Application of Bayesian Trained Neural Networks to Predict Stochastic Travel Times in Urban Networks

Urban travel time prediction has received much less attention than freeway prediction, because urban travel times are much more stochastic. However, urban travel forms a significant part of total travel time. In this paper, neural networks are used for urban travel time prediction as these are able to deal with noisy data. Bayesian techniques are used for training, resulting in committees with lower error and in confidence bounds. It is shown that the networks are capable of predicting the ‘trend’, obtained through de-noising, with an error in the same order of freeway predictions, and that they accurately predict confidence bounds.

Language

  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; Maps; References; Tables;
  • Pagination: 13p
  • Monograph Title: ITS in Daily Life

Subject/Index Terms

Filing Info

  • Accession Number: 01149138
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 26 2010 10:54AM