With ever increasing vehicles on the roads, traffic congestion is one of the major concerns of the modern world. Cities frequently experience heavy traffic, especially in developing countries due to lack of technology and automatic control systems. To address this issue, numerous models of traffic monitoring have been proposed by researchers. However, due to the static nature of these approaches, they are either inefficient or require a lot of resources and in turn a huge investment. Use of low-cost Unmanned aerial vehicles (UAVs) for urban traffic Surveillance is gaining increased attention in the research community in order to overcome existing limitations and provide a cost-effective and sustainable solution. A key challenge is to optimize the continuous coverage of large urban territories using a minimal number of UAVs. This research provides a comprehensive methodology to determine optimal resource requirements as well as positioning for improved coverage. Relying on a structural analysis of road network (represented as a graph) and using a combination of edge clustering and Silhouette-Score metric, the proposed method provides full coverage with a sufficient number of UAVs. Experimental results show that operational performance for continuous coverage is improved with reduced idle time.