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Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis
16 references·0 citing·49 second hop
Using Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis (2018)Using Passive Data Collection Methods to …Enriching Activity-Based Models using Smartphone-Based Travel Surveys (2018)Enriching Activity-Based Mode…Smartphone based travel diary collection: experiences from a field trial in Stockholm (2017)Smartphone based travel diary…On the consistency between commuting satisfaction and traveling utility: the case of the University of Luxembourg (2017)On the consistency between co…Mapping Social Media for Transportation Studies (2016)Mapping Social Media for Tran…A Seven-Day Smartphone-Based GPS Household Travel Survey in Indiana (2016)A Seven-Day Smartphone-Based …Field Evaluation of the Smartphone-based Travel Behaviour Data Collection App “SmartMo” (2015)Field Evaluation of the Smart…Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore (2015)Exploratory Analysis of a Sma…Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore (2015)Exploratory Analysis of a Sma…Using Smartphones and Sensor Technologies to Automate Collection of Travel Data (2013)Using Smartphones and Sensor …Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data (2012)Extracting Dynamic Urban Mobi…Predicting human behaviour from selected mobile phone data points (2010)Predicting human behaviour fr…Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study (2010)Evaluating the feasibility of…Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model (2009)Framework for the development…Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands (2009)Deriving and validating trip …Real-time tracking of activity scheduling/schedule execution within a unified data collection framework (2006)Real-time tracking of activit…Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data (2006)Comparative Analysis of Globa…Can Land-use Policy Really Affect Travel Behaviour? A Study of the Link between Non-work Travel and Land-use Characteristics (1998)GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference? (2013)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Discrete Choice Analysis (1985)Examining Trip-Chaining Behavior: Comparison of Travel by Men and Women (1999)Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey (2003)An automated GPS-based prompted recall survey with learning algorithms (2009)Dynamic accessibility mapping using floating car data: a network-constrained density estimation approach (2010)Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review (2013)ALBATROSS : a learning based transportation oriented simulation system (2000)Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns (2004)Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model (2012)Uncertainty in travel demand forecasting models: literature review and research agenda (2012)Incorporating space–time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling (2013)Transportation Research Board (2014)Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data (2001)A Trip Reconstruction Tool for GPS-based Personal Travel Surveys (2005)Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems (2006)A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection (2017)The role of data in sustainability assessment of urban mobility policies (2022)Understanding mobility based on GPS data (2008)Processing Raw Data from Global Positioning Systems without Additional Information (2009)Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies (2014)From traces to trajectories: How well can we guess activity locations from mobile phone traces? (2014)TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data (2018)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study (2016)Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data (2017)Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities (2018)Understanding transportation modes based on GPS data for web applications (2010)MEILI: A travel diary collection, annotation and automation system (2018)Transportation Mode Detection from Low-Power Smartphone Sensors Using Tree-Based Ensembles (2019)Future Mobility Survey (2013)DataMobile: Smartphone Travel Survey Experiment (2016)Perspectives of the use of smartphones in travel behaviour studies: Findings from a literature review and a pilot study (2018)Urban spatiotemporal analysis using mobile phone data: Case study of medium- and large-sized Korean cities (2018)Users of the world, unite! The challenges and opportunities of Social Media (2009)Friendship and mobility (2011)Social media? Get serious! Understanding the functional building blocks of social media (2011)Utilizing Social Media in Transport Planning and Public Transit Quality: Survey of Literature (2018)Data to the people: a review of public and proprietary data for transport models (2021)Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich (2021)Transportation mode recognition using GPS and accelerometer data (2013)Changing mobility patterns in the Netherlands during COVID-19 outbreak (2021)A comprehensive review of trip generation models based on land use characteristics (2022)Observing Germany’s nationwide public transport fare policy experiment “9-Euro-Ticket” – Empirical findings from a panel study (2024)
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References

  • 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands501 cited
  • 2010Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study193 cited
  • 2006Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data144 cited
  • 2009Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model141 cited
  • 2012Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data112 cited
  • 2015Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore92 cited
  • 2013Using Smartphones and Sensor Technologies to Automate Collection of Travel Data48 cited
  • 2015Field Evaluation of the Smartphone-based Travel Behaviour Data Collection App “SmartMo”44 cited
  • 2016Mapping Social Media for Transportation Studies43 cited
  • 2017Smartphone based travel diary collection: experiences from a field trial in Stockholm43 cited
  • 2006Real-time tracking of activity scheduling/schedule execution within a unified data collection framework39 cited
  • 2018Enriching Activity-Based Models using Smartphone-Based Travel Surveys32 cited
  • 2010Predicting human behaviour from selected mobile phone data points29 cited
  • 2015

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Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore
16 cited
  • 2016A Seven-Day Smartphone-Based GPS Household Travel Survey in Indiana15 cited
  • 2017On the consistency between commuting satisfaction and traveling utility: the case of the University of Luxembourg14 cited
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