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An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City
16 references·0 citing·46 second hopsampled
An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City (2023)An interpretable machine learning framewo…Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review (2021)Machine Learning Approaches t…Bikeshare and subway ridership changes during the COVID-19 pandemic in New York City (2021)Bikeshare and subway ridershi…Bike share responses to COVID-19 (2021)Bike share responses to COVID…COVID-19 effects on shared-biking in New York, Boston, and Chicago (2020)COVID-19 effects on shared-bi…Predicting bike sharing demand using recurrent neural networks (2019)Predicting bike sharing deman…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…Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City (2019)Gender gap generators for bik…From non-cyclists to frequent cyclists: Factors associated with frequent bike share use in New York City (2019)From non-cyclists to frequent…Exploring the health and spatial equity implications of the New York City Bike share system (2019)Exploring the health and spat…Climate Change and Physical Activity: Estimated Impacts of Ambient Temperatures on Bikeshare Usage in New York City (2019)Climate Change and Physical A…A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City (2019)A longitudinal study of bike …Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems (2018)Use of Deep Learning to Predi…Quantifying the Equity of Bikeshare Access in U.S. Cities (2016)Quantifying the Equity of Bik…Bike-share rebalancing strategies, patterns, and purpose (2016)Bike-share rebalancing strate…Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto (2015)Effects of built environment …Travel and the Built Environment: A Synthesis (2001)Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures (2003)Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Environmental benefits of bike sharing: A big data-based analysis (2018)A review on bike-sharing: The factors affecting bike-sharing demand (2019)Built environment effects on the integration of dockless bike-sharing and the metro (2020)Determinants of bicycle use: do municipal policies matter? (2004)Where do cyclists ride? A route choice model developed with revealed preference GPS data (2012)Are Bikeshare Users Different from Regular Cyclists? (2013)Breaking Barriers to Bike Share: Insights from Residents of Traditionally Underserved Neighborhoods (2017)How fair is the allocation of bike-sharing infrastructure? Framework for a qualitative and quantitative spatial fairness assessment (2020)The motivations for using bike sharing during the COVID-19 pandemic: Insights from Lisbon (2021)Bikesharing in Europe, the Americas, and Asia (2010)Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia (2014)Roadmap for free-floating bikeshare research and practice in North America (2019)Enhancing equitable service level: Which can address better, dockless or dock-based Bikeshare systems? (2020)The rebalancing of bike-sharing system under flow-type task window (2020)Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns (2011)Factors influencing the choice of shared bicycles and shared electric bikes in Beijing (2016)Bike sharing usage prediction with deep learning: a survey (2022)Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning (2022)Statistical and Econometric Methods for Transportation Data Analysis (2003)Built Environment Correlates of Walking (2008)Infrastructure, programs, and policies to increase bicycling: An international review (2009)An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China (2020)Exploring the relationship between bike-sharing and public transport in Poznań, Poland (2021)The effects of built environment spatial variation on bike-sharing usage: A case study of Suzhou, China (2021)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)Environmental factors associated with adults' participation in physical activity A review (2002)The relative influence of individual, social and physical environment determinants of physical activity (2002)Health benefits of cycling: a systematic review (2011)Using weather forecasts to forecast whether bikes are used (2020)How does weather affect bikeshare use? A comparative analysis of forty cities across climate zones (2021)Heat and mobility: Machine learning perspectives on bike-sharing resilience in Shanghai (2025)MEASURING SPRAWL AND ITS IMPACT (2002)Statistical and Econometric Methods for Transportation Data Analysis (2020)Impacts of the COVID-19 Pandemic on Active Travel Mode Choice in Bangladesh: A Study from the Perspective of Sustainability and New Normal Situation (2021)COVID-19 is linked to changes in the time–space dimension of human mobility (2023)Making Cycling Irresistible: Lessons from The Netherlands, Denmark and Germany (2008)Commuting by Bicycle: An Overview of the Literature (2009)Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method (2021)Neighborhood, built environment and resilience in transportation during the COVID-19 pandemic (2022)Modeling bike availability in a bike-sharing system using machine learning (2017)
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References

  • 2015Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto570 cited
  • 2021Bikeshare and subway ridership changes during the COVID-19 pandemic in New York City171 cited
  • 2019Gender gap generators for bike share ridership: Evidence from Citi Bike system in New York City150 cited
  • 2016Bike-share rebalancing strategies, patterns, and purpose105 cited
  • 2020COVID-19 effects on shared-biking in New York, Boston, and Chicago93 cited
  • 2019Climate Change and Physical Activity: Estimated Impacts of Ambient Temperatures on Bikeshare Usage in New York City92 cited
  • 2019Predicting bike sharing demand using recurrent neural networks90 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
  • 2016Quantifying the Equity of Bikeshare Access in U.S. Cities75 cited
  • 2021Bike share responses to COVID-1968 cited
  • 2021Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review62 cited
  • 2019Exploring the health and spatial equity implications of the New York City Bike share system58 cited
  • 2019A longitudinal study of bike infrastructure impact on bikesharing system performance in New York City

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55 cited
  • 2018Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems42 cited
  • 2019From non-cyclists to frequent cyclists: Factors associated with frequent bike share use in New York City39 cited
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