Extreme Scale Mobility Data Analytics (MDA) at the Compute Continuum (CC) involves advanced techniques for analysing large and diverse mobility data sets across the various CC levels, from Edge to Cloud. The project incorporates multiple analytics techniques, including ML and AI methods, to identify and predict mobility patterns across various use-cases, while also ensuring their applicability at the CC level that they mostly suited for. Examples include i) statistical and ML methods have been applied to Public Transport data, including trajectories and ticket validations, to predict exit stops of multi-leg or individual trips; ii) AI methods that can predict the demand for shared micro-mobility vehicles like scooters and e-bikes across various neighbouring districts; and iii) ML methods that can predict congestion across various highway road segments, with an emphasis on the segments where congestion happens organically and is not caused by easily identifable phenomena like traffic spillback or ongoing roadworks.
