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A comprehensive review of trip generation models based on land use characteristics
16 references·4 citing·36 second hop
A comprehensive review of trip generation models based on land use characteristics (2022)A comprehensive review of trip generation…Trip and parking generation at transit-oriented developments: Five US case studies (2017)Trip and parking generation a…Structural equation models to analyze activity participation, trip generation, and mode choice of low-income commuters (2017)Structural equation models to…On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)On data processing required t…Analyzing Cell Phone Location Data for Urban Travel (2015)Analyzing Cell Phone Location…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…Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai (2012)Urban land uses and traffic ‘…Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)Understanding individual mobi…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…Processing Raw Data from Global Positioning Systems without Additional Information (2009)Processing Raw Data from Glob…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 …Search for a global positioning system device to measure person travel (2007)Search for a global positioni…Household travel surveys: Where are we going? (2006)Household travel surveys: Whe…Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data (2006)Comparative Analysis of Globa…Comparison of Trip Determination Methods in Household Travel Surveys Enhanced by a Global Positioning System (2005)Comparison of Trip Determinat…Travel behavior of low-income residents: studying two contrasting locations in the city of Chennai, India (2004)Travel behavior of low-income…Model for Forecasting Rural Travel Demand Using Feed Forward—Backpropagation Neural Network and Minimized Akaike Information Criterion Algorithm (2024)Model for Forecasting Rural T…Integrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits (2024)Integrated Transit-Oriented D…How to incorporate machine learning and microsimulation tools in travel demand forecasting in multi-modal networks (2024)How to incorporate machine le…Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation (2023)Application of adaptive neuro…Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Limits of Predictability in Human Mobility (2010)Modelling the scaling properties of human mobility (2010)Understanding individual routing behaviour (2016)Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis (2018)Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data (2001)Using Geographic Information Systems (GIS) to assess the role of the built environment in influencing obesity: a glossary (2011)Transportation mode-based segmentation and classification of movement trajectories (2012)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)Cities and Housing. (1970)Understanding individual human mobility patterns (2008)A new insight into land use classification based on aggregated mobile phone data (2014)Revealing travel patterns and city structure with taxi trip data (2015)Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)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)Transportation mode detection – an in-depth review of applicability and reliability (2016)Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier (2017)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 universal visitation law of human mobility (2021)Understanding mobility based on GPS data (2008)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)Children's Independent Movement in the Local Environment (2007)A GPS/GIS method for travel mode detection in New York City (2011)Route choice of cyclists in Zurich (2010)Using Global Positioning Systems in Health Research (2011)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)
this work references cited by second hop

References

  • 2012Understanding individual mobility patterns from urban sensing data: A mobile phone trace example645 cited
  • 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands501 cited
  • 2012Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai423 cited
  • 2009Processing Raw Data from Global Positioning Systems without Additional Information326 cited
  • 2006Household travel surveys: Where are we going?317 cited
  • 2017On data processing required to derive mobility patterns from passively-generated mobile phone data248 cited
  • 2007Search for a global positioning system device to measure person travel246 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
  • 2015Analyzing Cell Phone Location Data for Urban Travel180 cited
  • 2014Deriving Personal Trip Data from GPS Data: A Literature Review on the Existing Methodologies162 cited
  • 2013Supporting large-scale travel surveys with smartphones – A practical approach154 cited
  • 2004Travel behavior of low-income residents: studying two contrasting locations in the city of Chennai, India153 cited
  • 2006Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data144 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2017Structural equation models to analyze activity participation, trip generation, and mode choice of low-income commuters96 cited
  • 2017Trip and parking generation at transit-oriented developments: Five US case studies94 cited
  • 2005Comparison of Trip Determination Methods in Household Travel Surveys Enhanced by a Global Positioning System89 cited
  • Cited by

    • 2024How to incorporate machine learning and microsimulation tools in travel demand forecasting in multi-modal networks26 cited
    • 2024Integrated Transit-Oriented Development (TOD) with suburban rail network design problem for maximizing profits12 cited
    • 2023Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation7 cited
    • 2024Model for Forecasting Rural Travel Demand Using Feed Forward—Backpropagation Neural Network and Minimized Akaike Information Criterion Algorithm6 cited