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Modeling urban activity-travel demand with activity pattern heterogeneity: An integrated LDA and interpretable ML framework
16 references·0 citing·45 second hopsampled
Modeling urban activity-travel demand with activity pattern heterogeneity: An integrated LDA and interpretable ML framework (2025)Modeling urban activity-travel demand wit…A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards (2023)A deep inverse reinforcement …Exploring Individual Travel Patterns Across Private Car Trajectory Data (2019)Exploring Individual Travel P…Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model (2017)Modeling travel mode and timi…Clustering Weekly Patterns of Human Mobility Through Mobile Phone Data (2017)Clustering Weekly Patterns of…A time-use activity-pattern recognition model for activity-based travel demand modeling (2017)A time-use activity-pattern r…The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)The promises of big data and …Inferring patterns in the multi-week activity sequences of public transport users (2016)Inferring patterns in the mul…Discovering urban activity patterns in cell phone data (2015)Discovering urban activity pa…Urban activity pattern classification using topic models from online geo-location data (2014)Urban activity pattern classi…Daily activity pattern recognition by using support vector machines with multiple classes (2013)Daily activity pattern recogn…Activity-based models of travel demand: promises, progress and prospects (2013)Activity-based models of trav…Clustering daily patterns of human activities in the city (2012)Clustering daily patterns of …Eigenbehaviors: identifying structure in routine (2009)Eigenbehaviors: identifying s…Habitual travel behaviour: Evidence from a six-week travel diary (2003)Habitual travel behaviour: Ev…A learning-based transportation oriented simulation system (2003)A learning-based transportati…Micro-simulation of daily activity-travel patterns for travel demand forecasting (2000)Micro-simulation of daily act…WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Urban Travel Demand: A Behavioral Analysis (1977)A theoretical and empirical model of trip chaining behavior (1979)Human mobility: Models and applications (2018)THE AUTOMATIC ACTIVATION OF GOAL-DIRECTED BEHAVIOUR: THE CASE OF TRAVEL HABIT (2000)Observing the rhythms of daily life: A six-week travel diary (2002)Mining smart card data for transit riders’ travel patterns (2013)Friendship and mobility (2011)A deep learning approach for detecting traffic accidents from social media data (2017)Social-media data for urban sustainability (2018)Applications of artificial intelligence for disaster management (2020)Exploring Millions of Footprints in Location Sharing Services (2021)Predicting Depression via Social Media (2021)Understanding individual human mobility patterns (2008)Modelling the scaling properties of human mobility (2010)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)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)Architecture of Cognition (1983)A model of complex travel behavior: Part I—Theoretical development (1986)Theoretical Foundations of Travel Choice Modeling (1998)A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions (2004)Examining the relationship between built environment and metro ridership at station-to-station level (2020)A Theory of the Allocation of Time (1965)A Treatise on the Family (1991)Motivation and Personality (1997)Returners and explorers dichotomy in human mobility (2015)Understanding commuting patterns using transit smart card data (2016)Annals of the Association of American Geographers (1921)Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework (1998)Mobility as a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges (2017)BIOGEME: a free package for the estimation of discrete choice models (2003)Smart card data use in public transit: A literature review (2011)Clustering Smart Card Data for Urban Mobility Analysis (2016)Smart cities, big data and urban policy: Towards urban analytics for the long run (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)Big Data Analytics in Intelligent Transportation Systems: A Survey (2018)A survey of models and algorithms for optimizing shared mobility (2019)The Massachusetts Institute of Technology (1999)Using GPS to learn significant locations and predict movement across multiple users (2003)Reality mining: sensing complex social systems (2005)Limits of Predictability in Human Mobility (2010)Smart cities of the future (2012)
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References

  • 2003A learning-based transportation oriented simulation system612 cited
  • 2016The promises of big data and small data for travel behavior (aka human mobility) analysis609 cited
  • 2009Eigenbehaviors: identifying structure in routine449 cited
  • 2003Habitual travel behaviour: Evidence from a six-week travel diary399 cited
  • 2013Activity-based models of travel demand: promises, progress and prospects350 cited
  • 2012Clustering daily patterns of human activities in the city296 cited
  • 2014Urban activity pattern classification using topic models from online geo-location data291 cited
  • 2015Discovering urban activity patterns in cell phone data245 cited
  • 2000Micro-simulation of daily activity-travel patterns for travel demand forecasting220 cited
  • 2016Inferring patterns in the multi-week activity sequences of public transport users208 cited
  • 2017Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model115 cited
  • 2013Daily activity pattern recognition by using support vector machines with multiple classes95 cited
  • 2019Exploring Individual Travel Patterns Across Private Car Trajectory Data94 cited
  • 2017A time-use activity-pattern recognition model for activity-based travel demand modeling90 cited

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  • 2023A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards89 cited
  • 2017Clustering Weekly Patterns of Human Mobility Through Mobile Phone Data85 cited
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