SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems
16 references·0 citing·46 second hop
Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems (2024)Mitigating biases in big mobility data: a…Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning (2023)Algorithmic fairness and bias…Understanding the Demographics of Twitter Users (2021)Understanding the Demographic…Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle (2021)Time lag effects of COVID-19 …Population Bias in Geotagged Tweets (2021)Population Bias in Geotagged …Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges (2021)Human mobility data in the CO…The impacts of COVID-19 pandemic on public transit demand in the United States (2020)The impacts of COVID-19 pande…Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning (2020)Nonlinear effect of accessibi…On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)On data processing required t…Understanding the bias of call detail records in human mobility research (2016)Understanding the bias of cal…The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)The promises of big data and …Dynamic population mapping using mobile phone data (2014)Dynamic population mapping us…A Tale of Cities: Urban Biases in Volunteered Geographic Information (2014)A Tale of Cities: Urban Biase…The impact of biases in mobile phone ownership on estimates of human mobility (2013)The impact of biases in mobil…Are call detail records biased for sampling human mobility? (2012)Are call detail records biase…Estimating Propensity Adjustments for Volunteer Web Surveys (2011)Estimating Propensity Adjustm…Correlating mobile phone usage and travel behavior – A case study of Harbin, China (2011)Correlating mobile phone usag…WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Empirical Model Building and Response Surfaces (1988)Understanding individual human mobility patterns (2008)Discovering Spatial Interaction Communities from Mobile Phone D ata (2013)Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Computational Social Science (2009)Limits of Predictability in Human Mobility (2010)A survey of results on mobile phone datasets analysis (2015)Human development report 2015: work for human development (2015)Predicting poverty and wealth from mobile phone metadata (2015)Reality mining: sensing complex social systems (2005)Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges (2016)Gender gaps in urban mobility (2020)Twitter mood predicts the stock market (2011)A Three‐Stage Adoption Process for Social Media Use in Government (2013)Psychological Language on Twitter Predicts County-Level Heart Disease Mortality (2015)Urban mobility and neighborhood isolation in America’s 50 largest cities (2018)Prospect Theory: An Analysis of Decision under Risk (1979)Prospect theory: An analysis of decision under risk (1988)The theory of planned behavior (1991)Applying mobile phone data to travel behaviour research: A literature review (2017)Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Big data and analytics in hospitality and tourism: a systematic literature review (2021)Modelling the scaling properties of human mobility (2010)Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)Transport mode detection based on mobile phone network data: A systematic review (2019)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)Spatial Autocorrelation: Trouble or New Paradigm? (1993)Citizens as sensors: the world of volunteered geography (2007)Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy (2016)Social media and political communication in the 2014 elections to the European Parliament (2016)Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019)Travel Behavior as Function of Accessibility, Land Use Mixing, and Land Use Balance: Evidence from San Francisco Bay Area (1997)A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels (2006)Linking urban transport and land use in developing countries (2013)Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity (2021)Comparing urban form influences on travel distance, car ownership, and mode choice (2024)Nonlinear and threshold effects of the built environment, road vehicles and air pollution on urban vitality (2024)Understanding commuting patterns using transit smart card data (2016)Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period (2021)The impact of the COVID-19 pandemic on people's mobility: A longitudinal study of the U.S. from March to September of 2020 (2021)Why has public transit ridership declined in the United States? (2022)Does ICT diffusion lead to energy efficiency and environmental sustainability in emerging Asian economies? (2021)RETRACTED ARTICLE: The impact of COVID-19 on small- and medium-sized enterprises (SMEs): empirical evidence for green economic implications (2022)Visualizing the sustainable development goals and natural resource utilization for green economic recovery after COVID-19 pandemic (2022)
this work references cited by second hop

References

  • 2014Dynamic population mapping using mobile phone data908 cited
  • 2021Understanding the Demographics of Twitter Users760 cited
  • 2016The promises of big data and small data for travel behavior (aka human mobility) analysis580 cited
  • 2020The impacts of COVID-19 pandemic on public transit demand in the United States297 cited
  • 2011Correlating mobile phone usage and travel behavior – A case study of Harbin, China252 cited
  • 2017On data processing required to derive mobility patterns from passively-generated mobile phone data248 cited
  • 2013The impact of biases in mobile phone ownership on estimates of human mobility246 cited
  • 2021Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges217 cited
  • 2014A Tale of Cities: Urban Biases in Volunteered Geographic Information215 cited
  • 2020Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning192 cited
  • 2011Estimating Propensity Adjustments for Volunteer Web Surveys167 cited
  • 2021Population Bias in Geotagged Tweets164 cited
  • 2016Understanding the bias of call detail records in human mobility research146 cited
  • 2023Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning142 cited

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

  • 2012Are call detail records biased for sampling human mobility?124 cited
  • 2021Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle122 cited
  • Cited by

    No citing works inside the corpus for this window.