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Exploring dynamic urban mobility patterns from traffic flow data using community detection
16 references·0 citing·46 second hop
Exploring dynamic urban mobility patterns from traffic flow data using community detection (2024)Exploring dynamic urban mobility patterns…The Effects of Pedestrian Environments on Walking Behaviors and Perception of Pedestrian Safety (2021)The Effects of Pedestrian Env…Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage (2018)Do different datasets tell th…Tourist Activity Analysis by Leveraging Mobile Social Media Data (2017)Tourist Activity Analysis by …Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Coupling mobile phone and soc…Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing (2015)Combining smart card data and…Geo-located Twitter as proxy for global mobility patterns (2014)Geo-located Twitter as proxy …Detecting the dynamics of urban structure through spatial network analysis (2014)Detecting the dynamics of urb…An Analysis of Visitors' Behavior in the Louvre Museum: A Study Using Bluetooth Data (2014)An Analysis of Visitors' Beha…Participatory bluetooth sensing: A method for acquiring spatio-temporal data about participant mobility and interactions at large scale events (2013)Participatory bluetooth sensi…Understanding intra-urban trip patterns from taxi trajectory data (2012)Understanding intra-urban tri…Reconstructing Gravitational Attractions of Major Cities in China from Air Passenger Flow Data, 2001–2008: A Particle Swarm Optimization Approach (2012)Reconstructing Gravitational …Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data (2012)Extracting Dynamic Urban Mobi…Analysing spatiotemporal sequences in Bluetooth tracking data (2012)Analysing spatiotemporal sequ…Does Urban Mobility Have a Daily Routine? 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A methodological review of sustainable tourism (2019)Exploring Millions of Footprints in Location Sharing Services (2021)Forecasting daily attraction demand using big data from search engines and social media (2021)Exploratory calibration of a spatial interaction model using taxi GPS trajectories (2011)Mining spatiotemporal patterns of urban dwellers from taxi trajectory data (2015)Time-evolving O-D matrix estimation using high-speed GPS data streams (2015)Spatial variations in urban public ridership derived from GPS trajectories and smart card data (2018)Portraying the spatial dynamics of urban vibrancy using multisource urban big data (2019)
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References

  • 2014Geo-located Twitter as proxy for global mobility patterns675 cited
  • 2014Detecting the dynamics of urban structure through spatial network analysis523 cited
  • 2012Understanding intra-urban trip patterns from taxi trajectory data327 cited
  • 2017Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns298 cited
  • 2010Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks266 cited
  • 2015Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing265 cited
  • 2009Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data257 cited
  • 2014An Analysis of Visitors' Behavior in the Louvre Museum: A Study Using Bluetooth Data182 cited
  • 2009Mining time-dependent attractive areas and movement patterns from taxi trajectory data122 cited
  • 2017Tourist Activity Analysis by Leveraging Mobile Social Media Data116 cited
  • 2012Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data112 cited
  • 2018Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage111 cited
  • 2012Analysing spatiotemporal sequences in Bluetooth tracking data91 cited
  • 2021The Effects of Pedestrian Environments on Walking Behaviors and Perception of Pedestrian Safety

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63 cited
  • 2012Reconstructing Gravitational Attractions of Major Cities in China from Air Passenger Flow Data, 2001–2008: A Particle Swarm Optimization Approach57 cited
  • 2013Participatory bluetooth sensing: A method for acquiring spatio-temporal data about participant mobility and interactions at large scale events50 cited
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