In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.

The advent of GPS technologies generates location data-streams and accentuates the importance of developing practical tools that can process and analyze the vast amounts of location data at a given moment in a meaningful way. Profiling the trajectory of a moving object with respect to the trajectories of its surrounding objects, for example, can elicit its mobility behaviour and analyze it in order to inform domain experts with critical knowledge in real time. For instance, clustering multiple moving objects with respect to their spatial and temporal dimension to identify co-movement patterns. In this paper, we propose a novel graph-based online co-movement pattern mining algorithm, called EvolvingClusters, which can be used to discover different collective movement behaviours (like the well-known flocks and convoys) in a unified way based on the activity of multiple concurrent objects through time and space. We evaluate EvolvingClusters using real-world and synthetic datasets from multiple mobility domains. Our study demonstrates the effectiveness of the proposed algorithm as well as its value towards a tool to profile semantically rich behaviour and with capabilities to observe and categorize multiple moving objects in real-time.