing of low-risk zones--consequences that carry not only operational but also ethical implications. Recent advancements in smart city infrastructure, particularly the proliferation of autonomous vehicles (AVs), edge computing, and Internet of Things (IoT)-enabled sensors, offer new opportunities to rethink how urban patrolling is planned and executed. These technologies provide a foundation for creating intelligent, adaptive, and decentralized patrol systems that can sense, reason, and act in real time [5]. When combined with historical crime data and predictive modeling, AVs can move beyond reactive policing toward a proactive deterrence model--one that maximizes coverage, prioritizes high-risk locations, and dynamically adjusts to contextual cues such as fuel availability, lighting conditions, or crowds. To this end, we propose a novel, data-driven patrol planning framework that integrates three core components: (1) historical crime datasets spanning multiple years and cities, (2) predictive models built on Spatiotemporal Graph Convolutional Networks (ST-GCNs) to forecast evolving crime risk across urban zones, and (3) policy optimization agents that learn optimal routing behavior through feedback from simulated city environments [2; 5]. The urban landscape is modeled as a directed, weighted graph where each node represents a spatial unit such as a precinct or cluster, and edge weights are determined by both predicted risk and operational constraints. The framework is tested on two rich urban crime datasets: Washington DC (2024-2025), which provides high-frequency, high-resolution incident records; and Seattle (2008-2024), offering a longitudinal view of crime evolution across a major U.S. metropolitan region [6]. Using these datasets, we train policy optimization agents that learn to navigate the city graph efficiently while minimizing patrol redundancy and maximizing spatiotemporal crime coverage. We evaluate three state-of-the-art value-based and policy-gradient algorithms--Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO)--each representing a different strategy for handling uncertainty and environmental variability [7; 8; 9]. DQN serves as a foundational benchmark, while DDQN addresses value overestimation via decoupled action evaluation, and PPO incorporates stochastic policy updates and real-time adaptation to sensor inputs such as vehicle speed, fuel levels, and onboard threat detection [10].