Abstract
With the rapid growth of holiday travel market, more and more attention has been paid to the analysis of holiday travel behavior, such as the holiday travel mode choice. This study presents a cluster-based logistic regression model for predicting the travel mode choice on holiday. At first, a regression and classification tree approach is employed to split the source date to clusters. Based on the data collected from the Beijing Fragrant Hills Park during the Qingming Festival (Tomb-Sweeping Day), an optimal tree with two levels and three leaf nodes is built and the collected data are divided into three clusters according to the tree structure. The three clusters are further considered as the dummy variables for the logistic regression analysis. Since the cluster based logistic regression model avoids the variable interaction effects, it significantly outperforms the logistic regression model in terms of prediction accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
