Modeling Stream Speed in Heterogeneous Traffic Environment Using ANN-Lessons Learnt

In this paper, the authors describe how an Artificial Neural Network (ANN) approach is used to model traffic stream speed resulting from the complex interactions among different vehicle types in a heterogeneously mixed traffic volume. Two different categories of an ANN model are developed, with their fundamental differences being the nature of input vectors that are used. In addition, the authors discuss how further investigation is conducted on both categories of the ANN models by carrying out relevant logical tests in order to understand the rationality of the relationship captured in the models.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/16484142
• Authors:
  • Basu, Debasis
  • Maitra, Bhargab
• Publication Date: 2006

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References;
• Pagination: pp 269-273
• Serial:
  • Transport: Research Journal of Vilnius Gediminas Technical University and Lithuanian Academy of Sciences
  • Volume: 21
  • Issue Number: 4
  • Publisher: Vilnius Gediminas Technical University
  • ISSN: 1648-4142
  • EISSN: 1648-3480

Subject/Index Terms

• TRT Terms: Mathematical models; Neural networks; Traffic flow; Traffic speed; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 01043016
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: BTRIS, TRIS
• Created Date: Feb 12 2007 12:28PM