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Quantifying Parking Difficulty with Transport and Prediction Models for Travel Mode Choice Modelling
10 references·2 citing·47 second hop
Quantifying Parking Difficulty with Transport and Prediction Models for Travel Mode Choice Modelling (2023)Quantifying Parking Difficulty with Trans…Predicting the travel mode choice with interpretable machine learning techniques: A comparative study (2022)Predicting the travel mode ch…A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity (2022)A systematic comparative eval…The Response of Urban Travel Mode Choice to Parking Fees considering Travel Time Variability (2020)The Response of Urban Travel …Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region (2020)Longitudinal spatial dataset …A systematic review of machine learning classification methodologies for modelling passenger mode choice (2020)A systematic review of machin…What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling (2018)What about the dynamics in da…Urban Travel Behavior Study Based on Data Fusion Model (2018)Urban Travel Behavior Study B…A comparative study of machine learning classifiers for modeling travel mode choice (2017)A comparative study of machin…Data-Mining Approach to Work Trip Mode Choice Analysis in Chicago, Illinois, Area (2010)Data-Mining Approach to Work …Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks (2003)Work Travel Mode Choice Model…Analysing Urban Transport Using Synthetic Journeys (2024)Analysing Urban Transport Usi…Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland (2024)Air quality and transport beh…Conditional logit analysis of qualitative choice behavior (1972)Alternative nested logit models: structure, properties and estimation (1998)A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice (2000)Statistical methods versus neural networks in transportation research: Differences, similarities and some insights (2010)Big Data Analytics in Intelligent Transportation Systems: A Survey (2018)Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020)RAMBLAS: A Regional Planning Model Based on the Microsimulation of Daily Activity Travel Patterns (2000)ILUTE: An Operational Prototype of a Comprehensive Microsimulation Model of Urban Systems (2005)Choice modelling in the age of machine learning - Discussion paper (2021)Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm (2024)Discrete Choice Analysis: Theory and Application to Travel Demand (1987)Travel and the Built Environment (2010)Is your dataset big enough? 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A SALC modeling based on RRM (2025)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. (2009)Enhancing discrete choice models with representation learning (2020)A New Approach to Consumer Theory (1966)Mixed MNL models for discrete response (2000)A prediction and behavioural analysis of machine learning methods for modelling travel mode choice (2023)On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice (2023)Understanding Travel Mode Choice Behavior: Influencing Factors Analysis and Prediction with Machine Learning Method (2023)The measurement of urban travel demand (1974)Discrete choice analysis: Theory and application to travel demand (1988)Public transport equity with the concept of time-dependent accessibility using Geostatistics methods, Lorenz curves, and Gini coefficients (2023)Tourists' urban travel modes: Choices for enhanced transport and environmental sustainability (2024)
this work references cited by second hop

References

  • 2017A comparative study of machine learning classifiers for modeling travel mode choice448 cited
  • 2003Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks215 cited
  • 2022Predicting the travel mode choice with interpretable machine learning techniques: A comparative study183 cited
  • 2020A systematic review of machine learning classification methodologies for modelling passenger mode choice172 cited
  • 2022A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity108 cited
  • 2020Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region55 cited
  • 2018What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling41 cited
  • 2010Data-Mining Approach to Work Trip Mode Choice Analysis in Chicago, Illinois, Area22 cited
  • 2020The Response of Urban Travel Mode Choice to Parking Fees considering Travel Time Variability10 cited
  • 2018Urban Travel Behavior Study Based on Data Fusion Model9 cited

Cited by

  • 2024Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland3 cited
  • 2024Analysing Urban Transport Using Synthetic Journeys2 cited

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