## 3 Literature Review and Gap Analysis  
The integration of artificial intelligence (AI) with urban safety initiatives has seen rapid advancement, particularly in crime forecasting and patrol optimization. Initial efforts in this domain relied heavily on hotspot detection methods using kernel density estimation and GIS-based mapping platforms such as CrimeStat and ArcGIS, primarily aimed at strategic deployment rather than adaptive, real-time response [3, 4]. While useful for retrospective analysis and broad spatial targeting, these methods lacked temporal granularity and could not account for sudden changes in urban dynamics. The next wave of innovation introduced predictive policing models grounded in statistical and machine learning techniques. For instance, [PERSON] et al. [1] proposed a self-exciting point process to capture repeat and near-repeat victimization patterns, enabling short-term crime prediction at the neighborhood level. Similarly, [PERSON] et al. [13] leveraged deep learning architectures to estimate crime intensity as a continuous spatiotemporal function, which showed high forecasting accuracy, albeit with limited interpretability for decision-makers on the ground.  
To enable autonomous and context-aware patrol planning, reinforcement learning (RL) has gained prominence as a viable decision-making framework. [PERSON] et al. [14] employed Q-learning for patrol robots in controlled environments, and [PERSON] et al. [15] extended the approach to urban traffic policing using Deep Q-Networks (DQNs). However, most existing RL-based patrolling frameworks are constrained by simplified assumptions, such as static environment modeling or full observability of state transitions. They often fail to incorporate real-time constraints like sensor failures, fuel depletion, or the influence of surrounding crowd density--factors crucial for practical AV deployments. Simultaneously, Spatiotemporal Graph Neural Networks (ST-GNNs) have emerged as powerful tools to capture evolving urban patterns. [PERSON] et al. [5] introduced ST-GCNs for traffic prediction using graph-structured data, while [PERSON] et al. [2] applied attention-based variants for neighborhood-level crime prediction. These architectures excel at modeling both spatial adjacency and temporal evolution but have rarely been fused with reinforcement learning for downstream patrol optimization tasks. Despite these promising developments, critical limitations remain across existing literature. Most notably, routing strategies in RL-based models continue to use discretized gridworlds or static graphs that fail to represent the granularity and variability of real road networks. Sensor telemetry--such as real-time fuel levels, onboard threat detection systems, or environmental factors like congestion and noise--is typically absent from the state space, limiting practical deployability. Furthermore, algorithmic comparisons are sparse, making it difficult to determine which RL approaches perform reliably under variable conditions. Few studies evaluate cross-city generalizability, a key concern when transferring learned policies across diverse urban morphologies such as those of Seattle and Washington DC.  
In response to these gaps, as per Table 2, our study introduces a unified framework that integrates crime risk prediction through ST-GCNs with deep reinforcement learning agents capable of adapting to real-time AV sensor inputs. By modeling cities as directed, risk-weighted road graphs, we enable continuous patrolling strategies that reflect both spatial constraints and temporal crime dynamics. The proposed architecture is evaluated using three distinct RL algorithms--DQN, DDQN, and PPO--and tested across over a decade of crime data from two metropolitan regions. This comprehensive evaluation facilitates both methodological rigor and practical insight into scalable, city-wide deployment of autonomous patrol vehicles for dynamic public safety operations.