A real case: how to use micromobility data to decide where to install or relocate parking for bicycles and e-scooters.
With Urbiverse – SWITCH’s AI simulation platform – the City of Turin was able to move from “gut-driven” decisions to a data-driven strategy for bicycle and e-scooter parking. The tool helped the administration analyze real usage patterns to identify exactly where infrastructure was missing, leading them to win the SMAU Innovation Award. By comparing actual parking behavior against the existing rack network, Turin identified critical gaps to make the city tidier, safer, and more accessible.
This case study is for city planners, public administrators, and mobility managers who need to optimize urban space and justify infrastructure investments with objective data.
In a few pages, you will see how transforming raw data into simple “Intensity” and “Frequency” indices can resolve parking disorder, support transparent public spending, and guide precise urban interventions.
What you will learn inside
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How Turin won the SMAU Innovation Award by adopting an objective, data-first approach to urban planning.
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How Urbiverse identified the 4 critical areas where parking supply was insufficient relative to real demand.
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How to use the PI1 (Intensity) and PI2 (Frequency) indices to detect overcrowding and recurring hot spots.
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How to perform a Gap Analysis by overlaying real micromobility flows onto the existing map of bike racks.
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How to turn analysis into action, from prioritizing specific street cells to planning modular installations that reduce sidewalk clutter.
Download the full case study
