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Improving next location prediction with inferred activity semantics in mobile phone data
16 references·0 citing·45 second hopsampled
Improving next location prediction with inferred activity semantics in mobile phone data (2025)Improving next location prediction with i…A survey on deep learning for human mobility (2023)A survey on deep learning for…Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis (2022)Understanding cycling distanc…Mobile phone location data for disasters: A review from natural hazards and epidemics (2022)Mobile phone location data fo…Individual Mobility Prediction in Mass Transit Systems Using Smart Card Data: An Interpretable Activity-Based Hidden Markov Approach (2021)Individual Mobility Predictio…Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions (2021)Analytics of location-based b…Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data (2020)Mining Daily Activity Chains …Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)Modeling real-time human mobi…DeepMove (2018)DeepMoveAnalyzing large-scale human mobility data: a survey of machine learning methods and applications (2018)Analyzing large-scale human m…Real-time trip purpose prediction using online location-based search and discovery services (2017)Real-time trip purpose predic…Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Coupling mobile phone and soc…A hybrid Markov-based model for human mobility prediction (2017)A hybrid Markov-based model f…Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach (2015)Understanding aggregate human…Predicting human mobility with activity changes (2015)Predicting human mobility wit…Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Inferring trip purposes and u…Inferring individual daily activities from mobile phone traces: A Boston example (2015)Inferring individual daily ac…Understanding individual human mobility patterns (2008)Modelling the scaling properties of human mobility (2010)Discovering regions of different functions in a city using human mobility and POIs (2012)TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data (2018)Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China (2020)How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics (2021)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? 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(1970)Big data: a revolution that will transform how we live, work, and think (2013)Street view imagery in urban analytics and GIS: A review (2021)Capturing urban recreational hotspots from GPS data: A new framework in the lens of spatial heterogeneity (2023)Understanding temporal and spatial patterns of urban activities across demographic groups through geotagged social media data (2023)Big data applications: overview, challenges and future (2024)17. A Value for n-Person Games (1953)Travel demand and the 3Ds: Density, diversity, and design (1997)Travel and the Built Environment (2010)Machine learning techniques for evaluating the nonlinear link between built-environment characteristics and travel behaviors: A systematic review (2023)Using machine-learning models to understand nonlinear relationships between land use and travel (2023)Examining active travel behavior through explainable machine learning: Insights from Beijing, China (2024)Urban Human Mobility (2019)Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA (2020)Trackintel: An open-source Python library for human mobility analysis (2023)scikit-mobility : A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data (2022)Future directions in human mobility science (2023)Human-AI coevolution (2024)
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References

  • 2018DeepMove714 cited
  • 2017Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns298 cited
  • 2023A survey on deep learning for human mobility282 cited
  • 2015Inferring trip purposes and uncovering travel patterns from taxi trajectory data254 cited
  • 2015Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach189 cited
  • 2018Analyzing large-scale human mobility data: a survey of machine learning methods and applications167 cited
  • 2022Mobile phone location data for disasters: A review from natural hazards and epidemics163 cited
  • 2018Modeling real-time human mobility based on mobile phone and transportation data fusion150 cited
  • 2022Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis150 cited
  • 2021Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions135 cited
  • 2015Inferring individual daily activities from mobile phone traces: A Boston example109 cited
  • 2017Real-time trip purpose prediction using online location-based search and discovery services97 cited
  • 2017A hybrid Markov-based model for human mobility prediction91 cited
  • 2015Predicting human mobility with activity changes

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60 cited
  • 2020Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data60 cited
  • 2021Individual Mobility Prediction in Mass Transit Systems Using Smart Card Data: An Interpretable Activity-Based Hidden Markov Approach49 cited
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