Transportation systems are the economic foundation of any regional development. Our reliance on transportation to move goods and resources and to ensure access to labor to increase productivity, all have tremendous impact on revenue generation and growth. Traffic congestion is an inevitable byproduct of economic growth; the costs of traffic is not just time wasted, but also include the financial loss and environmental impacts of fuel being wasted. As an effort to understand congestion formation, this project investigates modeling traffic as a network and uses a percolation model to identify a normal traffic pattern as exhibited by the inhabitants of the region. Using real street maps from the OpenStreetMap project, morning, noon, and evening rush-hour traffic zones in Westwood Village were created to simulate the travel behavior of the inhabitants. The street bottlenecks identified for a 24-hr period were then compared to those formed from a uniform traffic flow. The results from this study may provide the foundation for a reasonable starting configuration for a self-organizing traffic light network that can dynamically adapt to unexpected demand in real-time.