OPTIMIZATION OF RAILWAY OPERATIONS USING NEURAL NETWORKS

In this paper, neural networks (an empirically-based AI approach) are examined for obtaining good solutions in short time periods for the train formation problem (TFP). Following an overview, and formulation of railroad operations, a neural network formulation and solution to the problem are presented. First a training process for neural network development is conducted followed by a testing process that indicates that the neural network model will probably be both sufficiently fast, and accurate, in producing train formation plans.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Corporate Authors:

  Elsevier

  The Boulevard, Langford Lane
  Kidlington, Oxford  United Kingdom  OX5 1GB
• Authors:
  • Martinelli, D R
  • Teng, H
• Publication Date: 1996-2

Language

• English

Media Info

• Pagination: p. 33-49
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 4
  • Issue Number: 1
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Artificial intelligence; Network analysis (Planning); Optimization; Railroads; Train operation
• Subject Areas: Freight Transportation; Highways; Operations and Traffic Management; Planning and Forecasting; Railroads; I71: Traffic Theory;

Filing Info

• Accession Number: 00723337
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 26 1996 12:00AM