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Identification method of residents' medical travel behavior characteristics driven by mobile signaling data: A Case Study of Kunshan
3 references·0 citing·16 second hopsampled
Identification method of residents' medical travel behavior characteristics driven by mobile signaling data: A Case Study of Kunshan (2020)Identification method of residents' medic…Data from mobile phone operators: A tool for smarter cities? (2014)Data from mobile phone operat…Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Estimating Origin-Destination…Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data (2009)Daily rhythms of suburban com…The Global City (1991)Understanding individual human mobility patterns (2008)The Rise of the Network Society (2009)Applying mobile phone data to travel behaviour research: A literature review (2017)Smart city as a tool for sustainable mobility and transport decarbonisation (2017)Smart cities with big data: Reference models, challenges, and considerations (2018)A Theoretical Foundation for the Gravity Equation. (1979)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Social Sensing: A New Approach to Understanding Our Socioeconomic Environments (2015)Human mobility: Models and applications (2018)Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set (2000)Observing the rhythms of daily life: A six-week travel diary (2002)Understanding intra-urban trip patterns from taxi trajectory data (2012)Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai (2012)The use of tracking technologies in tourism research: the first decade (2016)
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References

  • 2011Estimating Origin-Destination Flows Using Mobile Phone Location Data545 cited
  • 2009Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data269 cited
  • 2014Data from mobile phone operators: A tool for smarter cities?192 cited

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