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Effective Metagraph-based Life Pattern Clustering with Big Human Mobility Data
16 references·0 citing·47 second hop
Effective Metagraph-based Life Pattern Clustering with Big Human Mobility Data (2021)Effective Metagraph-based Life Pattern Cl…Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand (2020)Mobile Phone Data in Urban Co…Spatial structure, intra-urban commuting patterns and travel mode choice: Analyses of relationships in the Kumasi Metropolis, Ghana (2019)Spatial structure, intra-urba…Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN (2019)Exploring the uncertainty of …Identifying spatiotemporal urban activities through linguistic signatures (2018)Identifying spatiotemporal ur…Detecting Popular Temporal Modes in Population-scale Unlabelled Trajectory Data (2018)Detecting Popular Temporal Mo…Detecting and visualizing cohesive activity-travel patterns: A network analysis approach (2017)Detecting and visualizing coh…Mining human mobility patterns from social geo-tagged data (2016)Mining human mobility pattern…Inferring weekly primary activity patterns using public transport smart card data and a household travel survey (2016)Inferring weekly primary acti…Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Activity-Based Human Mobility…Uncovering urban human mobility from large scale taxi GPS data (2015)Uncovering urban human mobili…Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan (2014)Understanding tourist behavio…Mining smart card data for transit riders’ travel patterns (2013)Mining smart card data for tr…Detection of Activities of Public Transport Users by Analyzing Smart Card Data (2012)Detection of Activities of Pu…Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data (2010)Activity-Aware Map: Identifyi…Understanding individual human mobility patterns (2008)Understanding individual huma…A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method (2004)A Survey Method for Character…Reality mining: sensing complex social systems (2005)Limits of Predictability in Human Mobility (2010)Correlating mobile phone usage and travel behavior – A case study of Harbin, China (2011)Intra-urban human mobility patterns: An urban morphology perspective (2011)Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach (2015)Smart card data use in public transit: A literature review (2011)Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Understanding commuting patterns using transit smart card data (2016)A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership (2018)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Validity and Reliability of the Experience-Sampling Method (1987)Choices, Values, and Frames (2000)Developments in the Measurement of Subjective Well-Being (2006)High income improves evaluation of life but not emotional well-being (2010)Loneliness Matters: A Theoretical and Empirical Review of Consequences and Mechanisms (2010)Choices, Values, and Frames (2013)Modelling the scaling properties of human mobility (2010)Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data (2018)Using Flickr Geotagged Photos to Estimate Visitor Trajectories in World Heritage Cities (2020)Using smartphone-GPS data to understand pedestrian-scale behavior in urban settings: A review of themes and approaches (2021)Activity spaces: measures of social exclusion? (2003)Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap (2014)Activity detection and transfer identification for public transit fare card data (2015)Validating and improving public transport origin–destination estimation algorithm using smart card fare data (2016)Learning transportation mode from raw gps data for geographic applications on the web (2008)Understanding mobility based on GPS data (2008)Mapping the world's photos (2009)Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play (2018)Flickr data for analysing tourists’ spatial behaviour and movement patterns (2020)NexT: A framework for next-place prediction on location based social networks (2020)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)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)Location and Land Use: Toward a General Theory of Land Rent (1964)Activity-based disaggregate travel demand model system with activity schedules (2001)Urban Sprawl, Socioeconomic Features, and Travel Patterns in Middle East Countries: A Case Study in Iran (2020)Urban expansion and differential accessibility by car and public transport in the Greater Kumasi city-region, Ghana—A geospatial modelling approach (2021)A multi-activity view of intra-urban travel networks: A case study of Beijing (2023)Exploring Human Mobility Patterns in Urban Scenarios: A Trajectory Data Perspective (2018)Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi'an, Shaanxi, China (2018)Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China (2020)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2004A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method3,418 cited
  • 2013Mining smart card data for transit riders’ travel patterns606 cited
  • 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore472 cited
  • 2010Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data280 cited
  • 2015Uncovering urban human mobility from large scale taxi GPS data280 cited
  • 2012Detection of Activities of Public Transport Users by Analyzing Smart Card Data148 cited
  • 2019Spatial structure, intra-urban commuting patterns and travel mode choice: Analyses of relationships in the Kumasi Metropolis, Ghana87 cited
  • 2014Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan74 cited
  • 2016Mining human mobility patterns from social geo-tagged data66 cited
  • 2016Inferring weekly primary activity patterns using public transport smart card data and a household travel survey52 cited
  • 2019Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN51 cited
  • 2018Identifying spatiotemporal urban activities through linguistic signatures46 cited
  • 2017Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

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43 cited
  • 2018Detecting Popular Temporal Modes in Population-scale Unlabelled Trajectory Data37 cited
  • 2020Mobile Phone Data in Urban Commuting: A Network Community Detection-Based Framework to Unveil the Spatial Structure of Commuting Demand36 cited
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