Digital Twin-Assisted Real-Time Traffic Data Prediction Method for 5G-Enabled Internet of Vehicles

Abstract

The development of Internet of Vehicles (IoV) has produced a considerable amount of real-time traffic data. These traffic data constitute a kind of digital twin that connects the physical vehicles and their virtual representation via 5G communications. Generally, through analyzing the digital twin traffic data, traffic administrators can optimize traffic scheduling and alleviate traffic jams. However, the exceptions of IoV sensors inevitably raise an issue of traffic data sparsity and consequently influence scientific traffic scheduling decisions. Inspired by this drawback, in this article, a digital twin-assisted real-time traffic data prediction method is proposed by analyzing the traffic flow and velocity data monitored by IoV sensors and transmitted through 5G. At last, we conduct a set of experiments based on a traffic dataset collected by Nanjing city of China. Reported results show the feasibility of our proposal in smart traffic flow and velocity prediction that call for a quick response and high accuracy.