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A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems
6 references·1 citing·31 second hop
A Deep Learning Approach for Estimating Inventory Rebalancing Demand in Bicycle Sharing Systems (2018)A Deep Learning Approach for Estimating I…Rebalancing Bike Sharing Systems (2016)Rebalancing Bike Sharing Syst…Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem (2014)Proactive vehicle routing wit…Optimal inventory management of a bike-sharing station (2013)Optimal inventory management …A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2012)A review and comparison of st…Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)Urban cycles and mobility pat…Sensing and predicting the pulse of the city through shared bicycling (2009)Sensing and predicting the pu…On the simultaneous computation of target inventories and intervals for bimodal bike-sharing systems (2024)On the simultaneous computati…Large-Scale Vehicle Sharing Systems: Analysis of Vélib' (2012)Bike-share rebalancing strategies, patterns, and purpose (2016)Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests (2017)A comparison of users’ characteristics between station-based bikesharing system and free-floating bikesharing system: case study in Hangzhou, China (2018)Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Strategic design of public bicycle sharing systems with service level constraints (2010)A time-space network flow approach to dynamic repositioning in bicycle sharing systems (2016)A review of bicycle-sharing service planning problems (2020)The Combination of Forecasts (1969)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting with artificial neural networks: (1998)Machine Learning Strategies for Time Series Forecasting (2013)Time series forecasting using artificial neural networks methodologies: A systematic review (2018)An optimized model using LSTM network for demand forecasting (2020)Time Series Analysis: Forecasting and Control. (1978)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Mining bicycle sharing data for generating insights into sustainable transport systems (2013)Traffic prediction in a bike-sharing system (2015)A review on bike-sharing: The factors affecting bike-sharing demand (2019)Bikesharing in Europe, the Americas, and Asia (2010)Optimizing the location of stations in bike-sharing programs: A GIS approach (2012)Dynamic Bike Reposition (2018)Citywide Bike Usage Prediction in a Bike-Sharing System (2019)A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things (2020)Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)The scaling laws of human travel (2006)Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns (2011)Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach (2013)Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems (2013)Mapping the bike sharing research published from 2010 to 2018: A scientometric review (2018)
this work references cited by second hop

References

  • 2012A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition745 cited
  • 2010Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system446 cited
  • 2009Sensing and predicting the pulse of the city through shared bicycling307 cited
  • 2013Optimal inventory management of a bike-sharing station272 cited
  • 2016Rebalancing Bike Sharing Systems194 cited
  • 2014Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem148 cited

Cited by

  • 2024On the simultaneous computation of target inventories and intervals for bimodal bike-sharing systems5 cited

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