Abstract
Urban environments increasingly demand intelligent systems for routing and street coverage to manage growing infrastructure complexity. Despite recent advances in geospatial technologies and IoT, there remains a lack of scalable, open platforms that support adaptive routing models and real-time simulation. This paper presents a lightweight, reproducible Smart City platform for experimenting with intelligent routing algorithms and full street coverage analysis. The architecture integrates simulated IoT data, PostGIS-based spatial querying, microservice orchestration, and a dynamic web interface for visualization. The system supports prototyping of routing strategies such as A* and edge coverage models, enabling both research and practical deployment. Experimental results show the platform achieves API response times under 120 ms (uncached), under 10 ms (cached), with frontend load times under 3 seconds. Redis caching and container-based scaling ensure consistent performance under concurrent load. The results demonstrate the platform’s suitability for routing simulations, decision support, and educational use in constrained environments. By bridging adaptive models, open mapping, and urban simulation, the platform provides a solid foundation for Smart City experimentation.