Mobility Profiling

Mobility Profiling

Mirco Nanni (KDD Lab, ISTI-CNR, Italy), Roberto Trasarti (ISTI-CNR, Italy), Paolo Cintia (ISTI-CNR, Italy), Barbara Furletti (ISTI-CNR, Italy), Chiara Renso (ISTI-CNR, Italy), Lorenzo Gabrielli (ISTI-CNR, Italy), Salvatore Rinzivillo (ISTI-CNR, Italy) and Fosca Giannotti (ISTI-CNR, Italy)
Copyright: © 2014 |Pages: 29
DOI: 10.4018/978-1-4666-4920-0.ch001
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Abstract

The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network.
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Introduction

The ability to understand dynamics of human mobility is crucial for tasks like urban planning and transportation management. The rapidly growing of large spatio-temporal datasets gives us the possibility to develop always more sophisticated and accurate methods and algorithms, going towards a complete view on mobility. Private and public institutions have been recognized mobility data as a source of information to assess the lifestyle, habits and demands of citizens in terms of mobility.

The traditional approaches in this field mainly focuses on inferring simple measurements and aggregations, such as density of traffic and car flows in road segments: mobility profiles are an evolution, in terms of richness of information, of those results. Profiles lie in between single trajectories and a whole population, i.e. the individual person, with his regularities and habits. Analysing individuals, rather than large groups, provides the basis for an understanding of systematic mobility, which is fundamental in some mobility planning applications.

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