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Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach
12 references·16 citing·52 second hop
Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach (2021)Analysis of Travel Mode Choice in Seoul U…Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups (2020)Unraveling the impact of trav…Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model (2018)Machine Learning Travel Mode …Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling (2018)Comparison of Four Types of A…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…Comparing travel mode and trip chain choices between holidays and weekdays (2016)Comparing travel mode and tri…How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal (2014)How land-use and urban form i…Activity-Based Travel Demand Models: A Primer (2014)Activity-Based Travel Demand …Travelers’ preferences in multimodal networks: Design and results of a comprehensive series of choice experiments (2013)Travelers’ preferences in mul…SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution (2010)SACSIM: An applied activity-b…Social inequalities in travel behaviour: trip distances in the context of residential self-selection and lifestyles (2009)Social inequalities in travel…Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips (2008)Curbing automobile use for su…Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling (1998)Accommodating variations in r…Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments (2025)Brain activity patterns refle…Travel mode choice prediction: developing new techniques to prioritize variables and interpret black-box machine learning techniques (2024)Travel mode choice prediction…eXplainable DEA approach for evaluating performance of public transport origin-destination pairs (2024)eXplainable DEA approach for …Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice—A Systematic Review (2024)Discrete Choice Models and Ar…Built environment’s nonlinear effects on mode shares around BRT and rail stations (2024)Built environment’s nonlinear…A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand (2024)A machine learning comparison…A machine learning approach for unraveling the influence of air quality awareness on travel behavior (2024)A machine learning approach f…Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode (2023)Exploring nonlinear and colle…Determinants of passengers' ticketing channel choice in rail transit systems: New evidence of e-payment behaviors from Xi'an, China (2023)Determinants of passengers' t…Artificial Intelligence Techniques for Predicting Individuals’ Mode Choice Behavior in Mansoura City, Egypt (2023)Artificial Intelligence Techn…A deep generative model for feasible and diverse population synthesis (2023)A deep generative model for f…Why Do Students Choose Buses over Private Motorcycles and Motorcycle-Based Ride-Sourcing? A Hybrid Choice Approach (2022)Why Do Students Choose Buses …Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms (2022)Understanding transit ridersh…Examining the relationship between built environment and urban parking demand from the perspective of travelers (2022)Examining the relationship be…Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters (2022)Application of Machine Learni…Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China (2021)Nonlinear Associations of the…A review of British evidence on time and service quality valuations (2001)Meta-analysis of UK values of travel time: An update (2010)Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence (2010)Valuation of a transfer in a multimodal public transport trip (2015)Role of latent factors and public policies in travel decisions under COVID-19 pandemic: Findings of a hybrid choice model (2021)Multimodal urban mobility and multilayer transport networks (2022)Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Bikesharing in Europe, the Americas, and Asia (2010)Bike Share: A Synthesis of the Literature (2013)Bike sharing: A review of evidence on impacts and processes of implementation and operation (2015)Environmental benefits of bike sharing: A big data-based analysis (2018)A review on bike-sharing: The factors affecting bike-sharing demand (2019)Derivation and analysis of some models for combining trip distribution and assignment (1976)Activity-based disaggregate travel demand model system with activity schedules (2001)Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns (2004)Exploring the equity performance of bike-sharing systems with disaggregated data: A story of southern Tampa (2019)How to generate micro-agents? A deep generative modeling approach to population synthesis (2019)A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City (2020)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level (1998)Recreation Demand Models with Taste Differences over People (1998)Extended discrete choice models : integrated framework, flexible error structures, and latent variables (2001)Construction of experimental designs for mixed logit models allowing for correlation across choice observations (2010)Emissions Trading, Electricity Restructuring, and Investment in Pollution Abatement (2010)The consequences of modernity (1990)Travel and the Built Environment: A Synthesis (2001)Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures (2003)Satisfaction with travel and residential self-selection: How do preferences moderate the impact of the Hiawatha Light Rail Transit line? (2014)Travel Distances in Daily Travel and Long-Distance Travel: What Role is Played by Urban Form? (2014)The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination? (2017)Conditional logit analysis of qualitative choice behavior (1972)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)Discrete choice analysis: Theory and application to travel demand (1988)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)Urban travel demand: A behavioral analysis (1976)Discrete Choice Methods with Simulation (2001)Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model (2001)Enhancing discrete choice models with representation learning (2020)Built environments and mode choice: toward a normative framework (2002)The demand for public transport: The effects of fares, quality of service, income and car ownership (2006)Quality attributes of public transport that attract car users: A research review (2012)Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study (2021)Exploring non-linear and spatially non-stationary relationships between commuting burden and built environment correlates (2022)Exploring the factors influencing the use of public transport by commuters living in networks of small cities and towns (2022)Analysis of telecommuting behavior and impacts on travel demand and the environment (2018)Forecasting current and next trip purpose with social media data and Google Places (2018)Impacts of disability on daily travel behaviour: A systematic review (2022)
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References

  • 2014How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal486 cited
  • 2017A comparative study of machine learning classifiers for modeling travel mode choice423 cited
  • 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model214 cited
  • 1998Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling199 cited
  • 2010SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution181 cited
  • 2018Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling174 cited
  • 2014Activity-Based Travel Demand Models: A Primer171 cited
  • 2020Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups161 cited
  • 2009Social inequalities in travel behaviour: trip distances in the context of residential self-selection and lifestyles160 cited
  • 2013Travelers’ preferences in multimodal networks: Design and results of a comprehensive series of choice experiments124 cited
  • 2008Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips100 cited
  • 2016Comparing travel mode and trip chain choices between holidays and weekdays95 cited

Cited by

  • 2023

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Exploring nonlinear and collective influences of regional and local built environment characteristics on travel distances by mode
62 cited
  • 2024eXplainable DEA approach for evaluating performance of public transport origin-destination pairs61 cited
  • 2023A deep generative model for feasible and diverse population synthesis37 cited
  • 2021Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China28 cited
  • 2022Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters28 cited
  • 2022Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms27 cited
  • 2022Examining the relationship between built environment and urban parking demand from the perspective of travelers25 cited
  • 2024Built environment’s nonlinear effects on mode shares around BRT and rail stations25 cited
  • 2023Determinants of passengers' ticketing channel choice in rail transit systems: New evidence of e-payment behaviors from Xi'an, China22 cited
  • 2024A machine learning approach for unraveling the influence of air quality awareness on travel behavior21 cited
  • 2024A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand18 cited
  • 2023Artificial Intelligence Techniques for Predicting Individuals’ Mode Choice Behavior in Mansoura City, Egypt17 cited
  • 2022Why Do Students Choose Buses over Private Motorcycles and Motorcycle-Based Ride-Sourcing? A Hybrid Choice Approach15 cited
  • 2024Travel mode choice prediction: developing new techniques to prioritize variables and interpret black-box machine learning techniques14 cited
  • 2024Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice—A Systematic Review13 cited
  • 2025Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments11 cited