As cities grow and populations age, traditional transportation systems struggle with inefficiency and lack of accessibility. The challenge is not just technological but social: how to integrate new mobility into the daily lives and values of diverse citizens without causing social friction.
Inefficient logistics and “last-mile” delivery.
Value proposition: It enables cities to move from “technology-first” planning to “human-value-first” implementation, reducing the risk of social rejection of new technologies while optimizing resource use.
Key functionalities: Human mobility prediction, data collection from mobile networks, multi-modal simulation, and social value evaluation.
Type of data / inputs used: GPS human flow data, workers’ status in logistics, and qualitative review data (for AR components).
Outputs produced: Predictive movement maps, digital twins of districts, and interactive AR exploration tools.
Innovation: The innovation lies in the transdisciplinary methodology—using “Onomatopoeia” and “Location AI” to capture the feel and flow of a city, not just traffic numbers.
Main use cases: Urban planning, logistics optimization, and tourism enhancement.
Deployment examples: Tested in logistics warehouses for productivity and in urban districts for AR-based exploration (onoMAtoPoeiAR).
Scale of application: Metropolitan and District levels.
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