Spatiotemporal Patterns of Urban Human Mobility
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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.
KeywordsHuman mobility Spatial networks Activity models
The Oyster card anonymous data was collected by Transport for London (TfL) for operational purposes, and we are grateful for their permission to use it in this paper. We also thank Prof. Nigel Wilson and Michael Frumin of MIT Transit Research Group for providing us the data; Prof. Chris Magee and Dr. Daniel Whitney for giving useful comments in the initial stage of this project.
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