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
Link To Document