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Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt
16 references·5 citing·37 second hop
Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt (2024)Sensitivity evaluation of machine learnin…Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis (2023)Analysis of mode choice affec…AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022)AI-Based Modeling: Techniques…Choice behavior of commuters' rail transit mode during the COVID-19 pandemic based on logistic model (2020)Choice behavior of commuters'…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…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…Mode Choice Analysis Using Random Forrest Decision Trees (2016)Mode Choice Analysis Using Ra…Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China (2015)Using hierarchical tree-based…Pricing and mode choice based on nested logit model with trip-chain costs (2015)Pricing and mode choice based…Predicting Travel Mode of Individuals by Machine Learning (2015)Predicting Travel Mode of Ind…Modelling Transport (2011)Modelling TransportTravel Mode Choice Modeling with Support Vector Machines (2008)Travel Mode Choice Modeling w…A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models (2006)A Self Instructing Course in …Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks (2003)Work Travel Mode Choice Model…Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance (2002)Nested Logit Models and Artif…Neural network analysis of travel behavior: Evaluating tools for prediction (1996)Neural network analysis of tr…Systematic Review of Transportation Choice Modeling (2025)Systematic Review of Transpor…Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning (2025)Analyzing High-Speed Rail’s T…Public transport mode choice behavior of different-income passengers during large-scale public health incidents (2024)Public transport mode choice …Ensemble methods for route choice (2024)Ensemble methods for route ch…A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand (2024)A machine learning comparison…On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit (1977)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Mixed MNL models for discrete response (2000)An analysis of bicycle route choice preferences in Texas, US (2009)Valuing quality changes in Caribbean coastal waters for heterogeneous beach visitors (2010)Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul (2015)A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice (2000)Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction (2019)Deep neural networks for choice analysis: Extracting complete economic information for interpretation (2020)Enhancing discrete choice models with representation learning (2020)Discrete choice analysis: Theory and application to travel demand (1988)Daily activity pattern recognition by using support vector machines with multiple classes (2013)Choice modelling in the age of machine learning - Discussion paper (2021)Exploring interactive and nonlinear effects of key factors on intercity travel mode choice using XGBoost (2024)Conditional logit analysis of qualitative choice behavior (1972)Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling (2018)A systematic review of machine learning classification methodologies for modelling passenger mode choice (2020)Travel and the Built Environment (2010)Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis (2018)Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach (2021)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)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)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)Impacts of COVID-19 on urban rail transit ridership using the Synthetic Control Method (2021)Pandemic waves and the time after Covid-19 – Consequences for the transport sector (2021)The impact of COVID-19 on future public transport use in Scotland (2022)AI-based decision support systems in Industry 4.0, a review (2024)AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency (2024)Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability (2024)
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References

  • 2011Modelling Transport1,220 cited
  • 2022AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems1,205 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
  • 2006A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models369 cited
  • 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model214 cited
  • 2003Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks211 cited
  • 2015Predicting Travel Mode of Individuals by Machine Learning149 cited
  • 2008Travel Mode Choice Modeling with Support Vector Machines127 cited
  • 2020Choice behavior of commuters' rail transit mode during the COVID-19 pandemic based on logistic model119 cited
  • 2015Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China105 cited
  • 2016Mode Choice Analysis Using Random Forrest Decision Trees104 cited
  • 2015Pricing and mode choice based on nested logit model with trip-chain costs75 cited
  • 2002Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance

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73 cited
  • 1996Neural network analysis of travel behavior: Evaluating tools for prediction65 cited
  • 2023Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis45 cited
  • Cited by

    • 2024A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand18 cited
    • 2025Systematic Review of Transportation Choice Modeling12 cited
    • 2024Public transport mode choice behavior of different-income passengers during large-scale public health incidents12 cited
    • 2024Ensemble methods for route choice8 cited
    • 2025Analyzing High-Speed Rail’s Transformative Impact on Public Transport in Thailand Using Machine Learning7 cited