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Trip purpose prediction using travel survey data with POI information via gradient boosting decision trees
16 references·1 citing·44 second hop
Trip purpose prediction using travel survey data with POI information via gradient boosting decision trees (2023)Trip purpose prediction using travel surv…Public transport trip purpose inference using smart card fare data (2018)Public transport trip purpose…Applying a random forest method approach to model travel mode choice behavior (2018)Applying a random forest meth…Revealing intra-urban travel patterns and service ranges from taxi trajectories (2017)Revealing intra-urban travel …Real-time trip purpose prediction using online location-based search and discovery services (2017)Real-time trip purpose predic…Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests (2017)Exploring Bikesharing Travel …Applying mobile phone data to travel behaviour research: A literature review (2017)Applying mobile phone data to…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization (2016)Detecting trip purposes from …Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Origin–destination trips by p…Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Review of GPS Travel Survey a…Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies (2014)Deriving Personal Trip Data f…Supporting large-scale travel surveys with smartphones – A practical approach (2013)Supporting large-scale travel…A GPS/GIS method for travel mode detection in New York City (2011)A GPS/GIS method for travel m…Changes in Travel Behavior in Response to Weather Conditions (2010)Changes in Travel Behavior in…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 …Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data (2001)Elimination of the Travel Dia…Cognitive Agents in Urban Mobility: Integrating LLM Reasoning into Multi-Agent Simulations (2025)Cognitive Agents in Urban Mob…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)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Limits of Predictability in Human Mobility (2010)Modelling the scaling properties of human mobility (2010)Unravelling daily human mobility motifs (2013)The path most traveled: Travel demand estimation using big data resources (2015)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Transportation mode detection using mobile phones and GIS information (2011)Where do cyclists ride? A route choice model developed with revealed preference GPS data (2012)Big Data and Cycling (2015)Transport mode detection based on mobile phone network data: A systematic review (2019)Household travel surveys: Where are we going? (2006)Processing Raw Data from Global Positioning Systems without Additional Information (2009)Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing (2015)Algorithmic Geographies: Big Data, Algorithmic Uncertainty, and the Production of Geographic Knowledge (2016)Assessing the potential for carbon emissions savings from replacing short car trips with walking and cycling using a mixed GPS-travel diary approach (2018)The effect of weather and climate on bicycle commuting (1999)Assessing the Impact of Weather on Traffic Intensity (2009)Influence of weather conditions on transit ridership: A statistical study using data from Smartcards (2013)Impact of weather on urban transit ridership (2014)Impacts of weather on public transport ridership: Results from mining data from different sources (2016)Inferring High-Level Behavior from Low-Level Sensors (2003)Learning transportation mode from raw gps data for geographic applications on the web (2008)Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines (2015)Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier (2017)Modeling intra-destination travel behavior of tourists through spatio-temporal analysis (2018)Conditional logit analysis of qualitative choice behavior (1972)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Travel and the Built Environment (2010)Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis (2018)Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)Understanding individual human mobility patterns (2008)Big data: The next frontier for innovation, competition, and productivity (2011)An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain (2020)Exploring the spatiotemporal patterns and correlates of urban vitality: Temporal and spatial heterogeneity (2023)On the measurement of inequality (1970)Built environments and mode choice: toward a normative framework (2002)The effects of attitudes and personality traits on mode choice (2005)Examining non-linear built environment effects on elderly’s walking: A random forest approach (2020)To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults (2021)Tracking tourists in the digital age (2007)Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010)
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References

  • 2015Origin–destination trips by purpose and time of day inferred from mobile phone data637 cited
  • 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands501 cited
  • 2018Applying a random forest method approach to model travel mode choice behavior491 cited
  • 2017A comparative study of machine learning classifiers for modeling travel mode choice423 cited
  • 2001Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data394 cited
  • 2017Applying mobile phone data to travel behaviour research: A literature review368 cited
  • 2014Review of GPS Travel Survey and GPS Data-Processing Methods350 cited
  • 2011A GPS/GIS method for travel mode detection in New York City293 cited
  • 2010Changes in Travel Behavior in Response to Weather Conditions213 cited
  • 2014Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies162 cited
  • 2018Public transport trip purpose inference using smart card fare data160 cited
  • 2013Supporting large-scale travel surveys with smartphones – A practical approach154 cited
  • 2017Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests142 cited
  • 2016Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization

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114 cited
  • 2017Revealing intra-urban travel patterns and service ranges from taxi trajectories97 cited
  • 2017Real-time trip purpose prediction using online location-based search and discovery services97 cited
  • Cited by

    • 2025Cognitive Agents in Urban Mobility: Integrating LLM Reasoning into Multi-Agent Simulations5 cited