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Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data
16 references·16 citing·37 second hopsampled
Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data (2018)Analysis and Prediction of Regional Mobil…Measuring Regularity of Individual Travel Patterns (2017)Measuring Regularity of Indiv…Understanding commuting patterns using transit smart card data (2016)Understanding commuting patte…Factors influencing the choice of shared bicycles and shared electric bikes in Beijing (2016)Factors influencing the choic…Clustering Smart Card Data for Urban Mobility Analysis (2016)Clustering Smart Card Data fo…Measuring variability of mobility patterns from multiday smart-card data (2015)Measuring variability of mobi…Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing (2015)Combining smart card data and…Passenger Segmentation Using Smart Card Data (2014)Passenger Segmentation Using …Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap (2014)Examining the spatial–tempora…Behavioural data mining of transit smart card data: A data fusion approach (2014)Behavioural data mining of tr…Mining smart card data for transit riders’ travel patterns (2013)Mining smart card data for tr…Spatiotemporal Patterns of Urban Human Mobility (2012)Spatiotemporal Patterns of Ur…Discovering Spatial Patterns in Origin‐Destination Mobility Data (2012)Discovering Spatial Patterns …Smart card data use in public transit: A literature review (2011)Smart card data use in public…Enriching Archived Smart Card Transaction Data for Transit Demand Modeling (2008)Enriching Archived Smart Card…MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA (2006)MINING PUBLIC TRANSPORT USER …The potential of public transport smart card data (2005)The potential of public trans…TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management (2024)TripChain2RecDeepSurv: A nove…Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation (2024)Spatial-temporal identificati…Real-time bus arrival delays analysis using seemingly unrelated regression model (2024)Real-time bus arrival delays …Origin-destination demand prediction of public transit using graph convolutional neural network (2024)Origin-destination demand pre…The Spatiotemporal Patterns of Bus Passengers: Visualisation and Evaluation using Non-negative Tensor Decomposition (2023)The Spatiotemporal Patterns o…Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition (2022)Uncovering the spatiotemporal…Exploring regional sustainable commuting patterns based on dockless bike-sharing data and POI data (2022)Exploring regional sustainabl…Entropy Tucker model: Mining latent mobility patterns with simultaneous estimation of travel impedance parameters (2022)Entropy Tucker model: Mining …Travel mode recognition of urban residents using mobile phone data and MapAPI (2021)Travel mode recognition of ur…Mobility pattern recognition based prediction for the subway station related bike-sharing trips (2021)Mobility pattern recognition …Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars (2021)Exploring Human Mobility Patt…Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data (2020)Identifying and Segmenting Co…Nonnegative tensor decomposition for urban mobility analysis and applications with mobile phone data (2019)Nonnegative tensor decomposit…Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System (2019)Mining Customized Bus Demand …Combining ITS and optimization in public transportation planning: state of the art and future research paths (2019)Combining ITS and optimizatio…A Data-Driven Analysis for Operational Vehicle Performance of Public Transport Network (2019)A Data-Driven Analysis for Op…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)A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership (2018)Geography: A Modern Synthesis (1972)Geography: a modern synthesis (2004)Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System (2007)Observing the rhythms of daily life: A six-week travel diary (2002)Inferring patterns in the multi-week activity sequences of public transport users (2016)Public transport for smart cities: Recent innovations and future challenges (2022)Property, Privacy, and Personal Data (2004)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Urban Spatial Structure (2010)Friendship and mobility (2011)Rethinking big data: A review on the data quality and usage issues (2015)Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data (2019)Identifying urban spatial structure and urban vibrancy in highly dense cities using georeferenced social media data (2019)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)A review of urban computing for mobile phone traces (2013)Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method (2016)The TimeGeo modeling framework for urban mobility without travel surveys (2016)Specification Tests for the Multinomial Logit Model (1984)Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Bikesharing in Europe, the Americas, and Asia (2010)Cycling towards a more sustainable transport future (2017)The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets (2018)A review on bike-sharing: The factors affecting bike-sharing demand (2019)Multiple Attribute Decision Making: Methods and Applications (1981)Lockdowned: Everyday mobility changes in response to COVID-19 (2020)The impacts of COVID-19 pandemic on public transit demand in the United States (2020)Analyzing year-to-year changes in public transport passenger behaviour using smart card data (2017)Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data (2017)Research on the influence of after-sales service quality factors on customer satisfaction (2020)Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations (2018)Exploring Individual Travel Patterns Across Private Car Trajectory Data (2019)Urban Human Mobility (2019)
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References

  • 2011Smart card data use in public transit: A literature review930 cited
  • 2013Mining smart card data for transit riders’ travel patterns606 cited
  • 2016Factors influencing the choice of shared bicycles and shared electric bikes in Beijing489 cited
  • 2005The potential of public transport smart card data396 cited
  • 2016Understanding commuting patterns using transit smart card data351 cited
  • 2012Spatiotemporal Patterns of Urban Human Mobility282 cited
  • 2014Behavioural data mining of transit smart card data: A data fusion approach278 cited
  • 2015Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing265 cited
  • 2016Clustering Smart Card Data for Urban Mobility Analysis214 cited
  • 2006MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA199 cited
  • 2014Passenger Segmentation Using Smart Card Data195 cited
  • 2012Discovering Spatial Patterns in Origin‐Destination Mobility Data173 cited
  • 2008Enriching Archived Smart Card Transaction Data for Transit Demand Modeling150 cited
  • 2014Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap146 cited
  • 2015Measuring variability of mobility patterns from multiday smart-card data

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131 cited
  • 2017Measuring Regularity of Individual Travel Patterns122 cited
  • Cited by

    • 2019Combining ITS and optimization in public transportation planning: state of the art and future research paths59 cited
    • 2021Mobility pattern recognition based prediction for the subway station related bike-sharing trips53 cited
    • 2020Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data26 cited
    • 2021Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars25 cited
    • 2021Travel mode recognition of urban residents using mobile phone data and MapAPI25 cited
    • 2022Exploring regional sustainable commuting patterns based on dockless bike-sharing data and POI data23 cited
    • 2024TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management19 cited
    • 2019Nonnegative tensor decomposition for urban mobility analysis and applications with mobile phone data17 cited
    • 2023The Spatiotemporal Patterns of Bus Passengers: Visualisation and Evaluation using Non-negative Tensor Decomposition15 cited
    • 2022Uncovering the spatiotemporal motif patterns in urban mobility networks by non-negative tensor decomposition14 cited
    • 2019A Data-Driven Analysis for Operational Vehicle Performance of Public Transport Network13 cited
    • 2019Mining Customized Bus Demand Spots Based on Smart Card Data: A Case Study of the Beijing Public Transit System13 cited
    • 2024Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation11 cited
    • 2024Origin-destination demand prediction of public transit using graph convolutional neural network11 cited
    • 2024Real-time bus arrival delays analysis using seemingly unrelated regression model11 cited
    • 2022Entropy Tucker model: Mining latent mobility patterns with simultaneous estimation of travel impedance parameters10 cited