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Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks
16 references·3 citing·41 second hop
Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks (2024)Generating sparse origin–destination flow…Deep trip generation with graph neural networks for bike sharing system expansion (2023)Deep trip generation with gra…Modeling bike-sharing demand using a regression model with spatially varying coefficients (2021)Modeling bike-sharing demand …Health impacts of bike-sharing systems in the U.S. (2021)Health impacts of bike-sharin…A Deep Gravity model for mobility flows generation (2021)A Deep Gravity model for mobi…Trip distribution modeling with Twitter data (2019)Trip distribution modeling wi…A Machine Learning Approach to Modeling Human Migration (2018)A Machine Learning Approach t…Planning Bike Lanes based on Sharing-Bikes' Trajectories (2017)Planning Bike Lanes based on …Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare (2017)Bicycle infrastructure and tr…Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use (2012)Better Understanding of Facto…A universal model for mobility and migration patterns (2012)A universal model for mobilit…Count Data Models for Trip Generation (2005)Count Data Models for Trip Ge…Urban and regional models in geography and planning (1975)Urban and regional models in …A COMPARATIVE EVALUATION OF TRIP DISTRIBUTION PROCEDURES (1966)A COMPARATIVE EVALUATION OF T…GRAVITY MODELS AND TRIP DISTRIBUTION THEORY (1959)GRAVITY MODELS AND TRIP DISTR…The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons (1946)The P 1 P 2 D Hypothesis: On …Intervening Opportunities: A Theory Relating Mobility and Distance (1940)Intervening Opportunities: A …Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study (2025)Leveraging causal AI to uncov…Bike-sharing systems for sustainable cities: Demand prediction using graph neural networks and spatial aggregation (2025)Bike-sharing systems for sust…A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations (2025)A Model-Data Dual-Driven Appr…Growth, innovation, scaling, and the pace of life in cities (2007)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Geo-located Twitter as proxy for global mobility patterns (2014)A survey of results on mobile phone datasets analysis (2015)Human mobility: Models and applications (2018)Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method (2008)Bikesharing in Europe, the Americas, and Asia (2010)Internet, mail, and mixed-mode surveys: The tailored design method * (2010)Are Bikeshare Users Different from Regular Cyclists? 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References

  • 2012A universal model for mobility and migration patterns1,509 cited
  • 1940Intervening Opportunities: A Theory Relating Mobility and Distance1,089 cited
  • 1946The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons1,033 cited
  • 1975Urban and regional models in geography and planning572 cited
  • 2012Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use342 cited
  • 2021A Deep Gravity model for mobility flows generation267 cited
  • 2017Planning Bike Lanes based on Sharing-Bikes' Trajectories203 cited
  • 2017Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare196 cited
  • 1959GRAVITY MODELS AND TRIP DISTRIBUTION THEORY174 cited
  • 2018A Machine Learning Approach to Modeling Human Migration122 cited
  • 2019Trip distribution modeling with Twitter data74 cited
  • 2021Modeling bike-sharing demand using a regression model with spatially varying coefficients60 cited
  • 2005Count Data Models for Trip Generation58 cited
  • 2023Deep trip generation with graph neural networks for bike sharing system expansion47 cited
  • 1966A COMPARATIVE EVALUATION OF TRIP DISTRIBUTION PROCEDURES

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46 cited
  • 2021Health impacts of bike-sharing systems in the U.S.45 cited
  • Cited by

    • 2025Bike-sharing systems for sustainable cities: Demand prediction using graph neural networks and spatial aggregation16 cited
    • 2025Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study12 cited
    • 2025A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations6 cited