A comparative study of machine learning classifiers for modeling travel mode choice
作者：
Highlights：
• A comparison of 7 classifiers for travel mode prediction is performed.
• Prediction accuracy and variable importance for each travel mode is investigated.
• Among the investigated classifiers, random forest performs best.
• Trip distance followed by the number of cars are the most important variables.
• The importance of other variables varies with travel mode and classifier.
摘要
•A comparison of 7 classifiers for travel mode prediction is performed.•Prediction accuracy and variable importance for each travel mode is investigated.•Among the investigated classifiers, random forest performs best.•Trip distance followed by the number of cars are the most important variables.•The importance of other variables varies with travel mode and classifier.
论文关键词：Travel mode choice,Classification,Machine learning,The Netherlands
论文评审过程：Received 12 October 2016, Revised 14 January 2017, Accepted 30 January 2017, Available online 13 February 2017, Version of Record 21 February 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.057
