To develop a digital twin of an urban traffic network that simulates real-time vehicle flow, congestion levels, and signal timing optimization. The system will analyze live and historical traffic data to improve route planning, reduce congestion, and enhance urban mobility efficiency.
Study digital twin architecture and smart city traffic systems.
Collect real-time traffic datasets from open city APIs.
Design a virtual model of a selected urban traffic intersection.
Implement traffic flow simulation using Python or AnyLogic.
Integrate IoT sensor data simulation for vehicle density tracking.
Develop congestion prediction algorithms using machine learning.
Implement adaptive traffic signal optimization logic.
Create a dashboard for real-time visualization of traffic status.
Test system performance under different traffic scenarios.
Analyze improvements in traffic throughput and wait times.
Prepare technical documentation and system architecture diagrams.
Conduct performance benchmarking and simulation comparison.
