Descent direction algorithm with multicommodity flow problem for signal optimization and traffic assignment jointly

Network managers wish to optimize control parameters such as signal setting which are very related to the traffic assignment models. On the other hand traffic assignment patterns as an important instrument for predicting the amount of flow on network links are dependent to control decisions. According to the significance of this concept, some important papers about this mutually relation are reviewed in this paper. Then we implement a nonlinear algorithm on a minimal cost multicommodity flow (MCMF) problem to optimize some control policies subject to optimal flows. Although we take signal times into account, but this approach has a far more reaching application in urban network control and design. We employ a hybrid intelligent algorithm integrating decent direction algorithm and an interior point algorithm in a mutually consistent scheme for obtaining optimal signals and equilibrium flows. An example is given to illustrate the effectiveness of our scheme.