Route Choice Modeling with Support Vector Machine

This paper aims at exploring the possibility of utilizing Support Vector Machine (SVM) to establish route choice model. A widely used non-parametric modelling approach, Neural Network (NN), was used to compare with SVM. A stated preferences survey was conducted among 18 participants. Information about three route attributes including travel time, travel time fluctuations and fuel cost were given to participants for making route choice decisions. The data collected from the survey was used to calibrate and test both NN and SVM. The results show that SVM has similar prediction accuracy with NN but has much more computing efficiency.

• Record URL:
  https://doi.org/10.1016/j.trpro.2017.05.151
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S235214651730443X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2017 Bingrong Sun and Byungkyu Brian Park. Published by Elsevier B.V.
• Authors:
  • Sun, Bingrong
  • Park, Byungkyu Brian
• Conference:
  • World Conference on Transport Research - WCTR 2016
  • Location: Shanghai , China
  • Date: 2016-7-10 to 2016-7-15
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References; Tables;
• Pagination: pp 1806-1814
• Serial:
  • Transportation Research Procedia
  • Volume: 25
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Calibration; Data collection; Decision making; Neural networks; Route choice; Stated preferences; Travel time
• Uncontrolled Terms: Support vector machines
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01642417
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 27 2017 2:15PM