Towards Adaptative Cyber‐Physical Transportation System: From Knowledge Elicitation to Meta‐Architecture Based on Clustering Algorithm

Transportation systems are increasingly evolving into highly integrated cyber‐physical environments that demand advanced coordination between computational intelligence and physical infrastructure. In response to growing requirements for sustainability, resilience and adaptability, this paper proposes a cyber‐physical transportation system (CPTS) framework that prioritises real‐time data‐driven decision‐making. The framework captures the dynamic interactions between cyber components (e.g., sensing, computation and communication) and physical subsystems (e.g., infrastructure, vehicles and users), thereby enabling continuous monitoring, anomaly detection and adaptive control. At its core lies a meta‐architectural design developed through structured knowledge elicitation and operationalised via a design structure matrix (DSM) clustering algorithm to manage system complexity and behavioural evolution. This approach strengthens the system's capacity to detect failures and uncover latent mobility demands, ultimately supporting strategic transportation planning. A real‐world case study validates the framework's effectiveness in guiding policymakers and stakeholders towards the creation of intelligent, sustainable and responsive transportation infrastructures. The findings underscore the transformative potential of CPTS in advancing transportation decision‐making through integrated cyber‐physical reasoning and adaptive system design.