It is essential to build, maintain, and use our transportation systems in a manner that meets our current needs while addressing the social and economic needs of future generations. In today's world, transportation congestion causes serious negative effects to our societies. To this end, researchers have been utilizing various statistical methods to study the flow of traffic into the road networks better. However, these valuable studies cannot realize their true potential without solid indepth understanding of the connectivity between the various traffic intersections. This paper bridges the gap between the engineering and social science domains. To this end, the authors propose a dynamic social network analysis framework to study the centrality of the existing road networks. This approach utilizes the field of network analysis where visualization and modeling techniques allow capturing the relationships, interactions, and attributes of and between network constituents and mathematical measurements facilitate analyzing quantitative relationships within the network. Connectivity and the importance of each intersection within the network will be understood using this method. The authors conducted social network analysis (SNA) using two studies in the state of Louisiana. Results indicate intersection SNA modeling aligns with current congestion studies and transportation planning decisions.