Road infrastructure constitutes a complex system characterized by multiple interacting subsystems. A comprehensive understanding of the correlations and dependencies among various road infrastructure elements is essential for enhancing infrastructure management and maintenance by providing a robust foundation for decision support. Digital Twins (DT) are recognized as effective tools for facilitating such decision-making. However, the development of comprehensive DTs considering road infrastructure in its entirety is still in its early stages. Hence, this paper focuses on the conceptualization of a digital representation of road infrastructure that enables the evaluation of relationships between the various heterogeneous subsystems. To accomplish this, we apply the systems-of-systems principle to road infrastructure. At its core, Labeled Property Graphs (LPG) are employed to capture intra-subsystem relationships and inter-system linkages, facilitating a holistic representation of interactions. Furthermore, we acknowledge the current organizational status of distributed responsibilities resulting in distributed data storage and maintenance by using the concept of federated databases. The presented approach enables multi-scale evaluations of relations among road infrastructure elements while preserving the system’s scalability and the distributed management of infrastructure data. Thus, previously separate data sets can be evaluated in relation to each other on a big scale. Doing so, the presented concept provides a foundation for extensive correlation studies between different heterogeneous infrastructure datasets. The concept is validated by applying it to a large-scale real-world data set stemming from multiple Bavarian road authorities, transferring into the proposed graph structure, and demonstrating the gained capabilities through cross-domain queries and analysis. • Concept for representing road infrastructure as systems-of-systems using graph-based methods. • A proposal for context-based linking of heterogeneous infrastructure data models • Concept for evaluation of spatial relations between different infrastructure assets on multiple scales. • Consideration of distributed data storage and maintenance of road infrastructure data. • Validation through large-scale real-world dataset spanning multiple road authorities in Bavaria.