Macroscopic Modeling of Freeway Traffic using an

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macro-scopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their behavior and often do not track real traffic data correctly. On the other hand, micro-scopic traffic flow models can yield more detailed and accurate repre-sentations of traffic flow but are computationally intensive and typically not suitable for real-time implementation. Nevertheless, such implementations are likely to be necessary for development and appli-cation of advanced traffic control concepts in intelligent vehicle-highway systems. The development of a multilayer feed-forward artificial neural network model to address the freeway traffic system identification problem is presented. The solution of this problem is viewed as an essential element of an effort to build an improved free-way traffic flow model for the purpose of developing real-time predic