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Machine Learning Approach for Mobility Context Classification Using Radio Beacons
5 references·0 citing·15 second hop
Machine Learning Approach for Mobility Context Classification Using Radio Beacons (2023)Machine Learning Approach for Mobility Co…On the Predictability of a User's Next Check-in Using Data from Different Social Networks (2018)On the Predictability of a Us…Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment (2013)Using GPS Technology to Quant…Everyday mobility context classification using radio beacons (2013)Everyday mobility context cla…Learning and inferring transportation routines (2007)Learning and inferring transp…Inferring High-Level Behavior from Low-Level Sensors (2003)Inferring High-Level Behavior…Learning significant locations and predicting user movement with GPS (2003)Understanding mobility based on GPS data (2008)Learning transportation mode from raw gps data for geographic applications on the web (2008)PEIR, the personal environmental impact report, as a platform for participatory sensing systems research (2009)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2014)Using Google Location History data to quantify fine-scale human mobility (2018)Measuring spatio-temporal accessibility to emergency medical services through big GPS data (2019)Bayesian Forecasting and Dynamic Models (2nd edn) (1998)Using GPS to learn significant locations and predict movement across multiple users (2003)Reality mining: sensing complex social systems (2005)Action understanding as inverse planning (2009)Understanding Human Mobility from Twitter (2015)
this work references cited by second hop

References

  • 2007Learning and inferring transportation routines810 cited
  • 2003Inferring High-Level Behavior from Low-Level Sensors529 cited
  • 2013Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment252 cited
  • 2018On the Predictability of a User's Next Check-in Using Data from Different Social Networks6 cited
  • 2013Everyday mobility context classification using radio beacons3 cited

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