Harnessing the power of AI to elucidate mobile traffic consumption at city scale


Accurate and timely understanding of mobile traffic consumption patterns, especially at city scale, is becoming increasingly important in the planning of digital infrastructure that can meet the diversifying requirements of end-user applications, including e-healthcare, autonomous cars, and industrial automation. Mobile traffic insights can further inform the provisioning and optimisation of public transportation systems, to reduce both their carbon footprint and citizens' commute times. Nonetheless, in the context of the Covid pandemic, mobile traffic consumption can be also indicative of social events and interactions, hence a potentially useful signal in modelling disease spread. Gaining fine-grained knowledge from mobile network traffic is not straightforward. To begin with, this requires dedicated equipment (probes) for measurements collection, which runs e.g. on network gateways.

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