Travel Mode Choice Modeling with Support Vector Machines

This study investigates the applications of nontraditional models for travel mode choice modeling, which traditionally has relied on disaggregate discrete choice models such as multinomial logit models. A new artificial intelligence model, a support vector machine, is applied for the first time to travel mode choice modeling. This support vector machine model is tested and compared with a multinomial logit model and a multilayer feedforward neural network model based on data collected in the San Francisco Bay Area in California. Two scenarios with different training data sizes are tested. For both scenarios, the support vector machine model outperforms the multinomial logit model in terms of fitting and testing results. Although the multilayer feedforward neural network model performs best for fitting, it underperforms the other two models for testing. It is recommended that the support vector machine model be used as an alternative procedure for travel mode choice modeling because of its promising performance and easy implementation.

• Record URL:
  https://doi.org/10.3141/2076-16
• Summary URL:
  http://scholar.google.com/scholar_lookup?title=Travel+Mode+Choice+Modeling+with+Support+Vector+Machines&author=Y.+Zhang&author=Y.+Xie&publication_year=2008
• Availability:
  • Find a library where document is available. Order URL: http://www.trb.org/Main/Public/Blurbs/160602.aspx
• Authors:
  • Zhang, Yunlong
  • Xie, Yuanchang
• Publication Date: 2008

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 141-150
• Monograph Title: Travel Demand 2008
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2076
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Artificial intelligence; Choice models; Mode choice; Multinomial logits; Neural networks
• Uncontrolled Terms: Discrete choice models; Support vector machines
• Geographic Terms: San Francisco Bay Area
• Subject Areas: Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01099471
• Record Type: Publication
• ISBN: 9780309125918
• Files: TRIS, TRB, ATRI
• Created Date: May 21 2008 7:08AM