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Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes
16 references·16 citing·48 second hopsampled
Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes (2018)Trip2Vec: a deep embedding approach for c…TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data (2018)TripImputor: Real-Time Imputi…POI2Vec: Geographical Latent Representation for Predicting Future Visitors (2017)POI2Vec: Geographical Latent …Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes (2017)Human Mobility Synchronizatio…Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests (2017)Exploring Bikesharing Travel …Autoencoder Regularized Network For Driving Style Representation Learning (2017)Autoencoder Regularized Netwo…Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Inferring trip purposes and u…Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies (2014)Zooming into individuals to u…Social media for large studies of behavior (2014)Social media for large studie…Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies (2014)Deriving Personal Trip Data f…B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces (2014)B-Planner: Planning Bidirecti…Placer (2013)PlacerMining smart card data for transit riders’ travel patterns (2013)Mining smart card data for tr…From taxi GPS traces to social and community dynamics (2013)From taxi GPS traces to socia…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 …Measuring transit use variability with smart-card data (2007)Measuring transit use variabi…Using GPS data loggers to replace travel diaries in the collection of travel data (2000)Using GPS data loggers to rep…Adaptive scootability index: Built environment, travel purpose and e-scooter preferred route (2025)Adaptive scootability index: …Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications (2025)Activity-aware urban area emb…Wheels Know Why You Travel (2022)Wheels Know Why You TravelInvestigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory Data (2022)Investigating Human Travel Pa…Enriching Large-Scale Trips With Fine-Grained Travel Purposes: A Semi-Supervised Deep Graph Embedding Framework (2022)Enriching Large-Scale Trips W…Enhancing the POI data for trip purpose inference using machine learning techniques (2022)Enhancing the POI data for tr…A review of studies on service quality of intermediate public transport (2022)A review of studies on servic…Variational embedding of a hidden Markov model to generate human activity sequences (2021)Variational embedding of a hi…Trip Purposes Mining From Mobile Signaling Data (2021)Trip Purposes Mining From Mob…Trip purpose inference for tourists by machine learning approaches based on mobile signaling data (2021)Trip purpose inference for to…Toward travel pattern aware tourism region planning: a big data approach (2021)Toward travel pattern aware t…Public Bike Trip Purpose Inference Using Point-of-Interest Data (2021)Public Bike Trip Purpose Infe…ToiletBuilder : A PU-Learning-Based Model for Selecting New Public Toilet Locations (2020)ToiletBuilder : A PU-Learning…Reviewing trip purpose imputation in GPS-based travel surveys (2020)Reviewing trip purpose imputa…Understanding and visualizing passengers’ travel behaviours: a device-free sensing way leveraging taxi trajectory data (2019)Understanding and visualizing…A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips (2019)A Spatiotemporal Constraint N…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)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)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)OpenStreetMap: User-Generated Street Maps (2008)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix (2015)Analyzing large-scale human mobility data: a survey of machine learning methods and applications (2018)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)Birds of a Feather: Homophily in Social Networks (2001)Why Most Published Research Findings Are False (2005)Why Most Published Research Findings Are False (2005)A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism (2016)Social media analytics – Challenges in topic discovery, data collection, and data preparation (2017)Fake news on Twitter during the 2016 U.S. presidential election (2019)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Spatio-Temporal Analytics for Exploring Human Mobility Patterns and Urban Dynamics in the Mobile Age (2014)Incorporating spatial interaction patterns in classifying and understanding urban land use (2015)Inferring High-Level Behavior from Low-Level Sensors (2003)Learning transportation mode from raw gps data for geographic applications on the web (2008)Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines (2015)Transportation mode detection – an in-depth review of applicability and reliability (2016)Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier (2017)Observing the rhythms of daily life: A six-week travel diary (2002)Introduction: Habitual travel choice (2003)The potential of public transport smart card data (2005)Passenger Segmentation Using Smart Card Data (2014)Inferring patterns in the multi-week activity sequences of public transport users (2016)Friendship and mobility (2011)LORE (2014)Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016)Location2vec: A Situation-Aware Representation for Visual Exploration of Urban Locations (2019)Predicting Human Mobility via Variational Attention (2019)Context-aware Deep Model for Joint Mobility and Time Prediction (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)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 2014Social media for large studies of behavior647 cited
  • 2013Mining smart card data for transit riders’ travel patterns606 cited
  • 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands501 cited
  • 2007Measuring transit use variability with smart-card data271 cited
  • 2015Inferring trip purposes and uncovering travel patterns from taxi trajectory data254 cited
  • 2013From taxi GPS traces to social and community dynamics253 cited
  • 2017POI2Vec: Geographical Latent Representation for Predicting Future Visitors224 cited
  • 2014Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies215 cited
  • 2018TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data171 cited
  • 2014Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies162 cited
  • 2017Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests142 cited
  • 2014B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces140 cited
  • 2000Using GPS data loggers to replace travel diaries in the collection of travel data131 cited
  • 2013Placer101 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2017Autoencoder Regularized Network For Driving Style Representation Learning73 cited
  • 2017Human Mobility Synchronization and Trip Purpose Detection with Mixture of Hawkes Processes69 cited
  • Cited by

    • 2020Reviewing trip purpose imputation in GPS-based travel surveys53 cited
    • 2021Trip Purposes Mining From Mobile Signaling Data40 cited
    • 2021Toward travel pattern aware tourism region planning: a big data approach22 cited
    • 2021Variational embedding of a hidden Markov model to generate human activity sequences21 cited
    • 2021Public Bike Trip Purpose Inference Using Point-of-Interest Data20 cited
    • 2022Enriching Large-Scale Trips With Fine-Grained Travel Purposes: A Semi-Supervised Deep Graph Embedding Framework17 cited
    • 2022Wheels Know Why You Travel17 cited
    • 2019A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips16 cited
    • 2022Investigating Human Travel Patterns from an Activity Semantic Flow Perspective: A Case Study within the Fifth Ring Road in Beijing Using Taxi Trajectory Data15 cited
    • 2021Trip purpose inference for tourists by machine learning approaches based on mobile signaling data14 cited
    • 2022A review of studies on service quality of intermediate public transport13 cited
    • 2020ToiletBuilder : A PU-Learning-Based Model for Selecting New Public Toilet Locations11 cited
    • 2019Understanding and visualizing passengers’ travel behaviours: a device-free sensing way leveraging taxi trajectory data10 cited
    • 2022Enhancing the POI data for trip purpose inference using machine learning techniques9 cited
    • 2025Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications8 cited
    • 2025Adaptive scootability index: Built environment, travel purpose and e-scooter preferred route6 cited