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USING DECISION TREES FOR ESTIMATING MODE CHOICE OF TRIPS IN BUCA-IZMIR
6 references·0 citing·24 second hop
USING DECISION TREES FOR ESTIMATING MODE CHOICE OF TRIPS IN BUCA-IZMIR (2013)USING DECISION TREES FOR ESTIMATING MODE …Data-Mining Approach to Work Trip Mode Choice Analysis in Chicago, Illinois, Area (2010)Data-Mining Approach to Work …Where are public transit needed – Examining potential demand for public transit for commuting trips (2007)Where are public transit need…Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks (2003)Work Travel Mode Choice Model…European Transport Conference (1999)European Transport ConferenceModal Split Analysis Using Logit Models (1987)Modal Split Analysis Using Lo…Application of disaggregate travel demand models (1982)Application of disaggregate t…Dependence between Shipment Size and Mode in Freight Transportation (1985)Investigation of Inland Freight Transport Modal Choice in Greece (2010)DEVELOPMENT OF MODE CHOICE MODELS USING MULTINOMIAL LOGIT APPROACH IN HYDERABAD CITY (2015)Discrete Choice Analysis: Theory and Application to Travel Demand. (1987)Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework (1998)Valuing time and reliability: assessing the evidence from road pricing demonstrations (2005)Spatial variation of transit service quality preferences in Dar-es-Salaam (2012)Effects of street pattern, traffic, road infrastructure, socioeconomic and demographic characteristics on public transit ridership (2016)Continuum approximation modeling of transit network design considering local route service and short-turn strategy (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)Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model (2018)A systematic review of machine learning classification methodologies for modelling passenger mode choice (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)EXTRACTION OF THE ACTUAL CONDITION OF THE COMMUNITY CYCLE BY USING DATA MINING TECHNIQUES (2014)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)Urban Trip Distribution in Practice. II: Quick Response and Special Topics (1993)A Dynamic Microsimulation Model System for Regional Travel Demand Forecasting (1997)ACTIVITY-BASED TRAVEL FORECASTING: WHAT ARE SOME ISSUES? (1997)Peak Car and Beyond: The Fourth Era of Travel (2013)Economic growth, energy consumption and CO2 emissions in Gulf Cooperation Council countries (2014)Congestion pricing practices and public acceptance: A review of evidence (2018)
this work references cited by second hop

References

  • 1999European Transport Conference219 cited
  • 2003Work Travel Mode Choice Modeling with Data Mining: Decision Trees and Neural Networks211 cited
  • 2007Where are public transit needed – Examining potential demand for public transit for commuting trips55 cited
  • 2010Data-Mining Approach to Work Trip Mode Choice Analysis in Chicago, Illinois, Area22 cited
  • 1982Application of disaggregate travel demand models15 cited
  • 1987Modal Split Analysis Using Logit Models14 cited

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