Built UrbanMind, a real-time urban traffic intelligence platform combining AI-powered computer vision, adaptive traffic management, emergency response automation, and digital-twin visualization into a unified mobility system. Developed YOLOv8-based vehicle detection for traffic footage analysis, including vehicle classification, lane-density analysis, detection confidence, annotated evidence frames, and signal-timing recommendations based on traffic conditions. Designed a real-time architecture using FastAPI, React, Redis, WebSockets, and Docker to synchronize traffic data across dashboards, analytics, emergency operations, ROI analysis, and digital-twin interfaces. Implemented an emergency response workflow that tracks ambulance, fire, and police units using GPS telemetry, activates green corridors, identifies congestion along active routes, and generates alternate-corridor recommendations. Built operator-facing modules for live multi-sector monitoring, manual vision analysis, emergency dispatch, traffic analytics, and spatial visualization across a simulated 9-sector urban traffic network. Tech: Python · FastAPI · React · TypeScript · YOLOv8 · Computer Vision · OpenCV · Redis · WebSockets · Docker · Leaflet · Zustand · Three.js