A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors

TL;DR

This paper presents a real-time digital twin framework for connected vehicle corridors that integrates infrastructure, vehicles, and signals using C-V2X technology, enabling enhanced traffic management and safety.

Contribution

It develops a scalable, synchronized digital twin for V2X-enabled corridors, demonstrating real-time traffic simulation, analysis, and feedback mechanisms in a real-world setting.

Findings

Accurate real-time replication of vehicle behaviors and traffic patterns.

Effective feedback for signal timing and incident notifications.

Robust architecture supporting future large-scale deployments.

Abstract

Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT), a virtual representation that enables simulation, analysis, and optimization through real-time data exchange and communication. Although existing studies have explored DTs for vehicles, communications, pedestrians, and traffic, real-world validations and implementations of DTs that encompass infrastructure, vehicles, signals, communications, and more remain limited due to several challenges. These include accessing real-world connected infrastructure, integrating heterogeneous, multi-sourced data, ensuring real-time data processing, and synchronizing the digital and physical systems. To address these challenges, this study develops a traffic DT based on a real-world connected vehicle corridor. Leveraging the Cellular Vehicle-to-Everything (C-V2X) infrastructure in the corridor, along with communication, computing, and simulation technologies, the proposed DT accurately replicates physical vehicle behaviors, signal timing, communications, and traffic patterns within the virtual environment. Building upon the previous data pipeline, the digital system ensures robust synchronization with the physical environment. Moreover, the DT's scalable and redundant architecture enhances data integrity, making it capable of supporting future large-scale C-V2X deployments. Furthermore, its ability to provide feedback to the physical system is demonstrated through applications such as signal timing adjustments, vehicle advisory messages, and incident notifications. The proposed DT is a vital tool in T-CPS, enabling real-time traffic monitoring, prediction, and optimization to enhance the reliability and safety of transportation systems.