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

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arxiv 2410.00356 v2 pith:5D6GGKL2 submitted 2024-10-01 cs.RO cs.ETcs.SYeess.SY

classification cs.ROcs.ETcs.SYeess.SY
keywords dataphysicaldigitalsystemstrafficvehiclecommunicationsconnected
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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  1. DigiT4TAF -- Bridging Physical and Digital Worlds for Future Transportation Systems

    cs.RO 2025-07 conditional novelty 5.0 of 10

    An open-source Digital Twin of the TAF-BW test area integrates real infrastructure sensor detections into an Unreal Engine 5 simulation, demonstrated by traffic signal optimization and V2X security case studies.

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