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Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues
1 references·16 citing·50 second hop
Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues (2006)Increasing the accuracy of trip rate info…Can using global positioning system (GPS) improve trip reporting? (1999)Can using global positioning …A data-driven travel mode share estimation framework based on mobile device location data (2021)A data-driven travel mode sha…Reviewing trip purpose imputation in GPS-based travel surveys (2020)Reviewing trip purpose imputa…Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier (2019)Detecting Travel Modes Using …Identifying activity-travel points from GPS-data with multiple moving windows (2018)Identifying activity-travel p…Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier (2017)Travel Mode Detection Using G…Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization (2016)Detecting trip purposes from …Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data (2016)Comparison of advanced imputa…Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area (2015)Improved methods to deduct tr…Annotating mobile phone location data with activity purposes using machine learning algorithms (2013)Annotating mobile phone locat…Automated time activity classification based on global positioning system (GPS) tracking data (2011)Automated time activity class…Identifying Walking Trips Using GPS Data (2010)Identifying Walking Trips Usi…Using Bus Probe Data for Analysis of Travel Time Variability (2009)Using Bus Probe Data for Anal…An automated GPS-based prompted recall survey with learning algorithms (2009)An automated GPS-based prompt…Using GPS Data to Understand Driving Behavior (2008)Using GPS Data to Understand …Identifying trips and activities and their characteristics from GPS raw data without further information (2008)Identifying trips and activit…Route choice modeling with network-free data (2007)Route choice modeling with ne…Measuring Space‐Time Accessibility Benefits within Transportation Networks: Basic Theory and Computational Procedures (1999)Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set (2000)Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data (2001)Finding food: Issues and challenges in using Geographic Information Systems (GIS) to measure food access (2010)Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment (2018)Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS (2019)Observing the rhythms of daily life: A six-week travel diary (2002)Using GPS to learn significant locations and predict movement across multiple users (2003)Learning and inferring transportation routines (2007)Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review (2013)Hypothetical bias in Stated Choice Experiments: Is it a problem? 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(2014)Comparison of Travel Diaries Generated from Smartphone Data and Dedicated GPS Devices (2015)Transportation research (1975)Tracking survey for individual travel behaviour using mobile communication instruments (2004)Travel through time: the story of research on travel time reliability (2013)A copula-based approach for estimating the travel time reliability of urban arterial (2017)Analysis of bus travel time distributions for varying horizons and real-time applications (2017)Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems (1992)Measuring transit use variability with smart-card data (2007)Conceptualizing the healthscape: Contributions of time geography, location technologies and spatial ecology to place and health research (2009)Behavioural data mining of transit smart card data: A data fusion approach (2014)Understanding individual human mobility patterns (2008)Washington DC - USA (2008)Modelling the scaling properties of human mobility (2010)Using ensembles of decision trees to predict transport mode choice decisions: Effects on predictive success and uncertainty estimates (2014)Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem (2016)Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data (2019)Discrete Choice Methods and their Applications to Short Term Travel Decisions (1999)Handbook of Transportation Science (1999)BIOGEME: a free package for the estimation of discrete choice models (2003)Probabilistic Multimodal Map Matching With Rich Smartphone Data (2013)A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures (2014)Developing advanced route choice models for heavy goods vehicles using GPS data (2015)Learning transportation mode from raw gps data for geographic applications on the web (2008)Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands (2009)Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment (2011)Implications of the modifiable areal unit problem for assessing built environment correlates of moderate and vigorous physical activity (2014)Making pervasive sensing possible: Effective travel mode sensing based on smartphones (2016)An automated approach from GPS traces to complete trip information (2018)Processing Raw Data from Global Positioning Systems without Additional Information (2009)Transportation mode recognition using GPS and accelerometer data (2013)Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics (2017)Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data (2020)Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Real-time trip purpose prediction using online location-based search and discovery services (2017)Deep neural networks for choice analysis: Extracting complete economic information for interpretation (2020)Inferring High-Level Behavior from Low-Level Sensors (2003)Inferring fine-grained transport modes from mobile phone cellular signaling data (2019)Travel Mode Identification With GPS Trajectories Using Wavelet Transform and Deep Learning (2020)A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity (2022)
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References

  • 1999Can using global positioning system (GPS) improve trip reporting?282 cited

Cited by

  • 2009Using Bus Probe Data for Analysis of Travel Time Variability131 cited
  • 2007Route choice modeling with network-free data131 cited
  • 2009An automated GPS-based prompted recall survey with learning algorithms119 cited
  • 2016Detecting trip purposes from smartphone-based travel surveys with artificial neural networks and particle swarm optimization114 cited
  • 2017Travel Mode Detection Using GPS Data and Socioeconomic Attributes Based on a Random Forest Classifier114 cited
  • 2013Annotating mobile phone location data with activity purposes using machine learning algorithms85 cited
  • 2011Automated time activity classification based on global positioning system (GPS) tracking data84 cited
  • 2016Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data83 cited
  • 2008Using GPS Data to Understand Driving Behavior79 cited
  • 2015Improved methods to deduct trip legs and mode from travel surveys using wearable GPS devices: A case study from the Greater Copenhagen area75 cited
  • 2010Identifying Walking Trips Using GPS Data68 cited
  • 2019Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier65 cited
  • 2020

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Reviewing trip purpose imputation in GPS-based travel surveys
53 cited
  • 2008Identifying trips and activities and their characteristics from GPS raw data without further information47 cited
  • 2021A data-driven travel mode share estimation framework based on mobile device location data44 cited
  • 2018Identifying activity-travel points from GPS-data with multiple moving windows37 cited