To engineer the modern large, complex, interdisciplinary systems-of-systems (SoS), the collaborative world teams must “speak” the same language and must work on the same “matter.” The “matter” is the system model and the communication mechanisms must be supported by standard, flexible, and friendly modeling languages. The evolving model-based systems engineering (MBSE) approach is leading the way and is expected to become a standard practice in the field of systems engineering (SE) in the next decade. As an emerging paradigm for the systems of the 21st century, it seems useful to overview its current state of the art concerning the developing standards, the embryonic formalisms, the available modeling languages, the methodologies, and the major applications.

Estimation/updating of origin-destination (OD) flows and other traffic state parameters is a classical, widely adopted procedure in transport engineering, both in off-line and in on-line contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks – except from closed highway systems – thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both off-line/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sourcesPeer ReviewedPostprint (author's final draft