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A hybrid Markov-based model for human mobility prediction
10 references·16 citing·36 second hop
A hybrid Markov-based model for human mobility prediction (2017)A hybrid Markov-based model for human mob…From mobile phone data to the spatial structure of cities (2014)From mobile phone data to the…Estimating human trajectories and hotspots through mobile phone data (2014)Estimating human trajectories…Unravelling daily human mobility motifs (2013)Unravelling daily human mobil…Predicting future locations with hidden Markov models (2012)Predicting future locations w…Evaluating mobility models for temporal prediction with high-granularity mobility data (2012)Evaluating mobility models fo…Trip destination prediction based on past GPS log using a Hidden Markov Model (2010)Trip destination prediction b…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…Understanding individual human mobility patterns (2008)Understanding individual huma…The scaling laws of human travel (2006)The scaling laws of human tra…Using GPS to learn significant locations and predict movement across multiple users (2003)Using GPS to learn significan…A review of human mobility: Linking data, models, and real-world applications (2025)A review of human mobility: L…Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data (2024)Testing feasibility of using …ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction (2024)ST-MoE-BERT: A Spatial-Tempor…Exploring human mobility: a time-informed approach to pattern mining and sequence similarity (2024)Exploring human mobility: a t…Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction (2024)Cross- and Context-Aware Atte…Coupling graph neural networks and travel mode choice for human mobility prediction (2024)Coupling graph neural network…Explaining human mobility predictions through a pattern matching algorithm (2022)Explaining human mobility pre…Beyond The Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability (2022)Beyond The Limits of Predicta…WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing (2021)WiFiMod: Transformer-based In…Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction (2021)Spatio-Temporal Urban Knowled…Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification (2021)Mobility Prediction Using a W…MDLF: A Multi-View-Based Deep Learning Framework for Individual Trip Destination Prediction in Public Transportation Systems (2021)MDLF: A Multi-View-Based Deep…Combining individual travel behaviour and collective preferences for next location prediction (2021)Combining individual travel b…Attentional Markov Model for Human Mobility Prediction (2021)Attentional Markov Model for …Human Mobility Prediction Using Sparse Trajectory Data (2020)Human Mobility Prediction Usi…Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain (2018)Pedestrians Complex Behavior …Reality mining: sensing complex social systems (2005)Limits of Predictability in Human Mobility (2010)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Returners and explorers dichotomy in human mobility (2015)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Understanding mobility based on GPS data (2008)Next place prediction using mobility Markov chains (2012)Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest (2016)GMove (2016)Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States! (2020)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)What about people in Regional Science? (1970)Data from mobile phone operators: A tool for smarter cities? (2014)Traffic zone division based on big data from mobile phone base stations (2015)Urban magnetism through the lens of geo-tagged photography (2015)The origin of bursts and heavy tails in human dynamics (2005)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences (2002)Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011)Patterns, Entropy, and Predictability of Human Mobility and Life (2012)The influence of temporal and spatial features on the performance of next-place prediction algorithms (2013)NextMe: Localization Using Cellular Traces in Internet of Things (2015)Inferring High-Level Behavior from Low-Level Sensors (2003)Learning and inferring transportation routines (2007)Exploiting machine learning techniques for location recognition and prediction with smartphone logs (2015)Trip destination prediction based on multi-day GPS data (2018)LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists (2020)Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Urban Spatial Structure (2010)Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)The universal visitation law of human mobility (2021)A survey of results on mobile phone datasets analysis (2015)Human mobility: Models and applications (2018)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2006The scaling laws of human travel2,207 cited
  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2003Using GPS to learn significant locations and predict movement across multiple users1,060 cited
  • 2013Unravelling daily human mobility motifs584 cited
  • 2014From mobile phone data to the spatial structure of cities458 cited
  • 2012Predicting future locations with hidden Markov models384 cited
  • 2014Estimating human trajectories and hotspots through mobile phone data212 cited
  • 2010Trip destination prediction based on past GPS log using a Hidden Markov Model127 cited
  • 2012Evaluating mobility models for temporal prediction with high-granularity mobility data90 cited

Cited by

  • 2021Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification68 cited
  • 2021Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction59 cited
  • 2022Beyond The Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability39 cited
  • 2021Attentional Markov Model for Human Mobility Prediction31 cited
  • 2020Human Mobility Prediction Using Sparse Trajectory Data

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25 cited
  • 2021MDLF: A Multi-View-Based Deep Learning Framework for Individual Trip Destination Prediction in Public Transportation Systems19 cited
  • 2025A review of human mobility: Linking data, models, and real-world applications17 cited
  • 2021WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing13 cited
  • 2024Exploring human mobility: a time-informed approach to pattern mining and sequence similarity12 cited
  • 2024Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data11 cited
  • 2021Combining individual travel behaviour and collective preferences for next location prediction10 cited
  • 2022Explaining human mobility predictions through a pattern matching algorithm8 cited
  • 2024Coupling graph neural networks and travel mode choice for human mobility prediction7 cited
  • 2024ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction6 cited
  • 2024Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction6 cited
  • 2018Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain6 cited