Transportation agencies face escalating challenges in forecasting the traffic demand. Traditional prediction methods focused on individual transportation sectors and failed to study the inter-dependencies between the different transportation systems. Hence, there is a need for more advanced and holistic modeling techniques. To this end, this paper models and analyses an urban transportation system-of-systems incorporating seven various systems: population and GDP, CO2 emission, gasoline price and total vehicle trips, traffic demand, public and private transportation, transportation investment, and traffic congestion. Accordingly, this research simulates transportation networks as a collection of task-oriented systems that combine their resources to form a complex system with increased functionality. The goal of this paper is to understand the traffic complex behavior of urban transportation networks and to study the interdependencies between the different variables. The proposed framework could be implemented to any urban city, county, state, or country. The developed model incorporates a hybrid modeling approach that includes: logistic model, system dynamics, stochastic cellular automata, chaos theory, and Lotka-Volterra model. The final model is demonstrated using a case study. The contribution of this paper lies in modeling the transportation network as a dynamic system of systems rather than as static model as provided in previous studies.