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Title: Individual mobility in context : from high resolution trajectories to social behaviour
Author: Alessandretti, L.
ISNI:       0000 0004 7426 7509
Awarding Body: City, University of London
Current Institution: City, University of London
Date of Award: 2018
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Understanding human mobility can help creating solutions to society-wide issues, from urban planning and traffic forecasting, to the modelling of epidemics. Existing studies have shown that knowledge on how single individuals take spatial decisions is fundamental for modelling collective mobility patterns. However, individual mobility remains poorly understood, also due to the lack of suitable data. In this thesis, we use novel datasets to characterize and model mobility in relation to other individual aspects: social behaviour, personality, and demographic attributes. Our study focuses on mobility across unprecedented spatial ranges, from ~ 10 m to ~ 10000 Km, and temporal scales, from seconds to years.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA Mathematics