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Analyzing Key Factors Influencing Human Mobility Before and During COVID ‐19 With Explainable Machine Learning
16 references·0 citing·49 second hop
Analyzing Key Factors Influencing Human Mobility Before and During COVID ‐19 With Explainable Machine Learning (2024)Analyzing Key Factors Influencing Human M…Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data (2023)Environmental factors for out…Understanding socioeconomic disparities in travel behavior during the COVID‐19 pandemic (2021)Understanding socioeconomic d…The Influence of the Covid-19 Pandemic on Mobility Patterns: The First Wave’s Results (2021)The Influence of the Covid-19…Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic (2021)Staying at Home Is a Privileg…Large socio-economic, geographic and demographic disparities exist in exposure to school closures (2021)Large socio-economic, geograp…Exploring the travel behavior changes caused by the COVID-19 crisis: A case study for a developing country (2021)Exploring the travel behavior…Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data (2020)Understanding the Impact of t…Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle (2020)Mobile phone data for informi…Mapping county-level mobility pattern changes in the United States in response to COVID-19 (2020)Mapping county-level mobility…Urban Human Mobility (2019)Urban Human MobilityHuman mobility and socioeconomic status: Analysis of Singapore and Boston (2018)Human mobility and socioecono…Weather, transport mode choices and emotional travel experiences (2016)Weather, transport mode choic…Analysis of human mobility patterns from GPS trajectories and contextual information (2015)Analysis of human mobility pa…Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review (2012)Impact of Everyday Weather on…Understanding individual human mobility patterns (2008)Understanding individual huma…17. 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Examining non-linear effects of streetscape greenery on walking propensity of older adults (2021)Crafting a jogging-friendly city: Harnessing big data to evaluate the runnability of urban streets (2024)Examining the nonlinear relationship between neighborhood environment and residents' health (2024)Utilizing Multi-Source Geospatial Big Data to Examine How Environmental Factors Attract Outdoor Jogging Activities (2024)Game theoretic analysis of a bankruptcy problem from the Talmud (1985)Monotonic solutions of cooperative games (1985)Decomposition procedures for distributional analysis: a unified framework based on the Shapley value (2012)Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study (2020)Emerging COVID-19 impacts, responses, and lessons for building resilience in the seafood system (2021)A survey on deep learning for human mobility (2023)
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References

  • 195317. A Value for n-Person Games6,944 cited
  • 2008Understanding individual human mobility patterns6,071 cited
  • 2020Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle630 cited
  • 2012Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review416 cited
  • 2015Analysis of human mobility patterns from GPS trajectories and contextual information275 cited
  • 2018Human mobility and socioeconomic status: Analysis of Singapore and Boston237 cited
  • 2020Mapping county-level mobility pattern changes in the United States in response to COVID-19226 cited
  • 2021Large socio-economic, geographic and demographic disparities exist in exposure to school closures217 cited
  • 2021Understanding socioeconomic disparities in travel behavior during the COVID‐19 pandemic179 cited
  • 2023Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data176 cited
  • 2016Weather, transport mode choices and emotional travel experiences166 cited
  • 2021Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic144 cited
  • 2021Exploring the travel behavior changes caused by the COVID-19 crisis: A case study for a developing country136 cited

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2021
The Influence of the Covid-19 Pandemic on Mobility Patterns: The First Wave’s Results
123 cited
  • 2019Urban Human Mobility111 cited
  • 2020Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data109 cited
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