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 :
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