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Bike sharing station placement leveraging heterogeneous urban open data
11 references·16 citing·41 second hop
Bike sharing station placement leveraging heterogeneous urban open data (2015)Bike sharing station placement leveraging…Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Modeling User Activity Prefer…Geo-spotting (2013)Geo-spottingOptimizing the location of stations in bike-sharing programs: A GIS approach (2012)Optimizing the location of st…Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering regions of differ…China's Hangzhou Public Bicycle (2011)China's Hangzhou Public Bicyc…Bikesharing in Europe, the Americas, and Asia (2010)Bikesharing in Europe, the Am…Sensing and predicting the pulse of the city through shared bicycling (2009)Sensing and predicting the pu…Infrastructure, programs, and policies to increase bicycling: An international review (2009)Infrastructure, programs, and…Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Bike-sharing: History, Impact…Significance Testing of the Spearman Rank Correlation Coefficient (1972)Significance Testing of the S…Significance Testing of the Spearman Rank Correlation Coefficient (1972)Significance Testing of the S…RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems (2022)RedPacketBike: A Graph-Based …Demand And/oR Equity (DARE) method for planning bike-sharing (2021)Demand And/oR Equity (DARE) m…Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns (2021)Analysis on bike-share riders…Using metaheuristics for the location of bicycle stations (2020)Using metaheuristics for the …Predicting station level demand in a bike‐sharing system using recurrent neural networks (2020)Predicting station level dema…A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things (2020)A Hybrid Machine Learning Mod…User profiling from their use of smartphone applications: A survey (2019)User profiling from their use…Optimizing Rebalance Scheme for Dock-Less Bike Sharing Systems with Adaptive User Incentive (2019)Optimizing Rebalance Scheme f…Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems (2019)Mobility Modeling and Data-Dr…Large-scale trip planning for bike-sharing systems (2019)Large-scale trip planning for…Decentralized Attention-based Personalized Human Mobility Prediction (2019)Decentralized Attention-based…Online Deep Ensemble Learning for Predicting Citywide Human Mobility (2018)Online Deep Ensemble Learning…Mining large-scale human mobility data for long-term crime prediction (2018)Mining large-scale human mobi…BRAVO (2018)BRAVOOpen PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas (2017)Open PFLOW: Creation and eval…Fine-Grained Urban Event Detection and Characterization Based on Tensor Cofactorization (2016)Fine-Grained Urban Event Dete…The Proof and Measurement of Association between Two Things (1904)Spearman Rank Correlation Coefficient (2005)Speech emotion recognition based on feature selection and extreme learning machine decision tree (2017)Relating Environmental, Social, and Governance scores and sustainability performances of firms: An empirical analysis (2019)China statistical yearbook (1988)Diffusion of innovations. (2003)Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use (2012)Factors influencing bike share membership: An analysis of Melbourne and Brisbane (2014)Sustainable bike-sharing systems: characteristics and commonalities across cases in urban China (2014)Diffusion of Innovations 1 (2019)Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph (1964)The Location of Emergency Service Facilities (1971)THE MAXIMAL COVERING LOCATION PROBLEM (1974)Bike sharing: A review of evidence on impacts and processes of implementation and operation (2015)Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations (2015)A review on bike-sharing: The factors affecting bike-sharing demand (2019)Friendship and mobility (2011)Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks (2016)Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach (2019)Exploring Millions of Footprints in Location Sharing Services (2021)A survey on deep learning for human mobility (2023)Promoting Safe Walking and Cycling to Improve Public Health: Lessons From The Netherlands and Germany (2003)Making Cycling Irresistible: Lessons from The Netherlands, Denmark and Germany (2008)Influences of Built Environments on Walking and Cycling: Lessons from Bogotá (2009)Role of Built Environments in Physical Activity, Obesity, and Cardiovascular Disease (2012)City planning and population health: a global challenge (2016)Systematic literature review of built environment effects on physical activity and active transport – an update and new findings on health equity (2017)Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns (2011)Large-Scale Vehicle Sharing Systems: Analysis of Vélib' (2012)Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach (2013)Rank Correlation Methods. (1957)The proof and measurement of association between two things (2010)Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies (2011)Bikeshare: A Review of Recent Literature (2015)Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto (2015)Bike Share: A Synthesis of the Literature (2013)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017)
this work references cited by second hop

References

  • 2009Infrastructure, programs, and policies to increase bicycling: An international review1,366 cited
  • 2010Bikesharing in Europe, the Americas, and Asia1,149 cited
  • 2012Discovering regions of different functions in a city using human mobility and POIs1,136 cited
  • 2009Bike-sharing: History, Impacts, Models of Provision, and Future1,070 cited
  • 2014Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs857 cited
  • 1972Significance Testing of the Spearman Rank Correlation Coefficient703 cited
  • 2012Optimizing the location of stations in bike-sharing programs: A GIS approach436 cited
  • 1972Significance Testing of the Spearman Rank Correlation Coefficient396 cited
  • 2011China's Hangzhou Public Bicycle320 cited
  • 2009Sensing and predicting the pulse of the city through shared bicycling307 cited
  • 2013Geo-spotting208 cited

Cited by

  • 2018Mining large-scale human mobility data for long-term crime prediction87 cited
  • 2020A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things73 cited
  • 2021Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns64 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2020Predicting station level demand in a bike‐sharing system using recurrent neural networks63 cited
  • 2019User profiling from their use of smartphone applications: A survey60 cited
  • 2016Fine-Grained Urban Event Detection and Characterization Based on Tensor Cofactorization57 cited
  • 2018Online Deep Ensemble Learning for Predicting Citywide Human Mobility53 cited
  • 2019Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems49 cited
  • 2021Demand And/oR Equity (DARE) method for planning bike-sharing45 cited
  • 2019Optimizing Rebalance Scheme for Dock-Less Bike Sharing Systems with Adaptive User Incentive41 cited
  • 2017Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas40 cited
  • 2018BRAVO35 cited
  • 2019Decentralized Attention-based Personalized Human Mobility Prediction32 cited
  • 2020Using metaheuristics for the location of bicycle stations31 cited
  • 2019Large-scale trip planning for bike-sharing systems26 cited
  • 2022RedPacketBike: A Graph-Based Demand Modeling and Crowd-Driven Station Rebalancing Framework for Bike Sharing Systems25 cited