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Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks
16 references·1 citing·32 second hop
Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks (2019)Discovering Urban Travel Demands Through …Exploring Millions of Footprints in Location Sharing Services (2021)Exploring Millions of Footpri…Spatiotemporal Sequential Influence Modeling for Location Recommendations (2015)Spatiotemporal Sequential Inf…CEPR (2015)CEPRLocation-Based Social Networking Data (2014)Location-Based Social Network…Residential location and daily travel distances: the influence of trip purpose (2013)Residential location and dail…Exploring temporal effects for location recommendation on location-based social networks (2013)Exploring temporal effects fo…Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering regions of differ…A universal model for mobility and migration patterns (2012)A universal model for mobilit…Friendship and mobility (2011)Friendship and mobilityReal-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Real-Time Urban Monitoring Us…A consolidated model of trip distribution (2009)A consolidated model of trip …An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables (2004)An Efficient Algorithm for Re…Travel estimation techniques for urban planning (1998)Travel estimation techniques …GRAVITY MODELS AND TRIP DISTRIBUTION THEORY (1959)GRAVITY MODELS AND TRIP DISTR…Intervening Opportunities: A Theory Relating Mobility and Distance (1940)Intervening Opportunities: A …Principles of social science (1888)Principles of social scienceRePiDeM: A Refined POI Demand Modeling based on Multi-Source Data (2020)RePiDeM: A Refined POI Demand…Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges (2016)Location-based advertising on mobile devices (2016)A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories (2017)An Empirical Study of Geographic User Activity Patterns in Foursquare (2021)Growth, innovation, scaling, and the pace of life in cities (2007)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Geo-located Twitter as proxy for global mobility patterns (2014)A survey of results on mobile phone datasets analysis (2015)Birds of a Feather: Homophily in Social Networks (2001)Power-Law Distributions in Empirical Data (2009)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016)DeepMove (2018)Power-law distributions in empirical data (2018)The scaling laws of human travel (2006)A METHOD OF FITTING THE GRAVITY MODEL BASED ON THE POISSON DISTRIBUTION* (1982)Modeling social influence through network autocorrelation: constructing the weight matrix (2002)Human mobility: Models and applications (2018)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Push-Pull Migration Laws (1983)Spatial interaction modelling (2003)Social Media Fingerprints of Unemployment (2015)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017)The Intra-Urban Migration Process: A Perspective (1970)Research on Internal Migration in the United States: A Survey (2016)Social physics (2022)Traffic Generated by Mixed-Use Developments—Six-Region Study Using Consistent Built Environmental Measures (2010)Nonwork Accessibility as a Social Equity Indicator (2013)Transportation planning methodologies for post-disaster recovery in regional communities: the East Japan Earthquake and tsunami 2011 (2013)
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References

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  • 2012Discovering regions of different functions in a city using human mobility and POIs1,136 cited
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  • 2021Exploring Millions of Footprints in Location Sharing Services685 cited
  • 2010Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome623 cited
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  • 2015Spatiotemporal Sequential Influence Modeling for Location Recommendations82 cited
  • 2009A consolidated model of trip distribution42 cited
  • 2014

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Location-Based Social Networking Data
38 cited

Cited by

  • 2020RePiDeM: A Refined POI Demand Modeling based on Multi-Source Data8 cited