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TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation
7 references·0 citing·29 second hopsampled
TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation (2023)TrajSGAN: A Semantic-Guiding Adversarial …A survey on deep learning for human mobility (2023)A survey on deep learning for…Trip Purposes Mining From Mobile Signaling Data (2021)Trip Purposes Mining From Mob…A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices (2021)A Semisupervised End-to-End F…What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities (2020)What is the Human Mobility in…Learning to Simulate Human Mobility (2020)Learning to Simulate Human Mo…A Generative Model of Urban Activities from Cellular Data (2017)A Generative Model of Urban A…The TimeGeo modeling framework for urban mobility without travel surveys (2016)The TimeGeo modeling framewor…Citizens as sensors: the world of volunteered geography (2007)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Human mobility and socioeconomic status: Analysis of Singapore and Boston (2018)Urban mobility and neighborhood isolation in America’s 50 largest cities (2018)Friendship and mobility (2011)Transport modelling in the age of big data (2017)Rethinking Behaviors and Activities of Base Stations in Mobile Cellular Networks Based on Big Data Analysis (2020)Location-Based Services for Mobile Telephony: a study of users' privacy concerns (2003)Discovering Urban Functional Zones Using Latent Activity Trajectories (2014)DeepMove (2018)Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning (2020)Inferring Origin-Destination Flows from Population Distribution (2021)GODDAG: Generating Origin-Destination Flow for New Cities Via Domain Adversarial Training (2023)Understanding mobility based on GPS data (2008)Activity Trajectory Generation via Modeling Spatiotemporal Dynamics (2022)Future directions in human mobility science (2023)Using human mobility data to quantify experienced urban inequalities (2025)Learning transportation mode from raw gps data for geographic applications on the web (2008)Inferring transportation modes from GPS trajectories using a convolutional neural network (2017)A Deep Semi-Supervised Machine Learning Algorithm for Detecting Transportation Modes Based on Gps Tracking Data (2022)GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification (2022)A hybrid method for intercity transport mode identification based on mobility features and sequential relations mined from cellular signaling data (2024)The Gravity Model (2011)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Spatial–Temporal Convolutional Model for Urban Crowd Density Prediction Based on Mobile-Phone Signaling Data (2021)Unveiling mobility patterns beyond home/work activities: A topic modeling approach using transit smart card and land-use data (2024)scikit-mobility : A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data (2022)Human-AI coevolution (2024)
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References

  • 2016The TimeGeo modeling framework for urban mobility without travel surveys295 cited
  • 2023A survey on deep learning for human mobility282 cited
  • 2017A Generative Model of Urban Activities from Cellular Data185 cited
  • 2020Learning to Simulate Human Mobility159 cited
  • 2020What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities54 cited
  • 2021Trip Purposes Mining From Mobile Signaling Data40 cited
  • 2021A Semisupervised End-to-End Framework for Transportation Mode Detection by Using GPS-Enabled Sensing Devices32 cited

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