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A Comparative Analysis between Machine Learning and Econometric Approaches for Travel Mode Choice Modeling
11 references·0 citing·38 second hop
A Comparative Analysis between Machine Learning and Econometric Approaches for Travel Mode Choice Modeling (2023)A Comparative Analysis between Machine Le…Assessing the Performance of Gradient-Boosting Models for Predicting the Travel Mode Choice Using Household Survey Data (2022)Assessing the Performance of …A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling (2022)A comparative study of machin…Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)Prediction and behavioral ana…A systematic review of machine learning classification methodologies for modelling passenger mode choice (2020)A systematic review of machin…Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model (2018)Machine Learning Travel Mode …Applying a random forest method approach to model travel mode choice behavior (2018)Applying a random forest meth…Value of time determination for the city of Alexandria based on a disaggregate binary mode choice model (2017)Value of time determination f…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…Modelling Transport (2011)Modelling TransportThe value of time and reliability: measurement from a value pricing experiment (2001)The value of time and reliabi…Conditional logit analysis of qualitative choice behavior (1972)Conditional logit analysis of…The Estimation of Choice Probabilities from Choice Based Samples (1977)THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS (1982)The valuation of reliability for personal travel (2001)Valuing time and reliability: assessing the evidence from road pricing demonstrations (2005)Route choice modeling: past, present and future research directions (2009)Value of travel time reliability: A review of current evidence (2012)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)Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)Discrete choice analysis: Theory and application to travel demand (1988)Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering (2022)Predicting the travel mode choice with interpretable machine learning techniques: A comparative study (2022)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)Mixed MNL models for discrete response (2000)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. (2009)Enhancing discrete choice models with representation learning (2020)Choice modelling in the age of machine learning - Discussion paper (2021)Comparing urban form influences on travel distance, car ownership, and mode choice (2024)Discrete Choice Methods with Simulation (2003)Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters (2022)Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data (2024)Predicting travel mode choice with a robust neural network and Shapley additive explanations analysis (2024)Evaluating multiple classifiers for stock price direction prediction (2015)Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China (2022)Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice—A Systematic Review (2024)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Macroeconomics and Reality (1980)Discrete Choice Methods with Simulation (2001)Universal predictability of mobility patterns in cities (2014)Uncovering the spatial structure of mobility networks (2015)Transport and equity in Latin America: a critical review of socially oriented accessibility assessments (2020)
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References

  • 1972Conditional logit analysis of qualitative choice behavior14,281 cited
  • 2011Modelling Transport1,220 cited
  • 2001The value of time and reliability: measurement from a value pricing experiment571 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
  • 2020Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models406 cited
  • 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model214 cited
  • 2020A systematic review of machine learning classification methodologies for modelling passenger mode choice154 cited
  • 2022Assessing the Performance of Gradient-Boosting Models for Predicting the Travel Mode Choice Using Household Survey Data42 cited
  • 2022A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling36 cited
  • 2017Value of time determination for the city of Alexandria based on a disaggregate binary mode choice model11 cited

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