Travel Mode Recognition Using RBF Neural Network

This paper focuses on travel mode recognition based on Radial Basis Function (RBF) Neural Networks using the characters extracted from travel trace information collected by smartphone. First, the authors analyzed the characters of travel mode to find out what the key parameters are to divide the two kinds of travel mode by using the F-score method in order to make up the character vectors to recognize different travel modes. Eventually, a classifier of the RBF-based Neural Network can be established. This Neural Network is trained by travel mode samples extracted from travel trace data in Dalian. For comparison, BP Neural Networks are used. The result shows that the recognition accuracy rate of RBF Neural Network is better than BP Neural Network, and suggests that the RBF Neural Network has a better recognition performance.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 711-721
  • Monograph Title: CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems

Subject/Index Terms

Filing Info

  • Accession Number: 01532901
  • Record Type: Publication
  • ISBN: 9780784413623
  • Files: TRIS, ASCE
  • Created Date: Jul 2 2014 3:02PM