Traffic congestion negatively impacts our society. Most of the traditional transportation planning techniques - though effective - require rigorous amounts of data and analysis which consumes time and resources. This paper uses social network analysis (SNA) to analyze transportation networks, and consequently corroborate the effectiveness of SNA as a complementary tool for improved transportation planning. After creating the connection between the language and concepts of SNA and those of transportation systems - as well as developing a model that utilizes different SNA centrality measures within the transportation context - the authors utilize SNA to investigate traffic networks in three case studies in the state of Louisiana, analyze the results and draw conclusions. To this effect, with minimal cost and time, the model identifies the most critical intersections that should be further investigated using traditional techniques. These results are in agreement with the findings of Louisiana's Department of Transportation and Development.

Social network analysis (SNA) is a well-established methodology for investigating networks through the use of mathematical formulations abstracted from graph theory. It has been successfully used in social sciences to evaluate how individuals and institutions are affected by societal or professional networks, and it has been applied to some civil and construction engineering applications where a network's main actors are people or organizations controlled by people. Current transportation analysis tools are expensive and time consuming, and require rigorous data for reliable results. Accordingly, a quick and inexpensive methodology to preliminarily analyze traffic networks is beneficial to better direct more detailed transportation analyses. Because of its ability to grasp the full complexity and connectivity of networks in a timely and cost effective manner, SNA can fulfill this requirement. Mathematically speaking, social networks are very close to transportation networks because they share fundamental characteristics. This paper uses SNA to analyze transportation networks and consequently corroborate its effectiveness as a complementary tool for improved transportation planning. To this end, the authors adopted a four-step interrelated research methodology: (1) investigating the connection between the language and concepts of SNA and those of transportation systems; (2) using different SNA centrality measures in the transportation context; (3) using SNA in two case studies in Mississippi; and (4) analyzing the results of the case studies and drawing conclusions. The SNA approach was able to easily and quickly determine the most critical intersections in the investigated transportation networks. These results were in alignment with Mississippi Department of Transportation (MDOT 2014) traffic studies. Using SNA, the research also demonstrated that the performance of central intersections drives the overall performance of the area roadway network. Accordingly, SNA is believed to be an effective and innovative tool in transportation analysis. Using it as an initial analysis step to identify critical areas will assist decision makers in better focusing their more detailed analyses using validated traditional methods in such areas only compared with the entire network. This will significantly decrease the invested resources in transportation planning and will create a more integrated and holistic perspective for evaluating transportation networks. (C) 2016 American Society of Civil Engineers.

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.