Spatiotemporal Patterns of Urban Human Mobility

The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit c...

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Veröffentlicht in:	Journal of statistical physics Jg. 151; H. 1-2; S. 304 - 318
Hauptverfasser:	Hasan, Samiul, Schneider, Christian M., Ukkusuri, Satish V., González, Marta C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.04.2013 Springer
Schlagworte:	Analysis Banks (Finance) Forecasts and trends Mathematical and Computational Physics Physical Chemistry Physics Physics and Astronomy Quantum Physics Statistical Physics and Dynamical Systems Theoretical Spatial networks Activity models Human mobility
ISSN:	0022-4715, 1572-9613
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Abstract	The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.
AbstractList	The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples' visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility. Keywords Human mobility * Spatial networks * Activity models The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples’ visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.
Audience	Academic
Author	Schneider, Christian M. Hasan, Samiul González, Marta C. Ukkusuri, Satish V.
Author_xml	– sequence: 1 givenname: Samiul surname: Hasan fullname: Hasan, Samiul organization: School of Civil Engineering, Purdue University – sequence: 2 givenname: Christian M. surname: Schneider fullname: Schneider, Christian M. organization: Department of Civil and Environmental Engineering, MIT – sequence: 3 givenname: Satish V. surname: Ukkusuri fullname: Ukkusuri, Satish V. organization: School of Civil Engineering, Purdue University – sequence: 4 givenname: Marta C. surname: González fullname: González, Marta C. email: martag@mit.edu organization: Department of Civil and Environmental Engineering, MIT
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Snippet	The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose...
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SubjectTerms	Analysis Banks (Finance) Forecasts and trends Mathematical and Computational Physics Physical Chemistry Physics Physics and Astronomy Quantum Physics Statistical Physics and Dynamical Systems Theoretical
Title	Spatiotemporal Patterns of Urban Human Mobility
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