Optimal neural networks architectures for the flow–density relationships of traffic models

Urban traffic is a complex process that is often described by macroscopic flow models. Anyway, the parameters identification of these models remains a heavy work. This paper proposes neural networks architectures that are inspired from the general form of the well-known traffic model but which have the advantage to be easier in identification and which track real traffic data more correctly.

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Year of publication:	2002
Authors:	Messai, Nadhir ; Thomas, Philippe ; Lefebvre, Dimitri ; El Moudni, Abdellah
Published in:	Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 60.2002, 3, p. 401-409
Publisher:	Elsevier
Subject:	Traffic model \| Neural networks \| Flow–density relationship

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010749122