A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2024.
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Citing papers per year: 2025: 5; 2026: 4.
9 papers.
- 2025V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridorscited by 13
- 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
- 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happencited by 5
- 2025Automated Vehicles Should be Connected with Natural Languagecited by 5
- 2025On Transferring, Merging, and Splitting Task-Oriented Network Digital Twinscited by 0
- 2026Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validationcited by 0
- 2026Forecasting the Emergence and Evolution of Crash Hotspots: A Unified Deep Learning Framework for Proactive Traffic Safetycited by 0
- 2026How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Conceptcited by 0
- 2026Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learningcited by 0
- 2026Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning25 Aug 2026
- 2026How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Concept10 Aug 2026
- 2026Forecasting the Emergence and Evolution of Crash Hotspots: A Unified Deep Learning Framework for Proactive Traffic Safety27 Jul 2026
- 2026Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation19 May 2026
- 2025On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins2 Sep 2025
- 2025Automated Vehicles Should be Connected with Natural Language29 Jun 2025
- 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen27 May 2025
- 2025Generative AI for Autonomous Driving: Frontiers and Opportunities13 May 2025
- 2025V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors4 Mar 2025