Mixture-Model-Based Clustering for Daily Traffic Volumes

Daily traffic volume data are collected and stored as historical data. By learning from the historical data, traffic volumes can be predicted. In this paper, the authors propose a clustering method based on the mixture model estimation approach that was introduced in previous papers. This method is compared with the whole-curve-based clustering method. From the method the authors propose, a partial clustering approach is derived based on the components of the mixture model which was introduced before. The partial clustering method based on components is interesting for research that only focuses on single component. The comparison between methods shows that the mixture-model-based method can reach the results of 7.38% to 14.57% of relative errors compared with the whole-curve-based method.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2757-2762
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01602732
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:20PM