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Study on the Accessibility and Recreational Development Potential of Lakeside Areas Based on Bike-Sharing Big Data Taking Wuhan City as an Example
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Study on the Accessibility and Recreational Development Potential of Lakeside Areas Based on Bike-Sharing Big Data Taking Wuhan City as an Example (2019)Study on the Accessibility and Recreation…A global map of travel time to cities to assess inequalities in accessibility in 2015 (2018)A global map of travel time t…Uncovering urban human mobility from large scale taxi GPS data (2015)Uncovering urban human mobili…Spatial variation of the urban taxi ridership using GPS data (2015)Spatial variation of the urba…Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Inferring trip purposes and u…Discovering urban activity patterns in cell phone data (2015)Discovering urban activity pa…Factors influencing bike share membership: An analysis of Melbourne and Brisbane (2014)Factors influencing bike shar…Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia (2014)Bike share’s impact on car us…Are Bikeshare Users Different from Regular Cyclists? (2013)Are Bikeshare Users Different…Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai (2012)Urban land uses and traffic ‘…Understanding intra-urban trip patterns from taxi trajectory data (2012)Understanding intra-urban tri…Uncovering cabdrivers’ behavior patterns from their digital traces (2010)Uncovering cabdrivers’ behavi…Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks (2010)Does Urban Mobility Have a Da…Sensing and predicting the pulse of the city through shared bicycling (2009)Sensing and predicting the pu…Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data (2009)Daily rhythms of suburban com…The bicycle as a feedering mode: experiences from three European countries (2004)The bicycle as a feedering mo…The concept of accessibility: A search for an operational form (1971)The concept of accessibility:…Pedaling to wellness: The impact of dockless bike-sharing services on physical and mental health in China (2025)Pedaling to wellness: The imp…Bikesharing in Europe, the Americas, and Asia (2010)The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study (2011)Bike Share: A Synthesis of the Literature (2013)Questions of governance: Rethinking the study of transportation policy (2017)The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets (2018)Dockless bike-sharing systems: what are the implications? (2020)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method (2016)Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic (2020)Cities and Housing. (1970)A new insight into land use classification based on aggregated mobile phone data (2014)Revealing travel patterns and city structure with taxi trip data (2015)Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)How Accessibility Shapes Land Use (1959)Is Los Angeles-Style Sprawl Desirable? (1997)Measuring Accessibility: An Exploration of Issues and Alternatives (1997)Accessibility evaluation of land-use and transport strategies: review and research directions (2003)Travel and the Built Environment: A Synthesis (2001)Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Intra-urban human mobility patterns: An urban morphology perspective (2011)Data ex Machina: Introduction to Big Data (2017)Drivers of household food availability in sub-Saharan Africa based on big data from small farms (2015)Transforming Our World: The 2030 Agenda for Sustainable Development (2018)Global maps of travel time to healthcare facilities (2020)Metropolitan areas in the world. Delineation and population trends (2020)The scales of human mobility (2020)Promoting bike-and-ride: The Dutch experience (2006)Cycling for Transportation in Sao Paulo City: Associations with Bike Paths, Train and Subway Stations (2018)Built environment effects on the integration of dockless bike-sharing and the metro (2020)Bike sharing: A review of evidence on impacts and processes of implementation and operation (2015)Understanding the usage of dockless bike sharing in Singapore (2018)A review on bike-sharing: The factors affecting bike-sharing demand (2019)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)Exploring Human Mobility Patterns in Urban Scenarios: A Trajectory Data Perspective (2018)Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi'an, Shaanxi, China (2018)Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China (2020)
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References

  • 2018A global map of travel time to cities to assess inequalities in accessibility in 20151,176 cited
  • 1971The concept of accessibility: A search for an operational form707 cited
  • 2014Bike share’s impact on car use: Evidence from the United States, Great Britain, and Australia506 cited
  • 2012Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai423 cited
  • 2004The bicycle as a feedering mode: experiences from three European countries390 cited
  • 2012Understanding intra-urban trip patterns from taxi trajectory data327 cited
  • 2014Factors influencing bike share membership: An analysis of Melbourne and Brisbane313 cited
  • 2009Sensing and predicting the pulse of the city through shared bicycling307 cited
  • 2015Uncovering urban human mobility from large scale taxi GPS data280 cited
  • 2010Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks266 cited
  • 2009Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data257 cited
  • 2015Spatial variation of the urban taxi ridership using GPS data256 cited
  • 2015Inferring trip purposes and uncovering travel patterns from taxi trajectory data254 cited
  • 2013Are Bikeshare Users Different from Regular Cyclists?

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254 cited
  • 2015Discovering urban activity patterns in cell phone data236 cited
  • 2010Uncovering cabdrivers’ behavior patterns from their digital traces217 cited
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    • 2025Pedaling to wellness: The impact of dockless bike-sharing services on physical and mental health in China4 cited