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Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems
16 references·0 citing·42 second hopsampled
Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems (2025)Spatiotemporal variability and prediction…Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems (2020)Using graph structural inform…Bike-sharing systems’ impact on modal shift: A case study in Delft, the Netherlands (2020)Bike-sharing systems’ impact …Understanding bike sharing travel patterns: An analysis of trip data from eight cities (2018)Understanding bike sharing tr…Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations (2018)Investigation on the effects …Health impacts of bike sharing systems in Europe (2018)Health impacts of bike sharin…A Dynamic Approach to Rebalancing Bike-Sharing Systems (2018)A Dynamic Approach to Rebalan…An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville (2017)An empirical analysis of bike…Mobility Modeling and Prediction in Bike-Sharing Systems (2016)Mobility Modeling and Predict…Factors influencing the choice of shared bicycles and shared electric bikes in Beijing (2016)Factors influencing the choic…Dynamic cluster-based over-demand prediction in bike sharing systems (2016)Dynamic cluster-based over-de…Traffic prediction in a bike-sharing system (2015)Traffic prediction in a bike-…Modeling Bike Sharing System using Built Environment Factors (2015)Modeling Bike Sharing System …Incentivizing Users for Balancing Bike Sharing Systems (2015)Incentivizing Users for Balan…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 …The Structure of Spatial Networks and Communities in Bicycle Sharing Systems (2013)The Structure of Spatial Netw…Approaching the Limit of Predictability in Human Mobility (2013)Approaching the Limit of Pred…Understanding individual human mobility patterns (2008)Understanding mobility based on GPS data (2008)Limits of Predictability in Human Mobility (2010)Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing (2015)Understanding the tourist mobility using GPS: Where is the next place? (2016)Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh (2016)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)Sensing and predicting the pulse of the city through shared bicycling (2009)Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)Large-Scale Vehicle Sharing Systems: Analysis of Vélib' (2012)Rebalancing Bike Sharing Systems (2016)A review of bicycle-sharing service planning problems (2020)Bikesharing in Europe, the Americas, and Asia (2010)Planning Bike Lanes based on Sharing-Bikes' Trajectories (2017)Specification Tests for the Multinomial Logit Model (1984)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)Friendship and mobility (2011)A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems (2019)A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system (2019)Using data mining techniques for bike sharing demand prediction in metropolitan city (2020)Stochastic Simulation (1987)Infrastructure, programs, and policies to increase bicycling: An international review (2009)A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile (2019)Do the Health Benefits of Cycling Outweigh the Risks? (2010)Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide (2014)Comparative life cycle assessment of station-based and dock-less bike sharing systems (2019)Physical activity of electric bicycle users compared to conventional bicycle users and non-cyclists: Insights based on health and transport data from an online survey in seven European cities (2019)Urban health: an example of a “health in all policies” approach in the context of SDGs implementation (2019)Discrete Choice Methods and their Applications to Short Term Travel Decisions (1999)Factors influencing the access duration of free-floating bike sharing as a feeder mode to the metro in Shenzhen (2020)Sharing for a circular economy? an analysis of digital sharing platforms’ principles and business models (2020)High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data (2021)A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area (1997)Improving the sustainability of integrated transportation system with bike-sharing: A spatial agent-based approach (2018)Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China (2020)Identifying the nonlinear relationship between free-floating bike sharing usage and built environment (2020)
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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
  • 2016Factors influencing the choice of shared bicycles and shared electric bikes in Beijing489 cited
  • 2013Approaching the Limit of Predictability in Human Mobility381 cited
  • 2015Traffic prediction in a bike-sharing system361 cited
  • 2017An empirical analysis of bike sharing usage and rebalancing: Evidence from Barcelona and Seville239 cited
  • 2018Health impacts of bike sharing systems in Europe231 cited
  • 2015Incentivizing Users for Balancing Bike Sharing Systems227 cited
  • 2016Dynamic cluster-based over-demand prediction in bike sharing systems204 cited
  • 2016Mobility Modeling and Prediction in Bike-Sharing Systems190 cited
  • 2020Bike-sharing systems’ impact on modal shift: A case study in Delft, the Netherlands190 cited
  • 2018Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations174 cited
  • 2015Modeling Bike Sharing System using Built Environment Factors152 cited
  • 2018A Dynamic Approach to Rebalancing Bike-Sharing Systems147 cited
  • 2013The Structure of Spatial Networks and Communities in Bicycle Sharing Systems142 cited

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  • 2018Understanding bike sharing travel patterns: An analysis of trip data from eight cities120 cited
  • 2020Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems116 cited
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