A lot of processes affect lifestyle in an urban area, above all in a huge area that of Milan that represents, in terms of inhabitants, one of the biggest European metropolitan areas. In particular, working, residential, and mobility activities can be indicated as crucial for the well-being of the city. However, these processes are difficult to evaluate directly. Then, undirected instruments for the evaluation of these activities can be taken into account. In this chapter, we analyze a mobile phone network dataset in order to retrieve meaningful features of urban dynamics. These features can be exploited for urban planning procedures as, for instance, the implementation of a car sharing system. We perform a statistical analysis, based on Blind Source Separation techniques on a dataset that measures the intensity of mobile phone activity over the city of Milan varying along two weeks. Blind Source Separation techniques allow to extrapolate significant sources from raw data and to associate each source to a specific urban behavior.

Analysis of Mobile Phone Data for Deriving City Mobility Patterns

Secchi, Piercesare;Vantini, Simone;Zanini, Paolo
2017

Abstract

A lot of processes affect lifestyle in an urban area, above all in a huge area that of Milan that represents, in terms of inhabitants, one of the biggest European metropolitan areas. In particular, working, residential, and mobility activities can be indicated as crucial for the well-being of the city. However, these processes are difficult to evaluate directly. Then, undirected instruments for the evaluation of these activities can be taken into account. In this chapter, we analyze a mobile phone network dataset in order to retrieve meaningful features of urban dynamics. These features can be exploited for urban planning procedures as, for instance, the implementation of a car sharing system. We perform a statistical analysis, based on Blind Source Separation techniques on a dataset that measures the intensity of mobile phone activity over the city of Milan varying along two weeks. Blind Source Separation techniques allow to extrapolate significant sources from raw data and to associate each source to a specific urban behavior.
Electric Vehicle Sharing Services for Smarter Cities
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1036101
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