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AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain Adaptation
16 references·0 citing·44 second hop
AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain Adaptation (2024)AGENDA: Predicting Trip Purposes with A N…What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities (2020)What is the Human Mobility in…The scales of human mobility (2020)The scales of human mobilityTripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data (2018)TripImputor: Real-Time Imputi…Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes (2018)Trip2Vec: a deep embedding ap…Forecasting current and next trip purpose with social media data and Google Places (2018)Forecasting current and next …Real-time trip purpose prediction using online location-based search and discovery services (2017)Real-time trip purpose predic…Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons (2017)Data selection in machine lea…Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization (2016)Detecting trip purposes from …Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Inferring trip purposes and u…Trip Purpose Identification from GPS Tracks (2014)Trip Purpose Identification f…Evaluation of Two Methods for Identifying Trip Purpose in GPS-Based Household Travel Surveys (2014)Evaluation of Two Methods for…We know how you live (2013)We know how you livePlacer (2013)PlacerEvaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study (2010)Evaluating the feasibility of…Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands (2009)Deriving and validating trip …Understanding individual human mobility patterns (2008)Understanding individual huma…The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Can Land-use Policy Really Affect Travel Behaviour? A Study of the Link between Non-work Travel and Land-use Characteristics (1998)GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference? (2013)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Modelling the scaling properties of human mobility (2010)Discovering regions of different functions in a city using human mobility and POIs (2012)Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China (2020)How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics (2021)Learning transportation mode from raw gps data for geographic applications on the web (2008)Transportation mode detection using mobile phones and GIS information (2011)Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices (2015)An evaluation of emerging data collection technologies for travel demand modeling: from research to practice (2016)Developing a passive GPS tracking system to study long-term travel behavior (2019)Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data (2001)Learning significant locations and predicting user movement with GPS (2003)The Mobile Data Challenge: Big Data for Mobile Computing Research (2012)What is this place? Inferring place categories through user patterns identification in geo-tagged tweets (2014)Choosing the Right Home Location Definition Method for the Given Dataset (2015)Understanding mobility based on GPS data (2008)Processing Raw Data from Global Positioning Systems without Additional Information (2009)Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies (2014)From traces to trajectories: How well can we guess activity locations from mobile phone traces? (2014)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier (2017)Deep neural networks for choice analysis: Extracting complete economic information for interpretation (2020)A Rationale and Test for the Number of Factors in Factor Analysis (1965)Modelling the Choice of Residential Location (1977)MODELING THE CHOICE OF RESIDENTIAL LOCATION (1978)Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study (2018)New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data (2020)Fences and neighbours in the postmodern world: boundary narratives in political geography (1998)Detecting the dynamics of urban structure through spatial network analysis (2014)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Understanding Urban Dynamics via Context-Aware Tensor Factorization with Neighboring Regularization (2019)Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China (2021)Power-Law Distributions in Empirical Data (2009)Power-law distributions in empirical data (2018)Cycling behaviour in 17 countries across 6 continents: levels of cycling, who cycles, for what purpose, and how far? (2021)Gender differences in active travel in major cities across the world (2022)A survey on deep learning for human mobility (2023)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands501 cited
  • 2020The scales of human mobility361 cited
  • 2015Inferring trip purposes and uncovering travel patterns from taxi trajectory data254 cited
  • 2010Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study193 cited
  • 2018TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data171 cited
  • 2016Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization114 cited
  • 2013Placer101 cited
  • 2017Real-time trip purpose prediction using online location-based search and discovery services97 cited
  • 2014Trip Purpose Identification from GPS Tracks87 cited
  • 2013We know how you live85 cited
  • 2018Forecasting current and next trip purpose with social media data and Google Places83 cited
  • 2018Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes66 cited
  • 2020What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities54 cited

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  • 2014Evaluation of Two Methods for Identifying Trip Purpose in GPS-Based Household Travel Surveys48 cited
  • 2017Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons42 cited
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