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Identifying the most critical transportation intersections using social network analysis

机译：使用社交网络分析识别最关键的交通路口

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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.
机译：交通拥堵会对我们的社会造成负面影响。大多数传统的运输计划技术尽管有效，但需要大量的数据和分析，这会浪费时间和资源。本文使用社会网络分析（SNA）来分析运输网络，因此证实了SNA作为改进运输计划的补充工具的有效性。在建立SNA与运输系统的语言和概念之间的联系-并开发出在运输环境中利用不同SNA中心性度量的模型之后-作者利用SNA在以下三个案例研究中调查了交通网络路易斯安那州，分析结果并得出结论。为此，该模型以最小的成本和时间即可确定最关键的交叉口，应使用传统技术对其进行进一步研究。这些结果与路易斯安那州交通与发展部的调查结果一致。

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