Harnessing Crowdsourced Mobile Data and LLM for Dynamic and Accessible Pedestrian Routing

Walking is a fundamental mode of human movement and an essential component of urban mobility. However, traditional pedestrian navigation systems lack real-time sidewalk accessibility data, relying primarily on static maps that fail to reflect dynamic hazards, such as construction zones, obstructions, or uneven pavement. To address this gap, we present a sidewalk navigation system that integrates crowdsourced reports, adaptive routing, and AI-powered assistance. The system leverages OpenStreetMap (OSM) maps and real-time user contributions to dynamically adjust pedestrian routes based on reported obstacles, ensuring a safer and more efficient walking experience. In addition, a user-driven rating system validates sidewalk conditions, while an AI assistant powered by Large Language Models (LLMs) provides context-aware guidance, interactive navigation, and additional insights. The application also features turn-by-turn navigation with audio support, enhancing accessibility for many users. By combining real-time crowdsourced data, personalized routing, and AIenhanced navigation, our system addresses critical limitations in existing pedestrian navigation applications and provides a more interactive, adaptive, and user-driven approach to sidewalk accessibility.