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Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data
3 references·0 citing·18 second hopsampled
Graph Convolutional Neural Networks for Human Activity Purpose Imputation from GPS-based Trajectory Data (2018)Graph Convolutional Neural Networks for H…TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data (2018)TripImputor: Real-Time Imputi…Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices (2015)Comparison of Travel Diaries …Annotating mobile phone location data with activity purposes using machine learning algorithms (2013)Annotating mobile phone locat…Understanding individual human mobility patterns (2008)Washington DC - USA (2008)Modelling the scaling properties of human mobility (2010)Using ensembles of decision trees to predict transport mode choice decisions: Effects on predictive success and uncertainty estimates (2014)Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem (2016)Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data (2019)Tracking survey for individual travel behaviour using mobile communication instruments (2004)Future Mobility Survey (2013)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Closer to the total? Long-distance travel of French mobile phone users (2017)Spatial context mining approach for transport mode recognition from mobile sensed big data (2017)Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics (2017)Fences and neighbours in the postmodern world: boundary narratives in political geography (1998)Detecting the dynamics of urban structure through spatial network analysis (2014)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes (2018)Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization (2019)Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China (2021)
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References

  • 2018TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data171 cited
  • 2013Annotating mobile phone location data with activity purposes using machine learning algorithms85 cited
  • 2015Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices80 cited

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