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Tour-based Travel Mode Choice Estimation based on Data Mining and Fuzzy Techniques
9 references·0 citing·33 second hop
Tour-based Travel Mode Choice Estimation based on Data Mining and Fuzzy Techniques (2015)Tour-based Travel Mode Choice Estimation …Neural Networks and Logit Models Applied to Commuters’ Mobility in The Metropolitan Area of Milan (2019)Neural Networks and Logit Mod…Examining the Possibility of Fuzzy Set Theory Application in Travel Demand Modelling (2010)Examining the Possibility of …Tour-Based Mode Choice Modeling: Using an Ensemble of Conditional and Unconditional Data Mining Classifiers (2009)Tour-Based Mode Choice Modeli…Mode Choice Models For Long Distance Travel In USA (2006)Mode Choice Models For Long D…A tour-based model of travel mode choice (2005)A tour-based model of travel …Modeling transportation mode choice through artificial neural networks (2004)Modeling transportation mode …A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice (2000)A comparison of the predictiv…Neural network analysis of travel behavior: Evaluating tools for prediction (1996)Neural network analysis of tr…Modelling inter-urban transport flows in Italy: A comparison between neural network analysis and logit analysis (1996)Modelling inter-urban transpo…ALBATROSS : a learning based transportation oriented simulation system (2000)Activity-based disaggregate travel demand model system with activity schedules (2001)Discrete Choice Methods with Simulation (2001)Intra-household interactions in transport research: a review (2015)Travel Mode Use, Travel Mode Shift and Subjective Well-Being: Overview of Theories, Empirical Findings and Policy Implications (2015)A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity (2022)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Discrete choice analysis: Theory and application to travel demand (1988)Bounded Rationality in Macroeconomics (1995)Potential Contributions of Spatial Analysis to Geographic Information Systems for Transportation (GIS‐T) (1999)Deep neural networks for choice analysis: Extracting complete economic information for interpretation (2020)Choice modelling in the age of machine learning - Discussion paper (2021)The Use of the Life-Style Concept in Travel Demand Models (1983)Repetition and Variability in Urban Travel (1986)Exploration of route choice behavior with advanced traveler information using neural network concepts (1993)Understanding urban mobility and the impact of public policies: The role of the agent-based models (2016)A review of Machine Learning (ML) algorithms used for modeling travel mode choice (2019)Enhancing discrete choice models with representation learning (2020)Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation (2000)Factors Influencing Mode Selections of Low-cost Carriers and a Full-service Airline in Thailand (2010)Development of neuro‐fuzzy‐based multimodal mode choice model for commuter in Delhi (2018)Analyzing significant variables for choosing different modes by female travelers (2021)Multiple Attribute Decision Making: Methods and Applications (1981)Fuzzy sets (1996)Forecasting with artificial neural networks: (1998)PREDICTING URBAN TRIP GENERATION USING A FUZZY EXPERT SYSTEM (2012)Spatial distribution of urban trips in recently expanded Surat city through Fuzzy Logic with various clustering Techniques: A case study of typical metropolitan city in India (2017)Conditional logit analysis of qualitative choice behavior (1972)Urban Travel Demand: A Behavioral Analysis (1977)Same-Day Mode Choice Modeling with Household Vehicle Usage Simulation in Developing Countries (2011)Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling (2018)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)
this work references cited by second hop

References

  • 2000A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice182 cited
  • 2005A tour-based model of travel mode choice177 cited
  • 1996Modelling inter-urban transport flows in Italy: A comparison between neural network analysis and logit analysis80 cited
  • 1996Neural network analysis of travel behavior: Evaluating tools for prediction65 cited
  • 2004Modeling transportation mode choice through artificial neural networks32 cited
  • 2019Neural Networks and Logit Models Applied to Commuters’ Mobility in The Metropolitan Area of Milan11 cited
  • 2009Tour-Based Mode Choice Modeling: Using an Ensemble of Conditional and Unconditional Data Mining Classifiers10 cited
  • 2010Examining the Possibility of Fuzzy Set Theory Application in Travel Demand Modelling9 cited
  • 2006Mode Choice Models For Long Distance Travel In USA6 cited

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