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Clustering Users by Their Mobility Behavioral Patterns
13 references·4 citing·38 second hop
Clustering Users by Their Mobility Behavioral Patterns (2019)Clustering Users by Their Mobility Behavi…Data from mobile phone operators: A tool for smarter cities? (2014)Data from mobile phone operat…Mining user similarity based on routine activities (2013)Mining user similarity based …Periodicity Based Next Place Prediction (2012)Periodicity Based Next Place …A habit mining approach for discovering similar mobile users (2012)A habit mining approach for d…Location-Based Social Networks: Users (2011)Location-Based Social Network…Extracting urban patterns from location-based social networks (2011)Extracting urban patterns fro…CLR (2011)CLRLearning Location Correlation from GPS Trajectories (2010)Learning Location Correlation…Mining Individual Life Pattern Based on Location History (2009)Mining Individual Life Patter…Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…Using GPS to learn significant locations and predict movement across multiple users (2003)Using GPS to learn significan…Context-Based Statistical Process Control (2003)Context-Based Statistical Pro…GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation (2025)GeoAvatar: A big mobile phone…Unlocking social network analysis methods for studying human mobility (2022)Unlocking social network anal…Graph-based mobility profiling (2022)Graph-based mobility profilingA Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study (2022)A Cluster-Based Approach Usin…The Global City (1991)Understanding individual human mobility patterns (2008)The Rise of the Network Society (2009)Big data and understanding change in the context of planning transport systems (2018)Changes in urban mobility in Sapporo city, Japan due to the Covid-19 emergency declarations (2020)New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data (2020)Reality mining: sensing complex social systems (2005)Understanding mobility based on GPS data (2008)Inferring friendship network structure by using mobile phone data (2009)An Advanced Systematic Literature Review on Spatiotemporal Analyses of T witter Data (2015)Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data (2015)Mobile Targeting Using Customer Trajectory Patterns (2016)Computational Social Science (2009)Spatio-Temporal Routine Mining on Mobile Phone Data (2018)Temporal and Spatial Variation Characteristics of Catering Facilities Based on POI Data: A Case Study within 5th Ring Road in Beijing (2018)Uncovering spatiotemporal and semantic aspects of tourists mobility using social sensing (2020)The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences (2002)Inferring High-Level Behavior from Low-Level Sensors (2003)Predestination: Inferring Destinations from Partial Trajectories (2006)Learning and inferring transportation routines (2007)What's Your Next Move: User Activity Prediction in Location-based Social Networks (2013)Analysis of human mobility patterns from GPS trajectories and contextual information (2015)Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data (2017)Digital Footprinting: Uncovering Tourists with User-Generated Content (2008)When a city tells a story (2012)You are where you e-mail (2012)Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm (2015)The Massachusetts Institute of Technology (1999)Mining large-scale smartphone data for personality studies (2011)Measuring Large-Scale Social Networks with High Resolution (2014)Location-Based Social Networks (2014)Spatiotemporal Sequential Influence Modeling for Location Recommendations (2015)A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media (2020)Location-Based Social Networks: Locations (2011)Capturing urban recreational hotspots from GPS data: A new framework in the lens of spatial heterogeneity (2023)Bridging the gap between physical location and online social networks (2010)Predictability of population displacement after the 2010 Haiti earthquake (2012)A survey of results on mobile phone datasets analysis (2015)
this work references cited by second hop

References

  • 2003Using GPS to learn significant locations and predict movement across multiple users1,060 cited
  • 2009Eigenbehaviors: identifying structure in routine504 cited
  • 2009Eigenbehaviors: identifying structure in routine439 cited
  • 2009Mining Individual Life Pattern Based on Location History281 cited
  • 2011Location-Based Social Networks: Users187 cited
  • 2014Data from mobile phone operators: A tool for smarter cities?184 cited
  • 2011Extracting urban patterns from location-based social networks147 cited
  • 2011CLR139 cited
  • 2010Learning Location Correlation from GPS Trajectories76 cited
  • 2013Mining user similarity based on routine activities65 cited
  • 2003Context-Based Statistical Process Control55 cited
  • 2012A habit mining approach for discovering similar mobile users53 cited
  • 2012Periodicity Based Next Place Prediction32 cited

Cited by

  • 2022Graph-based mobility profiling23 cited
  • 2022A Cluster-Based Approach Using Smartphone Data for Bike-Sharing Docking Stations Identification: Lisbon Case Study19 cited
  • 2025

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GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation
10 cited
  • 2022Unlocking social network analysis methods for studying human mobility8 cited