• kurapati supraja
• Hemalatha T
• Dr.D.Vivekananda Reddy

**ABSTRACT** The rapid growth of urbanization and the increasing number of vehicles have resulted in severe traffic congestion at urban intersections, creating a need for intelligent, adaptive, and predictive traffic management systems. This paper presents an AI-Driven Digital Twin System with Quantum Optimization for Smart Traffic Analysis and Control. The proposed system integrates computer vision, deep learning, predictive analytics, quantum optimization, and digital twin technologies to monitor and manage traffic at four-way intersections. Traffic videos from North, South, East, and West directions are processed using OpenCV, YOLOv8, and ByteTrack for vehicle detection, classification, and multi-object tracking. Median Blur, CLAHE, Non-Maximum Suppression, and majority voting techniques are applied to improve the reliability of traffic analysis. Rolling traffic density is estimated for each direction and an LSTM model implemented using PyTorch is used to predict future traffic conditions. Webster’s formula is employed for initial signal timing, while the traffic signal optimization problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and optimized using the Quantum Approximate Optimization Algorithm (QAOA) through the Cirq framework. The system also incorporates emergency vehicle detection and alert generation for priority-based traffic management. A 2D digital twin developed using Pillow provides a visual representation of vehicle movements, traffic density, signal states, and optimized signal timings. The complete system is integrated into a Django-based web portal with SQLite for data management, analysis, prediction, optimization, and visualization. The proposed framework provides an integrated approach toward intelligent, predictive, and adaptive traffic management for smart city transportation systems.

Digital Twin, Intelligent Transportation Systems, Smart Traffic Management, Adaptive Traffic Signal Control, YOLOv8, ByteTrack, Traffic Density Estimation, LSTM, Quantum Optimization, QUBO, QAOA, Emergency Vehicle Detection.

"AI-Driven Digital Twin System with Quantum Optimization for Smart Traffic Analysis and Control", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.a579-a589, October-2026, Available :http://www.jetir.org/papers/JETIR2610064.pdf

"AI-Driven Digital Twin System with Quantum Optimization for Smart Traffic Analysis and Control", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppa579-a589, October-2026, Available at : http://www.jetir.org/papers/JETIR2610064.pdf