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Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones
16 references·2 citing·43 second hop
Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones (2021)Exploring Individual Activity-Travel Patt…Variability in daily activity-travel patterns: the case of a one-week travel diary (2016)Variability in daily activity…Inferring patterns in the multi-week activity sequences of public transport users (2016)Inferring patterns in the mul…Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Activity-Based Human Mobility…Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach (2015)Understanding aggregate human…From traces to trajectories: How well can we guess activity locations from mobile phone traces? (2014)From traces to trajectories: …Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China (2013)Investigating commuting flexi…Human mobility characterization from cellular network data (2013)Human mobility characterizati…Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Understanding individual mobi…Clustering daily patterns of human activities in the city (2012)Clustering daily patterns of …Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010)Using Mobile Positioning Data…Processing Raw Data from Global Positioning Systems without Additional Information (2009)Processing Raw Data from Glob…Intrapersonal variability in daily urban travel behavior: Some additional evidence (1995)Intrapersonal variability in …Weekly travel-activity behavior (1988)Weekly travel-activity behavi…Repetition and Variability in Urban Travel (1986)Repetition and Variability in…Classification issues in the analysis of complex travel behavior (1986)Classification issues in the …Travel/activity analysis: Pattern recognition, classification and interpretation (1985)Travel/activity analysis: Pat…What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey (2022)What are the multimodal patte…Estimation of Transfer Time from Multimodal Transit Services in the Paris Region (2022)Estimation of Transfer Time f…Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Big Data Integration (2013)Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014)On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Scripts, plans, goals, and understanding : an inquiry into human knowledge structures. By Roger C. Schank and Robert P. Abelson. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977. Pp. 248. (1978)The New Mobilities Paradigm (2006)Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility (2013)Place and Placelessness (1978)Understanding the bias of call detail records in human mobility research (2016)Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data (2018)Evaluating spatial accessibility to healthcare services under travel time uncertainty: A reliability-based floating catchment area approach (2020)A theoretical and empirical model of trip chaining behavior (1979)The Travel-Activity Patterns of Urban Residents: Dimensions and Relationships to Sociodemographic Characteristics (1981)Behavioural theories of dispersion and the mis-specification of travel demand models (1982)Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS) (2008)Motivation and personality (1993)Motivation and Personality (1997)A review of urban computing for mobile phone traces (2013)Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data (2001)Learning transportation mode from raw gps data for geographic applications on the web (2008)Route choice of cyclists in Zurich (2010)A GPS/GIS method for travel mode detection in New York City (2011)Using Global Positioning Systems in Health Research (2011)Examining the impacts of residential self-selection on travel behavior: A focus on methodologies (2007)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Development of origin–destination matrices using mobile phone call data (2014)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)The universal visitation law of human mobility (2021)BIOGEME: a free package for the estimation of discrete choice models (2003)Smart card data use in public transit: A literature review (2011)Validating and improving public transport origin–destination estimation algorithm using smart card fare data (2016)Analyzing year-to-year changes in public transport passenger behaviour using smart card data (2017)Public transport trip purpose inference using smart card fare data (2018)Travel and the Built Environment (2010)Human mobility and socioeconomic status: Analysis of Singapore and Boston (2018)Measuring inequality in community resilience to natural disasters using large-scale mobility data (2021)Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data (2015)A data driven method for OD matrix estimation (2019)The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area (2021)
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References

  • 2012Understanding individual mobility patterns from urban sensing data: A mobile phone trace example645 cited
  • 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore472 cited
  • 2010Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones393 cited
  • 2009Processing Raw Data from Global Positioning Systems without Additional Information326 cited
  • 2013Human mobility characterization from cellular network data311 cited
  • 2012Clustering daily patterns of human activities in the city282 cited
  • 2016Inferring patterns in the multi-week activity sequences of public transport users197 cited
  • 2015Understanding aggregate human mobility patterns using passive mobile phone location data: a home-based approach189 cited
  • 1986Repetition and Variability in Urban Travel167 cited
  • 2014From traces to trajectories: How well can we guess activity locations from mobile phone traces?164 cited
  • 1995Intrapersonal variability in daily urban travel behavior: Some additional evidence153 cited
  • 1988Weekly travel-activity behavior134 cited
  • 2013Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China124 cited
  • 1986Classification issues in the analysis of complex travel behavior

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123 cited
  • 2016Variability in daily activity-travel patterns: the case of a one-week travel diary78 cited
  • 1985Travel/activity analysis: Pattern recognition, classification and interpretation78 cited
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    • 2022What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey10 cited
    • 2022Estimation of Transfer Time from Multimodal Transit Services in the Paris Region4 cited