APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS TO INTELLIGENT VEHICLE-HIGHWAY SYSTEMS

The potential applications of artificial neural networks (ANNs) to intelligent vehicle-highway systems (IVHSs) are evaluated, and the extent of use and the position of ANNs in future IVHS implementations are discussed. The state of the art and the potential implementation needs of IVHSs are reviewed, and the characteristics, properties, limitations, and application domains of ANNs are discussed from a technical perspective. On the basis of review and discussion, potential application domains of ANNs to IVHSs are evaluated. A technical demand-supply matrix is provided to indicate the most possible potential application domains of ANNs to IVHSs. As an application case study of ANNs in the implementation of IVHSs, an ANN-based model is established for vehicle travel time estimation. The results and findings associated with the development of the neural network-based travel time estimation model are also reported. It is concluded that (a) ANNs can provide most techniques needed by IVHSs, and (b) for some IVHS implementation domains, ANNs may be superior to conventional techniques. The case study demonstrates the modeling feasibility of ANNs for potential IVHS implementations.

• Record URL:
  http://onlinepubs.trb.org/Onlinepubs/trr/1994/1453/1453-009.pdf
• Record URL:
  https://scholar.google.com/scholar_lookup?title=APPLICATIONS+OF+ARTIFICIAL+NEURAL+NETWORKS+TO+INTELLIGENT+VEHICLE-HIGHWAY+SYSTEMS&author=J.+Hua&author=A.+Faghri&publication_year=1994
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• Supplemental Notes:
  • This paper appears in Transportation Research Record No. 1453, Intelligent Transportation Systems: Evaluation, Driver Behavior, and Artificial Intelligence. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved
• Authors:
  • Hua, Jiuyi
  • Faghri, Ardeshir
• Publication Date: 1994

Language

• English

Media Info

• Features: Figures; References;
• Pagination: p. 83-90
• Monograph Title: Intelligent transportation systems: evaluation, driver behavior, and artificial intelligence
• Serial:
  • Transportation Research Record
  • Issue Number: 1453
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Case studies; Estimating; Estimation theory; Implementation; Intelligent transportation systems; Neural networks; Travel time
• Old TRIS Terms: Application domains
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Research; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 00676573
• Record Type: Publication
• ISBN: 0309060613
• Files: TRIS, TRB, ATRI
• Created Date: Apr 13 1995 12:00AM