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Exploring Transit Use during COVID-19 Based on XGB and SHAP Using Smart Card Data
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Exploring Transit Use during COVID-19 Based on XGB and SHAP Using Smart Card Data (2022)Exploring Transit Use during COVID-19 Bas…Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data (2022)Exploring for Route Preferenc…Transport mode use during the COVID-19 lockdown period in Germany: The car became more important, public transport lost ground (2021)Transport mode use during the…Exploring the Behaviour Change of Crowding Impedance on Public Transit due to COVID-19 Pandemic: Before and After Comparison (2021)Exploring the Behaviour Chang…Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data (2021)Estimating Express Train Pref…Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership (2020)Who left riding transit? Exam…Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing the effects of risk perception and social influence on going-out self-restriction (2020)Travel behavior changes durin…The effect of COVID-19 and subsequent social distancing on travel behavior (2020)The effect of COVID-19 and su…How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago (2020)How is COVID-19 reshaping act…How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands (2020)How COVID-19 and the Dutch ‘i…Fare evasion in public transport systems: a review of the literature (2020)Fare evasion in public transp…Exploring the impacts of COVID-19 on travel behavior and mode preferences (2020)Exploring the impacts of COVI…Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model (2018)Machine Learning Travel Mode …Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level (2014)Fare evasion in proof-of-paym…Estimation of a route choice model for urban public transport using smart card data (2014)Estimation of a route choice …17. A Value for n-Person Games (1953)17. A Value for n-Person GamesUnderstanding Gender Gap in Bike-Sharing Services via eXplainable Artificial Intelligence (2025)Understanding Gender Gap in B…Resilience of public transport in the face of disruptions: Insights from explainable machine learning (2025)Resilience of public transpor…Using XGBoost and SHAP to explain citizens’ differences in policy support for reimposing COVID-19 measures in the Netherlands (2024)Using XGBoost and SHAP to exp…eXplainable DEA approach for evaluating performance of public transport origin-destination pairs (2024)eXplainable DEA approach for …Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete choice analysis: Theory and application to travel demand (1988)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)How derived is the demand for travel? 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(2012)Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions (2020)Social psychophysiology : a sourcebook (1983)Perception of Risk (1987)Impact of the COVID-19 pandemic on travel behavior in Istanbul: A panel data analysis (2020)Using social and behavioural science to support COVID-19 pandemic response (2020)Effect of Covid-19 pandemic on tourist travel risk and management perceptions (2021)Virtual Tours a Means to an End: An Analysis of Virtual Tours’ Role in Tourism Recovery Post COVID-19 (2021)Modal share changes due to COVID-19: The case of Budapest (2020)COVID-19, internet, and mobility: The rise of telework, telehealth, e-learning, and e-shopping (2021)Understanding and Using Factor Scores: Considerations for the Applied Researcher (2020)COVID-19 and transport: Findings from a world-wide expert survey (2021)Mobility Behaviour in View of the Impact of the COVID-19 Pandemic—Public Transport Users in Gdansk Case Study (2021)Travel demand and the 3Ds: Density, diversity, and design (1997)On the use of cross-validation for time series predictor evaluation (2012)Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic (2021)Why has public transit ridership declined in the United States? (2022)Discrete Choice Methods with Simulation (2001)The Economics of Urban Transportation (2007)COVID-19 and Public Transportation: Current Assessment, Prospects, and Research Needs (2020)Effects of COVID-19 on rail passengers’ crowding perceptions (2021)Public transit travel choice in the post COVID-19 pandemic era: An application of the extended Theory of Planned behavior (2022)Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands (2022)Game theoretic analysis of a bankruptcy problem from the Talmud (1985)Monotonic solutions of cooperative games (1985)Decomposition procedures for distributional analysis: a unified framework based on the Shapley value (2012)A paradigm shift in urban mobility: Policy insights from travel before and after COVID-19 to seize the opportunity (2021)COVID-19 effects on property markets: The pandemic decreases the implicit price of metro accessibility (2022)The Long-Term effects of COVID-19 on travel behavior in the United States: A panel study on work from home, mode choice, online shopping, and air travel (2022)
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References

  • 195317. A Value for n-Person Games6,944 cited
  • 2020The effect of COVID-19 and subsequent social distancing on travel behavior961 cited
  • 2020How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands734 cited
  • 2020Exploring the impacts of COVID-19 on travel behavior and mode preferences679 cited
  • 2020How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago548 cited
  • 2020Travel behavior changes during the COVID-19 pandemic in Japan: Analyzing the effects of risk perception and social influence on going-out self-restriction308 cited
  • 2021Transport mode use during the COVID-19 lockdown period in Germany: The car became more important, public transport lost ground300 cited
  • 2020Who left riding transit? Examining socioeconomic disparities in the impact of COVID-19 on ridership271 cited
  • 2018Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model214 cited
  • 2021Exploring the Behaviour Change of Crowding Impedance on Public Transit due to COVID-19 Pandemic: Before and After Comparison91 cited
  • 2020Fare evasion in public transport systems: a review of the literature84 cited
  • 2014Estimation of a route choice model for urban public transport using smart card data75 cited
  • 2014Fare evasion in proof-of-payment transit systems: Deriving the optimum inspection level

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59 cited
  • 2021Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data33 cited
  • 2022Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data30 cited
  • Cited by

    • 2024eXplainable DEA approach for evaluating performance of public transport origin-destination pairs61 cited
    • 2025Understanding Gender Gap in Bike-Sharing Services via eXplainable Artificial Intelligence16 cited
    • 2024Using XGBoost and SHAP to explain citizens’ differences in policy support for reimposing COVID-19 measures in the Netherlands9 cited
    • 2025Resilience of public transport in the face of disruptions: Insights from explainable machine learning7 cited