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Development of people mass movement simulation framework based on reinforcement learning
16 references·7 citing·37 second hop
Development of people mass movement simulation framework based on reinforcement learning (2020)Development of people mass movement simul…Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)Modeling real-time human mobi…DeepMove (2018)DeepMoveA Generative Model of Urban Activities from Cellular Data (2017)A Generative Model of Urban A…Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Activity-Based Human Mobility…The path most traveled: Travel demand estimation using big data resources (2015)The path most traveled: Trave…Predictability of population displacement after the 2010 Haiti earthquake (2012)Predictability of population …Human mobility modeling at metropolitan scales (2012)Human mobility modeling at me…Friendship and mobility (2011)Friendship and mobilityLimits of Predictability in Human Mobility (2010)Limits of Predictability in H…Understanding mobility based on GPS data (2008)Understanding mobility based …Understanding individual human mobility patterns (2008)Understanding individual huma…Activity-Based Modeling of Travel Demand (2006)Activity-Based Modeling of Tr…Activity-based disaggregate travel demand model system with activity schedules (2001)Activity-based disaggregate t…Discrete Choice Methods and their Applications to Short Term Travel Decisions (1999)Discrete Choice Methods and t…Activity-Based Modeling of Travel Demand (1999)Activity-Based Modeling of Tr…A theoretical and empirical model of trip chaining behavior (1979)A theoretical and empirical m…Context-aware inverse reinforcement learning for modeling individuals’ daily activity schedules (2025)Context-aware inverse reinfor…A comprehensive methodological review of human mobility simulation and modelling: Current trends, challenges, and future directions (2025)A comprehensive methodologica…A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery (2023)A state-based inverse reinfor…Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas (2022)Synthetic People Flow: Privac…Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data (2022)Pseudo-Pflow: Development of …Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data (2020)Predicting Venue Popularity U…Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning (2020)Intercity Simulation of Human…Elimination by aspects: A theory of choice. (1972)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete choice analysis: Theory and application to travel demand (1988)BIOGEME: a free package for the estimation of discrete choice models (2003)Route choice modeling: past, present and future research directions (2009)Where do cyclists ride? A route choice model developed with revealed preference GPS data (2012)Power-Law Distributions in Empirical Data (2009)A survey of results on mobile phone datasets analysis (2015)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Power-law distributions in empirical data (2018)Social physics (2022)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)A universal model for mobility and migration patterns (2012)Reality mining: sensing complex social systems (2005)Modelling the scaling properties of human mobility (2010)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? 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Longitudinal evidence from eighteen waves of the British Household Panel Survey (2014)Birds of a Feather: Homophily in Social Networks (2001)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016)Using GPS to learn significant locations and predict movement across multiple users (2003)Learning and inferring transportation routines (2007)Next place prediction using mobility Markov chains (2012)Understanding Human Mobility from Twitter (2015)Human mobility: Models and applications (2018)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)The TimeGeo modeling framework for urban mobility without travel surveys (2016)Understanding congested travel in urban areas (2016)Predicting Human Mobility via Variational Attention (2019)Learning to Simulate Human Mobility (2020)A survey on deep learning for human mobility (2023)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2010Limits of Predictability in Human Mobility3,230 cited
  • 2011Friendship and mobility2,912 cited
  • 2008Understanding mobility based on GPS data1,131 cited
  • 1999Discrete Choice Methods and their Applications to Short Term Travel Decisions981 cited
  • 2001Activity-based disaggregate travel demand model system with activity schedules763 cited
  • 2018DeepMove714 cited
  • 2012Predictability of population displacement after the 2010 Haiti earthquake633 cited
  • 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore472 cited
  • 2015The path most traveled: Travel demand estimation using big data resources418 cited
  • 2012Human mobility modeling at metropolitan scales295 cited
  • 1979A theoretical and empirical model of trip chaining behavior285 cited
  • 2017A Generative Model of Urban Activities from Cellular Data185 cited
  • 2006Activity-Based Modeling of Travel Demand152 cited
  • 1999Activity-Based Modeling of Travel Demand152 cited
  • 2018Modeling real-time human mobility based on mobile phone and transportation data fusion

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150 cited

Cited by

  • 2020Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning18 cited
  • 2020Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data15 cited
  • 2023A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery14 cited
  • 2022Pseudo-Pflow: Development of Nationwide Synthetic Open Dataset for People Movement Based on Limited Travel Survey and Open Statistical Data14 cited
  • 2025A comprehensive methodological review of human mobility simulation and modelling: Current trends, challenges, and future directions9 cited
  • 2025Context-aware inverse reinforcement learning for modeling individuals’ daily activity schedules6 cited
  • 2022Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas4 cited