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Review on Application of Call Details Records (CDRs) Data to Understand Urban Mobility Scenarios for Future Smart Cities
16 references·0 citing·40 second hopsampled
Review on Application of Call Details Records (CDRs) Data to Understand Urban Mobility Scenarios for Future Smart Cities (2023)Review on Application of Call Details Rec…Where do cyclists ride? A route choice model developed with revealed preference GPS data (2012)Where do cyclists ride? A rou…Understanding Road Usage Patterns in Urban Areas (2012)Understanding Road Usage Patt…Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Understanding individual mobi…Urban computing with taxicabs (2011)Urban computing with taxicabsFriendship and mobility (2011)Friendship and mobilityEstimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Estimating Origin-Destination…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…Limits of Predictability in Human Mobility (2010)Limits of Predictability in H…Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks (2010)Does Urban Mobility Have a Da…Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data (2010)Activity-Aware Map: Identifyi…Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network (2009)Eigenplaces: Analysing Cities…Characterizing the human mobility pattern in a large street network (2009)Characterizing the human mobi…Understanding individual human mobility patterns (2008)Understanding individual huma…Uncovering individual and collective human dynamics from mobile phone records (2008)Uncovering individual and col…Deriving origin–destination data from a mobile phone network (2007)Deriving origin–destination d…Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Mobile Landscapes: Using Loca…The origin of bursts and heavy tails in human dynamics (2005)Modeling bursts and heavy tails in human dynamics (2006)The scaling laws of human travel (2006)Computational Social Science (2009)Simulating dynamical features of escape panic (2000)Reality mining: sensing complex social systems (2005)A universal model for mobility and migration patterns (2012)Smart cities of the future (2012)A Theoretical Foundation for the Gravity Equation. (1979)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Social Sensing: A New Approach to Understanding Our Socioeconomic Environments (2015)Human mobility: Models and applications (2018)Analysis of Panel Data (2003)The Mixed Logit model: The state of practice (2003)Infrastructure, programs, and policies to increase bicycling: An international review (2009)Promoting Cycling for Transport: Research Needs and Challenges (2014)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Bikeway Networks: A Review of Effects on Cycling (2015)Official Journal of the European Communities (1984)Me++ (2003)Smart Mobs: The Next Social Revolution (2003)Code/Space (2011)Dynamic population mapping using mobile phone data (2014)Bridging the gap between physical location and online social networks (2010)Driving with knowledge from the physical world (2011)Discovering regions of different functions in a city using human mobility and POIs (2012)Urban Computing (2014)Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (2019)Stretched exponential distributions in nature and economy: “fat tails” with characteristic scales (1998)A survey of results on mobile phone datasets analysis (2015)Predicting poverty and wealth from mobile phone metadata (2015)Birds of a Feather: Homophily in Social Networks (2001)Power-Law Distributions in Empirical Data (2009)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)Natural Scales in Geographical Patterns (2017)Power-law distributions in empirical data (2018)Examining the impacts of residential self-selection on travel behavior: A focus on methodologies (2007)Geo-located Twitter as proxy for global mobility patterns (2014)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)
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References

  • 2008Understanding individual human mobility patterns6,185 cited
  • 2010Limits of Predictability in Human Mobility3,282 cited
  • 2011Friendship and mobility2,972 cited
  • 2010Modelling the scaling properties of human mobility1,304 cited
  • 2006Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis888 cited
  • 2012Where do cyclists ride? A route choice model developed with revealed preference GPS data756 cited
  • 2012Understanding individual mobility patterns from urban sensing data: A mobile phone trace example659 cited
  • 2008Uncovering individual and collective human dynamics from mobile phone records611 cited
  • 2011Estimating Origin-Destination Flows Using Mobile Phone Location Data545 cited
  • 2011Urban computing with taxicabs533 cited
  • 2012Understanding Road Usage Patterns in Urban Areas378 cited
  • 2009Characterizing the human mobility pattern in a large street network305 cited
  • 2010Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data287 cited
  • 2007Deriving origin–destination data from a mobile phone network277 cited
  • 2010Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks

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277 cited
  • 2009Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network262 cited
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