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Performance evaluation of mode choice models under balanced and imbalanced data assumptions
16 references·1 citing·35 second hop
Performance evaluation of mode choice models under balanced and imbalanced data assumptions (2021)Performance evaluation of mode choice mod…Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)Prediction and behavioral ana…Applying an ensemble-based model to travel choice behavior in travel demand forecasting under uncertainties (2019)Applying an ensemble-based mo…Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model (2018)Machine Learning Travel Mode …The path most traveled: Travel demand estimation using big data resources (2015)The path most traveled: Trave…Use of Artificial Intelligence for Mode Choice Analysis and Comparison with Traditional Multinomial Logit Model (2013)Use of Artificial Intelligenc…Modelling Transport (2011)Modelling TransportValidation and Forecasts in Models Estimated from Multiday Travel Survey (2010)Validation and Forecasts in M…Travel demand models in the developing world: correcting for measurement errors (2010)Travel demand models in the d…Active travel behavior (2009)Active travel behaviorThe Mixed Logit model: The state of practice (2003)The Mixed Logit model: The st…Discrete Choice Methods with Simulation (2003)Discrete Choice Methods with …Forecasting new product penetration with flexible substitution patterns (1998)Forecasting new product penet…Discrete choice analysis: Theory and application to travel demand (1988)Discrete choice analysis: The…STRUCTURE OF PASSENGER TRAVEL DEMAND MODELS (1974)STRUCTURE OF PASSENGER TRAVEL…Conditional logit analysis of qualitative choice behavior (1972)Conditional logit analysis of…THE DARWIN CELEBRATION AT CAMBRIDGE (1909)THE DARWIN CELEBRATION AT CAM…Identifying the key factors of intermodal travel using interpretative ensemble learning (2024)Identifying the key factors o…Mixed MNL models for discrete response (2000)Discrete Choice Methods with Simulation (2001)Applications of Simulation Methods in Environmental and Resource Economics (2005)Where do cyclists ride? A route choice model developed with revealed preference GPS data (2012)Contemporary Guidance for Stated Preference Studies (2017)The scaling laws of human travel (2006)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)The TimeGeo modeling framework for urban mobility without travel surveys (2016)Understanding congested travel in urban areas (2016)MODELING THE CHOICE OF RESIDENTIAL LOCATION (1978)Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice (1990)Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles (2000)Generalized random utility model (2002)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Travel and the Built Environment (2010)A systematic review of machine learning classification methodologies for modelling passenger mode choice (2020)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)A latent class model for discrete choice analysis: contrasts with mixed logit (2003)Conducting Discrete Choice Experiments to Inform Healthcare Decision Making (2008)Quantal Choice Analaysis: A Survey (1976)Chapter 24 Econometric analysis of qualitative response models (1984)ECONOMETRIC ANALYSIS OF QUALITATIVE RESPONSE MODELS (1984)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Macroeconomics and Reality (1980)Experience-weighted Attraction Learning in Normal Form Games (1999)UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning (2002)Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force (2016)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)Examining non-linear built environment effects on elderly’s walking: A random forest approach (2020)Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)
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References

  • 1972Conditional logit analysis of qualitative choice behavior14,281 cited
  • 2003Discrete Choice Methods with Simulation3,891 cited
  • 1988Discrete choice analysis: Theory and application to travel demand3,257 cited
  • 2003The Mixed Logit model: The state of practice2,044 cited
  • 2011Modelling Transport1,220 cited
  • 1998Forecasting new product penetration with flexible substitution patterns852 cited
  • 2015The path most traveled: Travel demand estimation using big data resources418 cited
  • 2020Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models406 cited
  • 1974STRUCTURE OF PASSENGER TRAVEL DEMAND MODELS389 cited
  • 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model214 cited
  • 2009Active travel behavior53 cited
  • 2010Travel demand models in the developing world: correcting for measurement errors50 cited
  • 2013Use of Artificial Intelligence for Mode Choice Analysis and Comparison with Traditional Multinomial Logit Model47 cited
  • 2010Validation and Forecasts in Models Estimated from Multiday Travel Survey33 cited
  • 2019Applying an ensemble-based model to travel choice behavior in travel demand forecasting under uncertainties

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17 cited
  • 1909THE DARWIN CELEBRATION AT CAMBRIDGE12 cited
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    • 2024Identifying the key factors of intermodal travel using interpretative ensemble learning2 cited