Identifying the most critical transportation intersections using social network analysis

Islam H. El-adaway, Ibrahim Abotaleb and Eric Vechan

Transportation Planning and Technology, 2018, vol. 41, issue 4, 353-374

Abstract: 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.

Date: 2018
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DOI: 10.1080/03081060.2018.1453456

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