AI-Powered World Models for Smart Governance: Integrating Traffic, Infrastructure, and Emergency Response Data

Abstract: This paper presents a novel AI-powered world modeling framework designed to support smart governance through the integration of traffic, infrastructure, and emergency response data. By constructing a unified spatiotemporal graph that continuously learns from real-time sensor inputs, mobility feeds, and incident logs, the system enables predictive simulation and policy optimization for urban decision-makers. Using a combination of graph neural networks, spatiotemporal transformers, and reinforcement learning, the model captures dynamic interactions among critical urban subsystems. Scenario-based simulations—ranging from infrastructure stress to multi-hazard emergencies—are used to evaluate governance strategies under uncertainty. An interactive GIS-based dashboard allows policymakers to test counterfactuals and visualize the cascading effects of interventions. The proposed architecture enhances operational readiness, decision transparency, and systemic resilience, offering a scalable foundation for AI-augmented public sector planning. Keywords: world models, smart governance, AI for public policy, urban resilience, traffic data, infrastructure monitoring, emergency response, spatiotemporal modeling, graph neural networks, digital twin, reinforcement learning, decision support systems, urban informatics, policy simulation