Smart parking systems (SPS) represent an evolving and heterogeneous field of approaches and applications in parking management. One commonality is that all present systems deal with digital data related to the parking domain, such as data about parking infrastructure, parking demand, transactions, and similar. Data offerings of SPS seem to provide essential benefits for actors in parking space management, as long as they can be discovered and assessed efficiently. This paper presents mechanisms for the discovery and assessment of SPS data offerings. First, a taxonomy is developed via an inductive approach, based on a review of existing approaches to categorizing such data offerings. The taxonomy represents a hierarchical classification system, looking at functional, technical, and content perspectives of SPS data. This taxonomy is further integrated and formalized into a metadata model, allowing structured and harmonized descriptions about data offerings of individual SPS. The metadata model is built on established metadata frameworks, namely the Resource Description Framework (RDF). For reasons of reusability and interoperability, it also adopts existing metadata vocabularies from the domain of internet data catalogs. This work intends to make the data offerings of SPS assessable and comparable for potential SPS users, namely actors in parking space management. It also provides a foundation for integrating the various forms and technologies of current SPS deployments. Such integration is missing so far, according to some other authors, and is addressed in this work by an interoperable metadata approach.
Parking space management via dynamic performance-based pricing
