Semantic communication cuts traffic data transmission by 99 percent
Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins
Networking and Internet ArchitectureComputer Vision and Pattern RecognitionDistributed, Parallel, and Cluster Computing
Summary
Traffic systems need real-time data to avoid accidents and ease congestion, but sending video all the time takes too much internet space and can be slow. The authors propose sending only the important information about traffic instead of full videos, reducing the amount of data sent by over 99%. Their approach still keeps track of cars’ locations accurately within 12 centimeters, which is good for real-time traffic monitoring and control. This new method makes it easier and faster to create a virtual model of city traffic that reacts in real time.
What this means in practice
- •For traffic management teams: Enable real-time urban traffic monitoring with minimal data transfer by transmitting only essential vehicle and event information from roadside cameras.
- •For cloud service providers: Support scalable and low-latency digital twins for city-scale traffic systems by offloading semantic processing to edge devices and reducing video stream handling.
- •For autonomous vehicle integrators: Provide accurate, lightweight digital twin data for better collision prediction and route planning without overwhelming communication networks.$Commercial implications: Enables new services delivering real-time traffic digital twins for vehicle navigation and safety systems, made possible by efficient semantic data transmission.
Authors
Murat Arda Onsu, Arman Dogru, Poonam Lohan, Burak Kantarci, Aisha Syed
Abstract
Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent management to improve safety and reduce congestion. Although AI-driven traffic management has shown strong promise, its success relies on diverse, high-quality data spanning critical scenarios such as collisions, near-collisions, and traffic rule violations. Digital twins offer a powerful solution by enabling synchronized virtual replicas of physical traffic environments for real-time monitoring, collision prediction, traffic-flow optimization, and diverse realistic scenario generation. However, continuously synchronizing a high-fidelity urban traffic digital twin through edge surveillance cameras requires substantial edge-cloud communication, and transmitting raw video streams leads to severe bandwidth and latency constraints. To overcome this challenge, this paper proposes a semantic communication-based edge-cloud framework for real-time urban traffic digital twin applications. By leveraging edge intelligence to extract and transmit only task-relevant semantic traffic information instead of full video feeds, the proposed framework significantly reduces communication overhead while preserving the essential dynamics needed for accurate real-time digital twin reconstruction. Experimental results demonstrate more than 99% transmission savings and an average trajectory reconstruction error of only 0.12 m, confirming the proposed framework as an effective, scalable, and low-latency solution for AI-enabled urban traffic digital twin applications.