Improved efficient queue resource reallocation strategy for traffic dynamics on scale-free networks

Jinlong Ma (), Huiling Wang (), Xiangyang Xu (), Weizhan Han, Congwen Duan () and Zhaohui Qi ()
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Jinlong Ma: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Huiling Wang: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Xiangyang Xu: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Weizhan Han: #x2020;The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, P. R. China
Congwen Duan: #x2021;Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, P. R. China
Zhaohui Qi: #xA7;Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha 410081, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 08, 1-10

Abstract: In recent years, the transportation systems have to face the increasing challenges of congestion and inefficiency, and therefore the research on traffic dynamics of complex networks has become a significant area. When the total node capacity of the network is fixed, a reasonable queue resource reallocation strategy is effective in improving the network traffic capacity. In this paper, a new queue resource allocation method is proposed based on the betweenness centrality and the degree centrality of nodes. With the proposed strategy, the node queue length is allocated accurately to enhance the transport efficiency. Simulation results show that the proposed strategy can effectively improve the traffic capacity of the scale-free networks.

Keywords: Scale-free networks; traffic dynamics; queue resource; betweenness centrality (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0129183119500566

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