RoadSense: An AI-Driven Smart Road Management System for Urban Traffic Intelligence

This paper presented RoadSense, an AI-driven Smart Road Management System that demonstrates a practical and immediately deployable approach to intelligent urban traffic management for Tier-2 Indian cities. The system successfully integrates seven operational modules into a single offline PWA, delivering measurable improvements across all evaluated performance metrics: signal efficiency increased by 39–40%, emergency response time reduced by 65–77%, and a 15–25% projected reduction in accident rates. The key innovation of RoadSense lies in its offline-first architecture, which eliminates the infrastructure dependency that has historically prevented intelligent traffic management deployment in cities like Salem. By packaging a complete AI traffic management platform into a 93 KB single-file application, the system achieves immediate deployability without capital expenditure on servers or network infrastructure. The three-layer architectural design provides a clear, structured pathway from the current fully functional prototype to full production deployment with physical sensor and signal hardware integration. Future work will focus on four phases: (1) integration with real Tamil Nadu RTO accident data and OpenWeatherMap API; (2) hardware deployment of Arduino signal controllers at 10 pilot Salem junctions; (3) training a custom YOLO v8 model on Salem-specific vehicle types and traffic conditions; and (4) scaling to full 38+ road corridor coverage with a companion mobile officer application and a public citizen-facing incident reporting portal integrated with the Tamil Nadu Police FIR registration system.

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