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Understanding Urban Human Mobility through Crowdsensed Data
9 references·15 citing·47 second hop
Understanding Urban Human Mobility through Crowdsensed Data (2018)Understanding Urban Human Mobility throug…Updating origin–destination matrices with aggregated data of GPS traces (2016)Updating origin–destination m…Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016)Inferring Passenger Travel De…Clustering Smart Card Data for Urban Mobility Analysis (2016)Clustering Smart Card Data fo…Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Activity-Based Human Mobility…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…Understanding urban human activity and mobility patterns using large-scale location-based data from online social media (2013)Understanding urban human act…Representation and communication (2013)Representation and communicat…Mining User Mobility Features for Next Place Prediction in Location-Based Services (2012)Mining User Mobility Features…Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)Estimation of a disaggregate …The movement pattern changes of population following a disaster: Example of the Aegean Sea earthquake of October 2020 (2024)The movement pattern changes …Quantifying spatial interaction centrality in urban population mobility: A mobility feature- and network topology-based locational measure (2024)Quantifying spatial interacti…Generative Models for Synthetic Urban Mobility Data: A Systematic Literature Review (2023)Generative Models for Synthet…A two-stage stochastic programming approach for dynamic OD estimation using LBSN data (2023)A two-stage stochastic progra…Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition (2022)Uncovering the spatiotemporal…Novel model for predicting individuals’ movements in dynamic regions of interest (2022)Novel model for predicting in…Nation-wide human mobility prediction based on graph neural networks (2021)Nation-wide human mobility pr…MetaMove: On Improving Human Mobility Classification and Prediction via Metalearning (2021)MetaMove: On Improving Human …Uber Movement Data: A Proxy for Average One-way Commuting Times by Car (2020)Uber Movement Data: A Proxy f…Ranking places in attributed temporal urban mobility networks (2020)Ranking places in attributed …Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data (2020)Multiple-Perspective Clusteri…Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China (2020)Machine Learning Approach to …PmA: a solution for people mobility monitoring and analysis based on WiFi probes (2019)PmA: a solution for people mo…Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities (2019)Crowdsensed Data Learning-Dri…A Generic Future Mobility Sensing System for Travel Data Collection, Management, Fusion, and Visualization (2019)A Generic Future Mobility Sen…Smart card data use in public transit: A literature review (2011)Detecting the dynamics of urban structure through spatial network analysis (2014)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Assessing social anxiety using gps trajectories and point-of-interest data (2016)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)Power-Law Distributions in Empirical Data (2009)Friendship and mobility (2011)SoCoMo marketing for travel and tourism: Empowering co-creation of value (2015)A Survey of Location Prediction on Twitter (2018)Power-law distributions in empirical data (2018)Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019)The Mode of Information: Poststructuralism and Social Context. 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(1970)Travel and the Built Environment (2010)On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)Human mobility and socioeconomic status: Analysis of Singapore and Boston (2018)Measuring inequality in community resilience to natural disasters using large-scale mobility data (2021)Modelling the scaling properties of human mobility (2010)The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis (2021)Learning Daily Human Mobility with a Transformer-Based Model (2024)From biases to opportunities: leveraging Location-Based-Service (LBS) data for next-generation transportation planning (2025)MobilityGPT: Enhanced Human Mobility Modeling With a GPT Model (2025)Urban activity pattern classification using topic models from online geo-location data (2014)Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China (2018)Nonlinear and synergistic effects of TOD on urban vibrancy: Applying local explanations for gradient boosting decision tree (2021)
this work references cited by second hop

References

  • 2012Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile528 cited
  • 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore472 cited
  • 2012Mining User Mobility Features for Next Place Prediction in Location-Based Services336 cited
  • 2013Understanding urban human activity and mobility patterns using large-scale location-based data from online social media315 cited
  • 2013Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment246 cited
  • 2016Clustering Smart Card Data for Urban Mobility Analysis214 cited
  • 2016Updating origin–destination matrices with aggregated data of GPS traces99 cited
  • 2016Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal90 cited
  • 2013Representation and communication78 cited

Cited by

  • 2023Generative Models for Synthetic Urban Mobility Data: A Systematic Literature Review39 cited
  • 2021Nation-wide human mobility prediction based on graph neural networks25 cited
  • 2019PmA: a solution for people mobility monitoring and analysis based on WiFi probes23 cited
  • 2024Quantifying spatial interaction centrality in urban population mobility: A mobility feature- and network topology-based locational measure

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21 cited
  • 2019A Generic Future Mobility Sensing System for Travel Data Collection, Management, Fusion, and Visualization19 cited
  • 2022Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition14 cited
  • 2023A two-stage stochastic programming approach for dynamic OD estimation using LBSN data14 cited
  • 2020Ranking places in attributed temporal urban mobility networks14 cited
  • 2019Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities14 cited
  • 2020Uber Movement Data: A Proxy for Average One-way Commuting Times by Car14 cited
  • 2021MetaMove: On Improving Human Mobility Classification and Prediction via Metalearning12 cited
  • 2020Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data10 cited
  • 2022Novel model for predicting individuals’ movements in dynamic regions of interest8 cited
  • 2024The movement pattern changes of population following a disaster: Example of the Aegean Sea earthquake of October 20207 cited
  • 2020Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China7 cited