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Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data
16 references·1 citing·34 second hop
Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data (2023)Big-Data Driven Framework to Estimate Veh…Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform (2021)Interactive COVID-19 Mobility…A data-driven travel mode share estimation framework based on mobile device location data (2021)A data-driven travel mode sha…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 r…A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France) (2020)A data-driven approach for or…Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)Inferring dynamic origin-dest…Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example (2019)Extracting trips from multi-s…On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)On data processing required t…Understanding congested travel in urban areas (2016)Understanding congested trave…The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)The promises of big data and …Returners and explorers dichotomy in human mobility (2015)Returners and explorers dicho…Traffic Flow Estimation Models Using Cellular Phone Data (2012)Traffic Flow Estimation Model…Towards Estimating Urban Population Distributions from Mobile Call Data (2012)Towards Estimating Urban Popu…Intra-urban human mobility patterns: An urban morphology perspective (2011)Intra-urban human mobility pa…Modelling the scaling properties of human mobility (2010)Modelling the scaling propert…Limits of Predictability in Human Mobility (2010)Limits of Predictability in H…Understanding individual human mobility patterns (2008)Understanding individual huma…Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19 (2024)Using Geographically Weighted…The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Reality mining: sensing complex social systems (2005)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events (2010)The digital matatu project: Using cell phones to create an open source data for Nairobi's semi-formal bus system (2015)Understanding the Impacts of Human Mobility on Accessibility Using Massive Mobile Phone Tracking Data (2018)Urban sensing based on mobile phone data: approaches, applications, and challenges (2020)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)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)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)Discovering Spatial Interaction Communities from Mobile Phone D ata (2013)Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment (2013)Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Human mobility: Models and applications (2018)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Measuring objective and subjective well-being: dimensions and data sources (2020)The universal visitation law of human mobility (2021)Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)Transport mode detection based on mobile phone network data: A systematic review (2019)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2010Limits of Predictability in Human Mobility3,230 cited
  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2016The promises of big data and small data for travel behavior (aka human mobility) analysis580 cited
  • 2015Returners and explorers dichotomy in human mobility491 cited
  • 2016Understanding congested travel in urban areas406 cited
  • 2017On data processing required to derive mobility patterns from passively-generated mobile phone data248 cited
  • 2011Intra-urban human mobility patterns: An urban morphology perspective239 cited
  • 2019Inferring dynamic origin-destination flows by transport mode using mobile phone data183 cited
  • 2012Traffic Flow Estimation Models Using Cellular Phone Data145 cited
  • 2021Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform123 cited
  • 2012Towards Estimating Urban Population Distributions from Mobile Call Data110 cited
  • 2019Extracting trips from multi-sourced data for mobility pattern analysis: An app-based data example81 cited
  • 2020A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)68 cited

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2020
Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA
62 cited
  • 2021A data-driven travel mode share estimation framework based on mobile device location data44 cited
  • Cited by

    • 2024Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-196 cited