The aim of the thesis is to provide an extension to an arterial decomposition model to increase the progression efficiency of vehicles traversing through the network corridor. Traffic signal progression has always been proven as an effective way to efficiently move the vehicles from one end to other without stopping at the red light. Different progression models have proved promising in providing green bands to through traffic along the arterial, bidirectionally. When a long arterial that contains a large number of intersections is considered, an effective progression plan is not able to offer definite better green bands for the through movement. To tackle this issue of sub-grouping and providing green band, this study provides an expansion to the objective function to a decomposition model that can concurrently determine the arterial and network decomposition strategy and optimize the resulting signal progression plan for each subgroup. It also defines a network decomposition technique to decompose a relatively large-scale urban network. To identify the most critical subnetwork within a network, a new search order algorithm is proposed with an integrated control objective function of maximizing the bandwidth. The proposed model can minimize the required number of subgroups and incorporate the traffic volumes but still satisfy the operational needs by achieving a minimum bandwidth that is required for progression. In addition, the proposed model is formulated with a mixed-integer-linear-programming technique that can guarantee a globally optimal solution. A numerical example is conducted on a field Arterial corridor, which consists of fifteen signalized intersections and relatively large grid network with thirty intersections. The results show that the proposed model proved to perform effectively and is able to get the best solution for the study network. In addition, traffic simulation is also being conducted using PTV VISSIM 7.0 to achieve better understanding and evaluate the network performance for different case scenarios.