Monitoring the activities of vehicles in modern cities and urban areas has become imperative for solving the traffic related problems. Latest information about mobile vehicles, such as their identification (number plate), position, speed, and so on, are very important for smart traffic management solutions and business analytics. To that end, RFID tags (installed on vehicles) and readers (installed on roads) based traffic monitoring systems have gained a lot of attention due to their cost effectiveness. Usually the RFID readers are much costlier than the RFID tags, therefore, there is always a constraint on the number of RFID readers that can be deployed. This work explores the particular problem of locating suitable places in a road network for RFID readers that can capture the maximum amount of traffic data. To that end, the graph centrality measures are used to find the nodes with most connectivity. The underlying assumption is that the most connected nodes experience the most traffic flow. A new centrality measure is also proposed that is more suitable for analyzing the road networks than the existing graph centrality measures. The experimental results on real maps and data reveal that the newly proposed measure is very effective for analyzing the road networks.