Mobility in the urban and maritime domains hugely impacts the global economy, generating data at high rates from an increasing number of moving objects. Management of the complete lifecycle of such data implies that trustworthy and privacy-preserving infrastructures need to be put in place, so that reliable and secure data operations can be provided. Meanwhile, the mobility data exploitation has still a wide potential due to the emerging applications and the environmental footprint caused by mobility (e.g. carbon emissions, energy consumption). Motivated by these pressing needs, MobiSpaces delivers an end-to-end mobility-aware and mobility-optimized data governance platform with key differentiating factor that the outcomes of mobility analytics will be utilized to optimize the complete data path, in terms of efficient, reliable, secure, fair and trustworthy data processing. MobiSpaces promises the extraction of actionable insights from ubiquitous mobile sensor data and IoT devices in a decentralized way, offering intelligent transportation services, enforcing privacy constraints at the expected point of action. XAI techniques will be applied at the level of data management and machine learning, supporting the creation of comprehensive and interpretable prediction models, while all the research outcomes will be validated through five use cases: 1) intelligent public transportation services in urban environments, 2) intelligent infrastructure traffic sensing for smart cities, 3) vessel tracking for non-cooperative vessels, 4) decentralized processing on-board of vessels, and 5) enhanced nautical maps via crowdsourced bathymetry vessels data. These actions will be continuously adapted and monitored for their environmental sustainability, whereas MobiSpaces will create a widely accepted standard of data processing and analytics, alongside a data-rich ecosystem providing trustworthy and actionable data that is vital for enabling the growth of the EU digital economy.

CORDIS website : https://cordis.europa.eu/project/id/101070279

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