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Daily metro origin-destination pattern recognition using dimensionality reduction and clustering methods
3 references·6 citing·35 second hop
Daily metro origin-destination pattern recognition using dimensionality reduction and clustering methods (2017)Daily metro origin-destination pattern re…Validating the coverage of bus schedules: A Machine Learning approach (2014)Validating the coverage of bu…Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)Estimation of a disaggregate …Modeling and Management of Signal Transduction Pathways with Live Sequence Charts (2007)Modeling and Management of Si…Point-Process Modeling and Divergence Measures Applied to the Characterization of Passenger Flow Patterns of a Metro System (2022)Point-Process Modeling and Di…Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul (2022)Classification of the metropo…Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method (2021)Short-term origin-destination…Station-to-User Transfer Learning (2020)Station-to-User Transfer Lear…Origin-Destination Matrix Estimation Using Big Traffic Data: A Structural Perspective (2019)Origin-Destination Matrix Est…Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction (2019)Contextualized Spatial–Tempor…Estimating a Rail Passenger Trip Origin‐Destination Matrix Using Automatic Data Collection Systems (2007)Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System (2007)Smart card data use in public transit: A literature review (2011)Mining smart card data for transit riders’ travel patterns (2013)Detecting the dynamics of urban structure through spatial network analysis (2014)Big Data Analytics in Intelligent Transportation Systems: A Survey (2018)Dynamic Bus Arrival Time Prediction with Artificial Neural Networks (2002)Urban Transit : Operations, Planning and Economics (2005)Urban Transit: Operations, Planning and Economics (2006)An Offline Framework for the Diagnosis of Time Reliability by Automatic Vehicle Location Data (2016)The sightseeing bus schedule optimization under Park and Ride System in tourist attractions (2016)Applications of Artificial Intelligence in Transport: An Overview (2019)Contextual correlates of semantic similarity (1991)Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2017)Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach (2017)Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018)Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction (2020)Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network (2020)The most likely trip matrix estimated from traffic counts (1980)Multivariate stochastic approximation using a simultaneous perturbation gradient approximation (1992)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Dynamic Origin-Destination Matrix Estimation with ICT Traffic Measurements using SPSA (2021)Methods to enhance the quality of bi-level origin–destination matrix adjustment process (2022)Celebrating the third place : inspiring stories about the "great good places" at the heart of our communities (2001)Discovering regions of different functions in a city using human mobility and POIs (2012)Human mobility characterization from cellular network data (2013)Traffic Flow Prediction With Big Data: A Deep Learning Approach (2014)Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (2018)Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019)Statistical inference on transit route‐level origin–destination flows using automatic passenger counter data (2015)Measuring transit use variability with smart-card data (2007)Seoul (2011)Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach (2024)Exploring spatiotemporal dynamic of metro ridership and the influence of built environment factors at the station level: A case study of Nanjing, China (2025)Uncovering Travel Rhythms: A Spatiotemporal and Data-Driven Typology of Urban Rail Transit Stations (2026)
this work references cited by second hop

References

  • 2012Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile546 cited
  • 2007Modeling and Management of Signal Transduction Pathways with Live Sequence Charts53 cited
  • 2014Validating the coverage of bus schedules: A Machine Learning approach46 cited

Cited by

  • 2019Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction287 cited
  • 2021Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method187 cited
  • 2019Origin-Destination Matrix Estimation Using Big Traffic Data: A Structural Perspective9 cited
  • 2022Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul8 cited
  • 2022Point-Process Modeling and Divergence Measures Applied to the Characterization of Passenger Flow Patterns of a Metro System3 cited
  • 2020Station-to-User Transfer Learning3 cited

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