Mobility Compass

• Bonavita, Agnese
• Guidotti, Riccardo
• Nanni, Mirco

Abstract

Classifying cities and other geographical units is a classical task in urban geography, typically carried out through manual analysis of specific characteristics of the area. The primary objective of this paper is to contribute to this process through the definition of a wide set of city indicators that capture different aspects of the city, mainly based on human mobility and automatically computed from a set of data sources, including mobility traces and road networks. The secondary objective is to prove that such set of characteristics is indeed rich enough to support a simple task of geographical transfer learning, namely identifying which groups of geographical areas can share with each other a basic traffic prediction model. The experiments show that similarity in terms of our city indicators also means better transferability of predictive models, opening the way to the development of more sophisticated solutions that leverage city indicators.

Topics

• learning
• data
• human being
• definition
• geography
• road network
• forecasting model
• city
• indicating instrument
• experiment
• mining
• data mining
• manual
• traffic forecasting
• mobility data
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