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A Markov chain dynamic model for trip generation and distribution based on CDR
12 references·3 citing·37 second hop
A Markov chain dynamic model for trip generation and distribution based on CDR (2015)A Markov chain dynamic model for trip gen…Traffic zone division based on big data from mobile phone base stations (2015)Traffic zone division based o…Analyzing Cell Phone Location Data for Urban Travel (2015)Analyzing Cell Phone Location…Big data: Issues, challenges, tools and Good practices (2013)Big data: Issues, challenges,…Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia (2013)Application of mobile phone l…Understanding Road Usage Patterns in Urban Areas (2012)Understanding Road Usage Patt…A universal model for mobility and migration patterns (2012)A universal model for mobilit…Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Estimating Origin-Destination…Understanding transportation modes based on GPS data for web applications (2010)Understanding transportation …Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Real-Time Urban Monitoring Us…Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Mobile Landscapes: Using Loca…Clustering and Prediction of Mobile User Routes from Cellular Data (2005)Clustering and Prediction of …Using GPS to learn significant locations and predict movement across multiple users (2003)Using GPS to learn significan…Generating a synthetic probabilistic daily activity-location schedule using large-scale, long-term and low-frequency smartphone GPS data with limited activity information (2021)Generating a synthetic probab…A Big Data Demand Estimation Model for Urban Congested Networks (2020)A Big Data Demand Estimation …Effectiveness of the two-step dynamic demand estimation model on large networks (2017)Effectiveness of the two-step…Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Limits of Predictability in Human Mobility (2010)Modelling the scaling properties of human mobility (2010)Understanding individual routing behaviour (2016)Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis (2018)Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)A review of urban computing for mobile phone traces (2013)Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014)On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)A Theoretical Foundation for the Gravity Equation. (1979)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)A new insight into land use classification based on aggregated mobile phone data (2014)Development of origin–destination matrices using mobile phone call data (2014)The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences (2002)Reality mining: sensing complex social systems (2005)Learning and inferring transportation routines (2007)Understanding mobility based on GPS data (2008)Transportation mode inference from anonymized and aggregated mobile phone call detail records (2010)What We Know and Don't Know about Online Word-of-Mouth: A Review and Synthesis of the Literature (2014)Analysis of human mobility patterns from GPS trajectories and contextual information (2015)Me++ (2003)Me++: The Cyborg Self and the Networked City (2003)Dynamic population mapping using mobile phone data (2014)A survey of results on mobile phone datasets analysis (2015)Human mobility: Models and applications (2018)Growth, innovation, scaling, and the pace of life in cities (2007)Geo-located Twitter as proxy for global mobility patterns (2014)Big Data: Issues and Challenges Moving Forward (2013)Big data analytics: a survey (2015)The Challenges of Data Quality and Data Quality Assessment in the Big Data Era (2015)Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities (2020)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Forecasting supply chain resilience performance using grey prediction (2016)Accessibility based on Gravity-Radiation model and Google Maps API: A case study in Australia (2018)Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications (2019)
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References

  • 2012A universal model for mobility and migration patterns1,509 cited
  • 2003Using GPS to learn significant locations and predict movement across multiple users1,060 cited
  • 2013Big data: Issues, challenges, tools and Good practices871 cited
  • 2006Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis865 cited
  • 2010Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome623 cited
  • 2011Estimating Origin-Destination Flows Using Mobile Phone Location Data511 cited
  • 2010Understanding transportation modes based on GPS data for web applications485 cited
  • 2012Understanding Road Usage Patterns in Urban Areas371 cited
  • 2015Analyzing Cell Phone Location Data for Urban Travel180 cited
  • 2015Traffic zone division based on big data from mobile phone base stations136 cited
  • 2005Clustering and Prediction of Mobile User Routes from Cellular Data88 cited
  • 2013Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia74 cited

Cited by

  • 2021Generating a synthetic probabilistic daily activity-location schedule using large-scale, long-term and low-frequency smartphone GPS data with limited activity information12 cited
  • 2020A Big Data Demand Estimation Model for Urban Congested Networks

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9 cited
  • 2017Effectiveness of the two-step dynamic demand estimation model on large networks4 cited