Development of an Integrated Intelligent Transportation Systems–Digital Twin Framework for Smart Mobility

Urban mobility systems are under increasing pressure from rapid urbanisation, population growth, and rising vehicle ownership, which contribute to recurrent congestion, unreliable travel times, environmental burdens, and declining service quality. Intelligent transportation systems (ITSs) support real-time sensing, communication, and traffic management, while digital twin (DT) technology provides continuously updated virtual representations that can support simulation, prediction, and decision support. In this study, a five-layer ITS–DT framework that links sensing and acquisition, communication and connectivity, data integration and digital twin synchronisation, analytics and intelligence, and operations and governance is developed. A structured review of literature published mainly between 2020 and 2025, a conceptual synthesis, and a prototype numerical experiment are undertaken. The proposed contribution is not the invention of the individual technologies, but their explicit cross-layer integration through a bidirectional data-and-control pipeline, interoperable interfaces, edge-to-cloud computing, and embedded governance. In a synthetic four-intersection corridor experiment with 50 paired stochastic replications, adaptive signal control reduced mean accrued queueing delay from 61.87 to 52.03 s per generated vehicle (15.9%) and the mean maximum approach queue from 84.28 to 29.52 vehicles (65.0%), while completed network exits increased by 0.7%. These prototype results demonstrate internal operational feasibility under stated assumptions rather than real-world city-scale validity. The framework therefore provides a transparent basis for the subsequent calibration, field validation, and context-specific implementation of integrated smart mobility systems.