🚦 Next-generation AI Traffic Management System with real-time computer vision, reinforcement learning optimization, emergency vehicle detection, and immersive 3D visualization

An AI-driven Adaptive Traffic Signal Control System (ATSCS) that replaces static timers with dynamic green-light phases. Utilizes YOLOv8 for real-time vehicle density estimation and multi-class classification, achieving 96.4% mAP. Optimized for low-latency inference (>30 FPS) on edge devices to reduce urban congestion and commuter wait times.

SUMO

A trajectory-aware emergency corridor orchestration prototype for smart cities. Integrates SUMO simulation, FastAPI, and React to automate traffic signal preemption and reduce ambulance response times.

An intelligent traffic management agent developed with Deep Reinforcement Learning (DQN) and PyTorch. This project optimizes signal timings in real-time, achieving a 26% reduction in average vehicle wait time and 17% queue reduction in SUMO simulations compared to fixed-timer baselines.

A centralized deep reinforcement learning framework for adaptive urban traffic signal control, leveraging simulation-based environments to minimize congestion and optimize traffic flow.

AI-powered dynamic traffic management system using PyTorch DQN and SUMO. Built for SIH.