Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data

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• 作者：Anabele Lindner ; Cira Souza Pitombo ; ^{cira@sc.usp.br} ; André ; Luiz Cunha
• 关键词：Artificial Neural Networks ; Decision tree algorithms ; Travel mode choice ; Multicollinear data
• 刊名：Travel Behaviour and Society
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：6
• 期：Complete
• 页码：100-109
• 全文大小：847 K
• 卷排序：6

文摘

CT and ANN may overcome disadvantages featured in travel demand traditional modeling. Two different levels of aggregation were considered: TAZ and household-related. CT and ANN turn out not to be affected by multicollinear information and outliers. Generally, data mining yields greater accuracy than logit models for travel mode choice. The use of classifiers has been seen as a positive tool towards travel behavior.