A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China

Shuangming Zhao, Pengxiang Zhao and Yunfan Cui

Physica A: Statistical Mechanics and its Applications, 2017, vol. 478, issue C, 143-157

Abstract: In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.

Keywords: Taxi trajectory data; Network centrality; Urban traffic flow; Multi-mode; Correlation analysis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:478:y:2017:i:c:p:143-157

DOI: 10.1016/j.physa.2017.02.069

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