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. (C) 2002 IMACS. Published by Elsevier Science B.V. All rights reserved.
neural networks flow-density relationship traffic model