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Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model
11 references·16 citing·37 second hop
Modeling travel mode and timing decisions: Comparison of artificial neural networks and copula-based joint model (2017)Modeling travel mode and timing decisions…A copula-based approach to accommodate residential self-selection effects in travel behavior modeling (2009)A copula-based approach to ac…Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models (2005)Multilayer feedforward networ…Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks (2003)Work Travel Mode Choice Model…Statistical and Econometric Methods for Transportation Data Analysis (2003)Statistical and Econometric M…The generalized nested logit model (2001)The generalized nested logit …Mixed MNL models for discrete response (2000)Mixed MNL models for discrete…A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice (2000)A comparison of the predictiv…Analysis of travel mode and departure time choice for urban shopping trips (1998)Analysis of travel mode and d…Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area (1997)Application of Cross-Nested L…THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS (1982)THE SCHEDULING OF CONSUMER AC…Random variables, joint distribution functions, and copulas (1973)Random variables, joint distr…Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis (2024)Linking short- and long-term …Comparing hundreds of machine learning and discrete choice models for travel demand modeling: An empirical benchmark (2024)Comparing hundreds of machine…Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach (2023)Exploring passengers’ choice …Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions (2023)Comparing and contrasting cho…Joint modeling of mode choice and travel distance with intra-household interactions (2022)Joint modeling of mode choice…Can continued anti-epidemic measures help post-COVID-19 public transport recovery? Evidence from Taiwan (2022)Can continued anti-epidemic m…A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability (2022)A neural-embedded discrete ch…Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model (2021)Framework for development of …Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory (2021)Extrapolation-enhanced model …Analyzing travel behavior in Hanoi using Support Vector Machine (2021)Analyzing travel behavior in …Wearable fitness trackers and smartphone pedometer apps: Their effect on transport mode choice in a transit-oriented city (2020)Wearable fitness trackers and…Enhancing discrete choice models with representation learning (2020)Enhancing discrete choice mod…Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting (2020)Data Analytics in the Supply …Trip distribution modeling with Twitter data (2019)Trip distribution modeling wi…Modeling the Effectiveness of Infrastructure and Travel Demand Management Measures to Improve Traffic Congestion during Typhoons (2018)Modeling the Effectiveness of…Activity start time and duration: incorporating regret theory into joint discrete–continuous models (2018)Activity start time and durat…MODELING THE CHOICE OF RESIDENTIAL LOCATION (1978)Discrete Choice Analysis (1985)A mixed spatially correlated logit model: formulation and application to residential choice modeling (2003)Modeling time-dependent travel choice problems in road networks with multiple user classes and multiple parking facilities (2005)Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model (2018)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)On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit (1977)Choice set generation within the generalized extreme value family of discrete choice models (2001)Multilateral resistance to migration (2012)Elimination by aspects: A theory of choice. (1972)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete choice analysis: Theory and application to travel demand (1988)Applications of Simulation Methods in Environmental and Resource Economics (2005)Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application (2019)Empirical research on nominal exchange rates (1995)Measuring Market Power in the Ready-to-Eat Cereal Industry (1998)A latent class model for discrete choice analysis: contrasts with mixed logit (2003)The Mixed Logit model: The state of practice (2003)SOME THOUGHTS ON THE DISTRIBUTION OF EARNINGS 1 (1951)Shadow Prices, Market Wages, and Labor Supply (1974)Correlation and Dependence in Risk Management: Properties and Pitfalls (2002)Built environment and mode choice relationship for commute travel in the city of Rajkot, India (2016)Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice (2016)The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination? (2017)Obesity relationships with community design, physical activity, and time spent in cars (2004)Service Quality Attributes Affecting Customer Satisfaction for Bus Transit (2007)Public transit user satisfaction: Variability and policy implications (2008)Discrete Choice Methods and their Applications to Short Term Travel Decisions (1999)The valuation of reliability for personal travel (2001)The Economics of Urban Transportation (2007)Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis (2018)A systematic review of machine learning in logistics and supply chain management: current trends and future directions (2021)On Nonparametric Measures of Dependence for Random Variables (1981)Copulas and Markov processes (1992)Measures of risk (2002)
this work references cited by second hop

References

  • 2000Mixed MNL models for discrete response4,354 cited
  • 2003Statistical and Econometric Methods for Transportation Data Analysis1,901 cited
  • 1982THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS1,083 cited
  • 1973Random variables, joint distribution functions, and copulas680 cited
  • 2001The generalized nested logit model527 cited
  • 2009A copula-based approach to accommodate residential self-selection effects in travel behavior modeling310 cited
  • 1997Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area298 cited
  • 1998Analysis of travel mode and departure time choice for urban shopping trips223 cited
  • 2003Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks211 cited
  • 2000A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice182 cited
  • 2005Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models135 cited

Cited by

  • 2020Enhancing discrete choice models with representation learning152 cited
  • 2020Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting134 cited
  • 2022A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability

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77 cited
  • 2019Trip distribution modeling with Twitter data74 cited
  • 2024Comparing hundreds of machine learning and discrete choice models for travel demand modeling: An empirical benchmark55 cited
  • 2021Extrapolation-enhanced model for travel decision making: An ensemble machine learning approach considering behavioral theory43 cited
  • 2022Can continued anti-epidemic measures help post-COVID-19 public transport recovery? Evidence from Taiwan32 cited
  • 2023Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions26 cited
  • 2023Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach25 cited
  • 2021Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model23 cited
  • 2021Analyzing travel behavior in Hanoi using Support Vector Machine20 cited
  • 2022Joint modeling of mode choice and travel distance with intra-household interactions17 cited
  • 2024Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis17 cited
  • 2018Activity start time and duration: incorporating regret theory into joint discrete–continuous models17 cited
  • 2018Modeling the Effectiveness of Infrastructure and Travel Demand Management Measures to Improve Traffic Congestion during Typhoons16 cited
  • 2020Wearable fitness trackers and smartphone pedometer apps: Their effect on transport mode choice in a transit-oriented city15 cited