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Investigating the impact of spatial-temporal grid size on the microscopic forecasting of the inflow and outflow gap in a free-floating bike-sharing system
15 references·9 citing·45 second hopsampled
Investigating the impact of spatial-temporal grid size on the microscopic forecasting of the inflow and outflow gap in a free-floating bike-sharing system (2021)Investigating the impact of spatial-tempo…The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes (2019)The analysis of catchment are…Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours (2019)Optimizing the Location of Vi…Modeling bike counts in a bike-sharing system considering the effect of weather conditions (2019)Modeling bike counts in a bik…Modeling bike counts in a bike-sharing system considering the effect of weather conditions (2019)Modeling bike counts in a bik…Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China (2019)Estimating the parking demand…Dynamic evolution of demand fluctuation in bike-sharing systems for green travel (2019)Dynamic evolution of demand f…Considering user behavior in free-floating bike sharing system design: A data-informed spatial agent-based model (2019)Considering user behavior in …Biking islands in cities: An analysis combining bike trajectory and percolation theory (2019)Biking islands in cities: An …Analysis on spatiotemporal urban mobility based on online car-hailing data (2019)Analysis on spatiotemporal ur…A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system (2019)A model framework for discove…The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets (2018)The station-free sharing bike…A modeling framework for the dynamic management of free-floating bike-sharing systems (2018)A modeling framework for the …Validation of a Relocation Strategy for Munich's Bike Sharing System (2016)Validation of a Relocation St…Mobility Modeling and Prediction in Bike-Sharing Systems (2016)Mobility Modeling and Predict…Traffic prediction in a bike-sharing system (2015)Traffic prediction in a bike-…Ensemble Learning with Systematic Hyperparameter Optimization for Urban-Bike-Sharing Demand Prediction (2026)Ensemble Learning with System…Assessing the impact of spatial scales in modelling urban mobility pattern (2026)Assessing the impact of spati…Optimal location of free-floating bike-sharing dispatching hubs: A multi-scale perspective (2025)Optimal location of free-floa…Designing online learning algorithms for electric fence placement in dockless bike-sharing system utilizing limited random parking strategies (2024)Designing online learning alg…CGA-STNet: A dockless shared bicycle demand prediction model considering multiple spatial features and time periodicity (2024)CGA-STNet: A dockless shared …Adaptive generative adjustable electric fence method and internal obstacle detection (2024)Adaptive generative adjustabl…Recovering Real Demand for Free-Floating Bike-Sharing System Considering Demand Truncation, Migration, and Spatial Correlation (2023)Recovering Real Demand for Fr…Analysing and visualising bike-sharing demand with outliers (2023)Analysing and visualising bik…A Demand Truncation and Migration Poisson Model for Real Demand Inference in Free-Floating Bike-Sharing System (2023)A Demand Truncation and Migra…Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Bikesharing in Europe, the Americas, and Asia (2010)Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)Dynamic cluster-based over-demand prediction in bike sharing systems (2016)Rebalancing Bike Sharing Systems (2016)Planning Bike Lanes based on Sharing-Bikes' Trajectories (2017)Friendship and mobility (2011)A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems (2019)Using data mining techniques for bike sharing demand prediction in metropolitan city (2020)Strategic design of public bicycle sharing systems with service level constraints (2010)Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns (2011)Optimizing the location of stations in bike-sharing programs: A GIS approach (2012)Mapping the bike sharing research published from 2010 to 2018: A scientometric review (2018)A review of bicycle-sharing service planning problems (2020)Statistical and Econometric Methods for Transportation Data Analysis (2003)Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto (2015)Deep learning with long short-term memory networks for financial market predictions (2017)A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data (2020)Identifying the nonlinear relationship between free-floating bike sharing usage and built environment (2020)MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY (1990)A continuous approximation model for the optimal design of public bike-sharing systems (2019)Development of a station-level demand prediction and visualization tool to support bike-sharing systems’ operators (2020)Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review (2021)Understanding individual human mobility patterns (2008)Power-Law Distributions in Empirical Data (2009)Power-law distributions in empirical data (2018)Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China (2020)First-mover firms in the transition towards the sharing economy in metallic natural resource-intensive industries: Implications for the circular economy and emerging industry 4.0 technologies (2020)Bike Share: A Synthesis of the Literature (2013)Environmental benefits of bike sharing: A big data-based analysis (2018)Understanding the usage of dockless bike sharing in Singapore (2018)Place-varying impacts of urban rail transit on property prices in Shenzhen, China: Insights for value capture (2020)Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China (2021)Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity (2022)Who Belongs in the Family? (1953)Bikeshare: A Review of Recent Literature (2015)Understanding the determinants of young commuters’ metro-bikeshare usage frequency using big data (2020)What factors influence ridership of station-based bike sharing and free-floating bike sharing at rail transit stations? (2021)Comparison of station-based and free-floating bikeshare systems as feeder modes to the metro (2023)Discovering regions of different functions in a city using human mobility and POIs (2012)Unravelling daily human mobility motifs (2013)Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model (2021)Exploring the nonlinear and asymmetric influences of built environment on CO2 emission of ride-hailing trips (2021)Urban Vitality and its Influencing Factors: Comparative Analysis Based on Taxi Trajectory Data (2022)Modeling bike availability in a bike-sharing system using machine learning (2017)
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References

  • 2015Traffic prediction in a bike-sharing system361 cited
  • 2018The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets291 cited
  • 2018A modeling framework for the dynamic management of free-floating bike-sharing systems269 cited
  • 2019A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system195 cited
  • 2016Mobility Modeling and Prediction in Bike-Sharing Systems190 cited
  • 2019The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes136 cited
  • 2019Analysis on spatiotemporal urban mobility based on online car-hailing data97 cited
  • 2019Modeling bike counts in a bike-sharing system considering the effect of weather conditions80 cited
  • 2019Modeling bike counts in a bike-sharing system considering the effect of weather conditions77 cited
  • 2019Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China77 cited
  • 2019Considering user behavior in free-floating bike sharing system design: A data-informed spatial agent-based model55 cited
  • 2019Biking islands in cities: An analysis combining bike trajectory and percolation theory40 cited
  • 2016Validation of a Relocation Strategy for Munich's Bike Sharing System36 cited
  • 2019

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Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours
25 cited
  • 2019Dynamic evolution of demand fluctuation in bike-sharing systems for green travel24 cited
  • Cited by

    • 2023A Demand Truncation and Migration Poisson Model for Real Demand Inference in Free-Floating Bike-Sharing System14 cited
    • 2024CGA-STNet: A dockless shared bicycle demand prediction model considering multiple spatial features and time periodicity9 cited
    • 2025Optimal location of free-floating bike-sharing dispatching hubs: A multi-scale perspective9 cited
    • 2023Analysing and visualising bike-sharing demand with outliers7 cited
    • 2023Recovering Real Demand for Free-Floating Bike-Sharing System Considering Demand Truncation, Migration, and Spatial Correlation4 cited
    • 2024Adaptive generative adjustable electric fence method and internal obstacle detection3 cited
    • 2024Designing online learning algorithms for electric fence placement in dockless bike-sharing system utilizing limited random parking strategies3 cited
    • 2026Ensemble Learning with Systematic Hyperparameter Optimization for Urban-Bike-Sharing Demand Prediction2 cited
    • 2026Assessing the impact of spatial scales in modelling urban mobility pattern2 cited