Artificial Intelligence Approach to Modeling Travel Mode Switching in a Dynamic Behavioral Process
As road congestion gets exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to get off the road and use other sustainable modes. In order to better analyze these planning/policy strategies, there is imperative need in multimodal analysis and accurately modeling travelers’ mode switching behavior. In this paper, a popular artificial intelligence approach, Decision Tree, is used to explore the underlying rules of travelers’ switching decision between two modes under proposed framework of dynamic mode searching and switching. The 2007/2008 TPB Household Travel Survey data is used to calibrate and validate the decision tree models. An effective and practical method for mode switching decision tree induction is proposed. Loss matrix is introduced to handle the class imbalance issues. Important factors and their relative importance are analyzed through the information gain and the feature selection. A total number of six mode switching models between each two modes are trained with a high accuracy. Through comparison with Logit models, the improved prediction ability of the decision tree models has been demonstrated.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ70 Artificial Intelligence and Advanced Computing Applications.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Tang, Liang
- Xiong, Chenfeng
- Zhang, Lei
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Conference:
- Transportation Research Board 93rd Annual Meeting
- Location: Washington DC
- Date: 2014-1-12 to 2014-1-16
- Date: 2014
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 93rd Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Artificial intelligence; Choice models; Data mining; Decision trees; Modal shift; Mode choice; Travel behavior
- Subject Areas: Highways; Passenger Transportation; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01518698
- Record Type: Publication
- Report/Paper Numbers: 14-4067
- Files: TRIS, TRB, ATRI
- Created Date: Mar 20 2014 1:39PM