• DocumentCode
    2755773
  • Title

    Determining maximum traffic flow using backpropagation

  • Author

    Heymans, B.C. ; Onema, J.P. ; Carriere, P.E.

  • Author_Institution
    Texas A&I Univ., Kingsville, TX
  • fYear
    1991
  • fDate
    8-14 Jul 1991
  • Abstract
    Summary form only given, as follows. The backpropagation model, a neural network model, was used to relate the traffic flow and the traffic density parameters used in the Greenberg equations to design traffic and highway constructions. After simulation, the relations between the different traffic parameters can be adequately learned by the neural network
  • Keywords
    civil engineering; neural nets; road traffic; Greenberg equations; backpropagation; highway constructions; maximum traffic flow; neural network model; traffic density parameters; Backpropagation; Computational modeling; Computer architecture; Computer science; Equations; Road transportation; Robot control; Traffic control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    0-7803-0164-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.1991.155686
  • Filename
    155686