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Blue collar laborers’ travel pattern recognition: Machine learning classifier approach
16 references·0 citing·54 second hop
Blue collar laborers’ travel pattern recognition: Machine learning classifier approach (2021)Blue collar laborers’ travel pattern reco…Inferring trip purpose by clustering sequences of smart card records (2021)Inferring trip purpose by clu…Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data (2021)Identifying Tourists and Loca…Ensemble learning activity scheduler for activity based travel demand models (2021)Ensemble learning activity sc…Machine learning applications in activity-travel behaviour research: a review (2020)Machine learning applications…Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation (2020)Formulation and solution appr…Activity-based travel demand generation using Bayesian networks (2020)Activity-based travel demand …Activity-Based Demand Modeling for a Future Urban District (2020)Activity-Based Demand Modelin…A comparison of time-use behavior in metropolitan areas using pattern recognition techniques (2019)A comparison of time-use beha…Travel Behavior Classification: An Approach with Social Network and Deep Learning (2018)Travel Behavior Classificatio…Data-driven activity scheduler for agent-based mobility models (2018)Data-driven activity schedule…A time-use activity-pattern recognition model for activity-based travel demand modeling (2017)A time-use activity-pattern r…A Travel Mode Choice Model Using Individual Grouping Based on Cluster Analysis (2016)A Travel Mode Choice Model Us…Use of acceleration data for transportation mode prediction (2014)Use of acceleration data for …Activity-Based Travel Demand Models: A Primer (2014)Activity-Based Travel Demand …Origin-based algorithms for combined travel forecasting models (2003)Origin-based algorithms for c…Activity pattern similarity: a multidimensional sequence alignment method (2002)Activity pattern similarity: …A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands (2009)Transportation mode detection using mobile phones and GIS information (2011)A comparative study of machine learning classifiers for modeling travel mode choice (2017)Applying a random forest method approach to model travel mode choice behavior (2018)Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data (2018)WHAT ABOUT PEOPLE IN REGIONAL SCIENCE? (1970)Activity‐based approaches to travel analysis: conceptual frameworks, models, and research problems (1992)Travel Motivations of Japanese Overseas Travelers: A Factor-Cluster Segmentation Approach (1995)Sequence Alignment as a Method for Human Activity Analysis in Space and Time (2007)Urban activity pattern classification using topic models from online geo-location data (2014)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Studies in the Economics of Transportation. (1956)Studies in the Economics of Transportation. (1957)The Traffic Assignment Problem: Models and Methods (1994)A REGIONAL ECONOMY, LAND USE, AND TRANSPORTATION MODEL (RELU‐TRAN © ): FORMULATION, ALGORITHM DESIGN, AND TESTING* (2007)Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2011)A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system (2015)Discrete Choice Analysis: Theory and Application to Travel Demand (1987)Discrete Choice Methods with Simulation (2001)Discrete Choice Methods with Simulation (2003)A review of Machine Learning (ML) algorithms used for modeling travel mode choice (2019)Modeling the Modal Shift towards a More Sustainable Transport by Stated Preference in Riyadh, Saudi Arabia (2021)Evaluating commuters' travel mode choice using the Z-number extension of Parsimonious Best Worst Method (2025)Activity-based disaggregate travel demand model system with activity schedules (2001)Introduction to Data Mining (2007)Trip chaining and tour mode choice of non-workers grouped by daily activity patterns (2018)Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models (2023)Prototype Model of Household Activity-Travel Scheduling (2003)Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns (2004)The Four-Step Model (2007)A system dynamic approach for the smart mobility of people: Implications in the age of big data (2019)How to generate micro-agents? A deep generative modeling approach to population synthesis (2019)Learning to Simulate Human Mobility (2020)A learning-based transportation oriented simulation system (2003)Mining smart card data for transit riders’ travel patterns (2013)Predicting the travel mode choice with interpretable machine learning techniques: A comparative study (2022)Artificial intelligence for improving public transport: a mapping study (2023)Non-Linear Associations Between the Urban Built Environment and Commuting Modal Split: A Random Forest Approach and SHAP Evaluation (2023)Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India (2001)Activity-based models of travel demand: promises, progress and prospects (2013)A Bayesian network approach for population synthesis (2015)Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data (2021)Generative population synthesis for joint household and individual characteristics (2022)OMOD: An open-source tool for creating disaggregated mobility demand based on OpenStreetMap (2023)Smart card data use in public transit: A literature review (2011)Spatiotemporal Patterns of Urban Human Mobility (2012)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile (2023)Machine Learning Applied to Public Transportation by Bus: A Systematic Literature Review (2023)Trip purpose prediction using travel survey data with POI information via gradient boosting decision trees (2023)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)Transport modelling in the age of big data (2017)Analysis of telecommuting behavior and impacts on travel demand and the environment (2018)
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References

  • 2014Activity-Based Travel Demand Models: A Primer194 cited
  • 2002Activity pattern similarity: a multidimensional sequence alignment method175 cited
  • 2020Machine learning applications in activity-travel behaviour research: a review140 cited
  • 2003Origin-based algorithms for combined travel forecasting models109 cited
  • 2014Use of acceleration data for transportation mode prediction101 cited
  • 2017A time-use activity-pattern recognition model for activity-based travel demand modeling90 cited
  • 2018Data-driven activity scheduler for agent-based mobility models71 cited
  • 2016A Travel Mode Choice Model Using Individual Grouping Based on Cluster Analysis68 cited
  • 2020Activity-based travel demand generation using Bayesian networks49 cited
  • 2021Inferring trip purpose by clustering sequences of smart card records42 cited
  • 2021Ensemble learning activity scheduler for activity based travel demand models39 cited
  • 2021Identifying Tourists and Locals by K-Means Clustering Method from Mobile Phone Signaling Data36 cited
  • 2018Travel Behavior Classification: An Approach with Social Network and Deep Learning33 cited
  • 2020Activity-Based Demand Modeling for a Future Urban District22 cited
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Formulation and solution approach for calibrating activity-based travel demand model-system via microsimulation
16 cited
  • 2019A comparison of time-use behavior in metropolitan areas using pattern recognition techniques15 cited
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