Towards a Digital Twin Framework for Secure and Efficient Cyber-Physical Transportation Systems

Cyber-Physical Systems (CPS) integrating autonomous vehicles (AVs) for mixed transport of people and goods necessitate robust methodologies to ensure secure, high-performance operations. This paper proposes a Digital Twin (DT) framework for a centralized transportation network, designed to optimize performance and enhance security using feedback-driven mechanisms. Real-time data from AVs is fed into the DT to enable Model Predictive Control (MPC), optimizing efficiency while ensuring compliance with system constraints. Behavioral profiling and pattern recognition are employed to detect misuse and anomalous vehicle behavior, bolstering system security through intrusion detection. The feedback loops compare DT predictions against real-world data to preemptively mitigate threats and operational inefficiencies, offering a flexible approach to securing and improving CPS performance.