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Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA
6 references·16 citing·44 second hopsampled
Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA (2020)Mobile device location data reveal human …Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)Inferring dynamic origin-dest…Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)Modeling real-time human mobi…Forecasting current and next trip purpose with social media data and Google Places (2018)Forecasting current and next …Understanding congested travel in urban areas (2016)Understanding congested trave…National Household Travel Survey (2014)National Household Travel Sur…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…Peaked too soon? Analyzing the shifting patterns of PM peak period travel in Southern California (2024)Peaked too soon? Analyzing th…Exploring the changes of individuals’ travel behavior in response to COVID-19 and their influencing factors based on mobile phone data (2024)Exploring the changes of indi…Changing associations of built environment with usage of urban space due to the COVID-19 pandemic in the United States (2024)Changing associations of buil…The Empirical Study of Human Mobility: Potentials and Pitfalls of Using Traditional and Digital Data (2023)The Empirical Study of Human …Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach (2023)Revealing human mobility tren…Replacing discontinued Big Tech mobility reports: a penetration-based analysis (2023)Replacing discontinued Big Te…Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments (2023)Quantifying the impact of COV…National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods (2023)National-Level Multimodal Ori…Examining the interactions between working from home, travel behavior and change in car ownership due to the impact of COVID-19 (2023)Examining the interactions be…Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data (2023)Big-Data Driven Framework to …Residency and worker status identification based on mobile device location data (2022)Residency and worker status i…Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas (2022)Aggravated social segregation…A tale of two Americas: Socio-economic mobility gaps within and across American cities before and during the pandemic (2022)A tale of two Americas: Socio…Is travel behaviour an equity issue? Using GPS location data to assess the effects of income and supermarket availability on travel reduction during the COVID-19 pandemic (2021)Is travel behaviour an equity…Analysis of Changes in Intercity Highway Traffic Travel Patterns under the Impact of COVID-19 (2021)Analysis of Changes in Interc…A data-driven travel mode share estimation framework based on mobile device location data (2021)A data-driven travel mode sha…The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Human mobility: Models and applications (2018)Potential Games (1996)The statistical physics of cities (2019)Deconstructing laws of accessibility and facility distribution in cities (2020)Gender gaps in urban mobility (2020)Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake (2010)How Large U.S. Companies Can Use Twitter and Other Social Media to Gain Business Value (2010)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Reviewing trip purpose imputation in GPS-based travel surveys (2020)Understanding temporal and spatial patterns of urban activities across demographic groups through geotagged social media data (2023)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)The concept of accessibility: A search for an operational form (1971)Promoting Safe Walking and Cycling to Improve Public Health: Lessons From The Netherlands and Germany (2003)Protective Association Between Neighborhood Walkability and Depression in Older Men (2007)Human Settlements, Infrastructure, and Spatial Planning (2015)Reducing car dependence in the heart of Europe: lessons from Germany, Austria, and Switzerland (2016)Smart card data use in public transit: A literature review (2011)A survey of results on mobile phone datasets analysis (2015)Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions (2019)Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China (2021)The Data Model Concept in Statistical Mapping (1967)Dynamic activity chain pattern estimation under mobility demand changes during COVID-19 (2021)A Framework of Travel Mode Identification Fusing Deep Learning and Map-Matching Algorithm (2023)Using mobile phone big data and street view images to explore the mismatch between walkability and walking behavior (2024)American Apartheid: Segregation and the Making of the Underclass (1990)Social Vulnerability to Environmental Hazards * (2003)Gauging urban resilience in the United States during the COVID-19 pandemic via social network analysis (2023)Where do people meet? Time-series clustering for social interaction levels in daily-life spaces during the COVID-19 pandemic (2023)Revealing disparities and driving factors in leisure activity segregation of residents and tourists: A data-driven analysis of smart phone data (2025)Methodologies for data quality assessment and improvement (2009)Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories (2024)Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19 (2024)The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects (2004)Estimation of spatial autoregressive panel data models with fixed effects (2009)Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data (2014)Assessing the long-term impact of COVID-19 on travel behavior: The United Arab Emirates perspective (2023)Evaluating travel behavior resilience across urban and Rural areas during the COVID-19 Pandemic: Contributions of vaccination and epidemiological indicators (2024)Exploring the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka (2024)Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example (2019)New and emerging data forms in transportation planning and policy: Opportunities and challenges for “Track and Trace” data (2020)Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform (2021)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2014National Household Travel Survey479 cited
  • 2016Understanding congested travel in urban areas406 cited
  • 2019Inferring dynamic origin-destination flows by transport mode using mobile phone data183 cited
  • 2018Modeling real-time human mobility based on mobile phone and transportation data fusion150 cited
  • 2018Forecasting current and next trip purpose with social media data and Google Places83 cited

Cited by

  • 2021A data-driven travel mode share estimation framework based on mobile device location data44 cited
  • 2022Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas37 cited
  • 2023Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments21 cited
  • 2022Residency and worker status identification based on mobile device location data20 cited
  • 2024Exploring the changes of individuals’ travel behavior in response to COVID-19 and their influencing factors based on mobile phone data19 cited
  • 2023Examining the interactions between working from home, travel behavior and change in car ownership due to the impact of COVID-1918 cited
  • 2022A tale of two Americas: Socio-economic mobility gaps within and across American cities before and during the pandemic17 cited

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  • 2023Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data17 cited
  • 2023National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods13 cited
  • 2021Analysis of Changes in Intercity Highway Traffic Travel Patterns under the Impact of COVID-1912 cited
  • 2023Replacing discontinued Big Tech mobility reports: a penetration-based analysis11 cited
  • 2021Is travel behaviour an equity issue? Using GPS location data to assess the effects of income and supermarket availability on travel reduction during the COVID-19 pandemic10 cited
  • 2023The Empirical Study of Human Mobility: Potentials and Pitfalls of Using Traditional and Digital Data8 cited
  • 2024Peaked too soon? Analyzing the shifting patterns of PM peak period travel in Southern California6 cited
  • 2023Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach6 cited
  • 2024Changing associations of built environment with usage of urban space due to the COVID-19 pandemic in the United States5 cited