Neural Agent Car-Following Models

This paper applies reactive agent techniques based in artificial neural networks (ANN) for the intelligent transportation concept of car-following. The model, which was verified on both microscopic and macroscopic levels, was found to be a better predictor than both the Gipps and psychophysical car-following models. It is also found by local and asymptotic stability analyses that this ANN is robust for such applications despite both mild and severe traffic disturbances. The authors discuss a number of historical car-following models and list their method and weaknesses. These include: GHR model, CA model, the linear model, the psychophysical/AP model mentioned above, the fuzzy logic based model, desired spacing models, capacity drop, and neural network-based models. In future research, the authors intend to explore the studied ANN with a larger data set and in a wider ranger of driving situations.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Authors:
  • Panwai, Sakda
  • Dia, Hussein
• Publication Date: 2007-3

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 60-70
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 8
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Car following; Microsimulation; Neural networks; Traffic models
• Subject Areas: Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 01044832
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: BTRIS, TRIS
• Created Date: Mar 22 2007 2:08PM