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

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  • Zhao, Shuangming
  • Zhao, Pengxiang
  • Cui, Yunfan

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.

Suggested Citation

  • Zhao, Shuangming & Zhao, Pengxiang & Cui, Yunfan, 2017. "A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 143-157.
    • Handle: RePEc:eee:phsmap:v:478:y:2017:i:c:p:143-157
    DOI: 10.1016/j.physa.2017.02.069
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    1. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    2. Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
    3. Zhao, Pengxiang & Jia, Tao & Qin, Kun & Shan, Jie & Jiao, Chenjing, 2015. "Statistical analysis on the evolution of OpenStreetMap road networks in Beijing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 59-72.
    4. A Penn & B Hillier & D Banister & J Xu, 1998. "Configurational Modelling of Urban Movement Networks," Environment and Planning B, , vol. 25(1), pages 59-84, February.
    5. B Hillier & A Penn & J Hanson & T Grajewski & J Xu, 1993. "Natural Movement: Or, Configuration and Attraction in Urban Pedestrian Movement," Environment and Planning B, , vol. 20(1), pages 29-66, February.
    6. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
    7. Song Gao & Yaoli Wang & Yong Gao & Yu Liu, 2013. "Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality," Environment and Planning B, , vol. 40(1), pages 135-153, February.
    8. Yu Liu & Zhengwei Sui & Chaogui Kang & Yong Gao, 2014. "Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
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    7. Mahyar, Hamidreza & Hasheminezhad, Rouzbeh & Ghalebi K., Elahe & Nazemian, Ali & Grosu, Radu & Movaghar, Ali & Rabiee, Hamid R., 2018. "Compressive sensing of high betweenness centrality nodes in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 166-184.
    8. Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan & Teixeira, Rui, 2022. "Identifying critical and vulnerable links: A new approach using the Fisher information matrix," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
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    10. Wang, Duo & Sipahi, Rifat, 2024. "Betweenness centrality can inform stability and delay margin in a large-scale connected vehicle system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    11. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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