A Reference Use Case, Data Space Architecture, and Prototype for Smart Truck Parking.

The logistics sector experiences an increasing shortage of secure truck parkings, in part due to lacking insight into occupancy rates. Extending earlier work – in which a variety of machine learning approaches are evaluated to predict truck parking occupancy – this paper presents a reference use case, data space architecture, and prototype for smart truck parking. The research builds upon case study research and 1.5 years’ worth of real-world data of a truck parking in Deventer, the Netherlands. Deploying the conceptual design from prior work, the main contributions of this paper are: (1) a reference use case, (2) a data space architecture, and (3) a prototype for smart truck parking. The reference use case is developed following the use case methodology of Fraunhofer. The architecture is rooted in the international data spaces reference architecture model, modelled using the ArchiMate language and specification, and validated by means of expert opinion. The cross industry standard process for data mining is utilized to develop and deploy a prototype, based on a single case mechanism experiment, for a smart truck parking application. Future research can focus on evaluating and developing ontologies to extend the current conceptual representation of the data objects and the experimental development of federative machine learning approaches among truck parkings.