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

Nadhir Messai, Philippe Thomas, Dimitri Lefebvre and Abdellah El Moudni

Mathematics and Computers in Simulation (MATCOM), 2002, vol. 60, issue 3, 401-409

Abstract: 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.

Keywords: Traffic model; Neural networks; Flow–density relationship (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:60:y:2002:i:3:p:401-409

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