Smart Navigation System for Urban Route Optimization

Abstract

Urban navigation has become increasingly complex due to rapid population growth, traffic congestion, road safety concerns, and climate-related disruptions such as flooding. This paper presents a Smart Navigation System for Urban Route Optimization, an AI-powered full-stack web application specifically engineered to address the multifaceted challenges of urban mobility in metropolitan environments. The system computes and presents four distinct route types—fastest, shortest, safest, and traffic-optimized—by leveraging real-time data integration, machine learning models, and graph-based pathfinding algorithms. Key components include accident hotspot prediction, dynamic flood risk assessment, multi-modal transportation planning encompassing road, metro, and bus networks, and real-time incident reporting. The frontend is built using React with TypeScript and Leaflet.js for interactive map visualization, while the backend employs FastAPI with OSMnx and NetworkX for road graph construction and route computation. Machine learning modules developed with scikit-learn and XGBoost enable congestion forecasting and risk scoring. Experimental evaluations demonstrate that the system significantly outperforms conventional navigation approaches in safety awareness, real-time responsiveness, and user-centric route selection.