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Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces
12 references·2 citing·37 second hop
Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces (2016)Minimizing User Involvement for Learning …Methodologies for Cross-Domain Data Fusion: An Overview (2015)Methodologies for Cross-Domai…The Mobile Data Challenge: Big Data for Mobile Computing Research (2012)The Mobile Data Challenge: Bi…Inferring land use from mobile phone activity (2012)Inferring land use from mobil…Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering regions of differ…Beyond "local", "categories" and "friends" (2012)Beyond "local", "categories" …Friendship and mobility (2011)Friendship and mobilityDiscovering routines from large-scale human locations using probabilistic topic models (2011)Discovering routines from lar…Limits of Predictability in Human Mobility (2010)Limits of Predictability in H…Understanding individual human mobility patterns (2009)Understanding individual huma…Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…Understanding individual human mobility patterns (2008)Understanding individual huma…A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling (2021)A Survey of Open Source User …Motion Based Inference of Social Circles via Self-Attention and Contextualized Embedding (2019)Motion Based Inference of Soc…Inferring friendship network structure by using mobile phone data (2009)Find me if you can (2010)Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014)Big data analytics: a survey (2015)A survey towards an integration of big data analytics to big insights for value-creation (2018)Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Data fusion (2009)Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2017)Machine Learning With Big Data: Challenges and Approaches (2017)Predicting citywide crowd flows using deep spatio-temporal residual networks (2018)The origin of bursts and heavy tails in human dynamics (2005)Modeling bursts and heavy tails in human dynamics (2006)The scaling laws of human travel (2006)Computational Social Science (2009)Simulating dynamical features of escape panic (2000)Reality mining: sensing complex social systems (2005)A universal model for mobility and migration patterns (2012)Smart cities of the future (2012)Travel demand and the 3Ds: Density, diversity, and design (1997)Urban Computing (2014)Social Sensing: A New Approach to Understanding Our Socioeconomic Environments (2015)The Death and Life of Great American Cities (2015)Natural Scales in Geographical Patterns (2017)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)Power-law distributions in empirical data (2018)Driving with knowledge from the physical world (2011)Measuring social functions of city regions from large-scale taxi behaviors (2011)Urban computing with taxicabs (2011)Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model (2016)Using GPS to learn significant locations and predict movement across multiple users (2003)Learning and inferring transportation routines (2007)The Massachusetts Institute of Technology (1999)Modelling the scaling properties of human mobility (2010)Human mobility: Models and applications (2018)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,185 cited
  • 2010Limits of Predictability in Human Mobility3,282 cited
  • 2011Friendship and mobility2,972 cited
  • 2012Discovering regions of different functions in a city using human mobility and POIs1,151 cited
  • 2009Understanding individual human mobility patterns532 cited
  • 2009Eigenbehaviors: identifying structure in routine504 cited
  • 2015Methodologies for Cross-Domain Data Fusion: An Overview454 cited
  • 2009Eigenbehaviors: identifying structure in routine449 cited
  • 2012The Mobile Data Challenge: Big Data for Mobile Computing Research429 cited
  • 2012Inferring land use from mobile phone activity313 cited
  • 2011Discovering routines from large-scale human locations using probabilistic topic models248 cited
  • 2012Beyond "local", "categories" and "friends"63 cited

Cited by

  • 2019Motion Based Inference of Social Circles via Self-Attention and Contextualized Embedding8 cited
  • 2021A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling3 cited

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