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Spatial Analysis of Bikeshare Ridership With Smart Card and POI Data Using Geographically Weighted Regression Method

16 references·16 citing·36 second hopsampled

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References

1988Spatial Econometrics: Methods and Models9,595 cited
2002Geographically Weighted Regression: The Analysis of Spatially Varying Relationships3,946 cited
1974Spatial Autocorrelation1,232 cited
2010Bikesharing in Europe, the Americas, and Asia1,149 cited
2014How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal486 cited
2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited
2013Mining bicycle sharing data for generating insights into sustainable transport systems368 cited
2012Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use342 cited
2014The impact of weather conditions on bikeshare trips in Washington, DC330 cited
2014Factors influencing bike share membership: An analysis of Melbourne and Brisbane313 cited
2012Large-Scale Vehicle Sharing Systems: Analysis of Vélib'291 cited
2013Station-Level Forecasting of Bikesharing Ridership277 cited
2016Bikeshare trip generation in New York City258 cited
2015Spatial variation of the urban taxi ridership using GPS data256 cited
2016

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Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system

235 cited

2013Public Bikesharing and Modal Shift Behavior: A Comparative Study of Early Bikesharing Systems in North America164 cited

Cited by

2022Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage39 cited
2022The Impact of the Neighborhood Built Environment on the Walking Activity of Older Adults: A Multi-Scale Spatial Heterogeneity Analysis29 cited
2021Spatiotemporal Heterogeneity Analysis of Influence Factor on Urban Rail Transit Station Ridership29 cited
2022Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm27 cited
2019Short‐term FFBS demand prediction with multi‐source data in a hybrid deep learning framework27 cited
2021Destination Prediction Based on Virtual POI Docks in Dockless Bike-Sharing System24 cited
2023How Do Land Use, Built Environment and Transportation Facilities Affect Bike-Sharing Trip Destinations?21 cited
2023Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems18 cited
2020BikeWay: A Multi-Sensory Fuzzy-Based Quality Metric for Bike Paths and Tracks in Urban Areas16 cited
2023Exploring the spatiotemporal factors affecting bicycle-sharing demand during the COVID-19 pandemic16 cited
2023Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul15 cited
2020Soft Sensor with Deep Learning for Functional Region Detection in Urban Environments14 cited
2025BikeshareGAN: Predicting dockless bike-sharing demand based on satellite image10 cited
2021Human Mobility Prediction With Region-Based Flows and Water Consumption10 cited
2021Predicting Travel Demand of a Docked Bikesharing System Based on LSGC-LSTM Networks8 cited
2025An XGBoost-Based Three-Stage Prediction Approach for True User Demand of Bike-Sharing Systems Based on Spatio-Temporal Analysis4 cited