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基于改进Newman快速划分算法的城市动态交通子区划分方法

Urban dynamic traffic subarea dividing method based on improved Fast Newman algorithm
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摘要 城市交通网络紧密联系,交叉口、干线和交通子区存在复杂关联性。从复杂系统角度提出一种基于Newman快速划分算法(Fast Newman,FN)的控制子区划分方法。首先,考虑城市道路网络拓扑结构复杂性,根据相邻交叉口的交叉口间距、路段交通流量、车流离散特性、交通流速度、车流密度等分析交叉口关联性,建立综合关联度计算模型;其次,将交叉口关联性引入到FN算法中,基于改进的Newman快速划分算法对路网控制子区进行划分;最后,通过实际路网,进行模型验证。结果表明:该子区动态划分方法有效考虑路网拓扑结构复杂性,更符合实际交通流特性,对城市区域路网子区划分更加合理。 The urban traffic network is closely connected,and there are complex correlations among intersections,trunk lines and traffic subareas.From the perspective of complex system,this paper proposes a control subarea dividing method based on Fast Newman algorithm(FN).Firstly,considering the complexity of the topology of urban road network,the correlation of intersections is analyzed according to the intersection spacing,traffic flow,discrete characteristics of traffic flow,traffic flow speed and traffic flow density,and the calculation model of comprehensive correlation degree is established.Then the intersection correlation is introduced into the FN algorithm,and the road network control subarea is divided based on the improved FN algorithm.Finally,this paper verifies the model through the actual road network.The results show that the subarea dynamic dividing method effectively considers the complexity of road network topology,and therefore is more consistent with the actual traffic flow characteristics and more reasonable for the subdividing of urban road network.
作者 宋晓晨 曲大义 王浩然 戴守晨 杨玉凤 SONG Xiaochen;QU Dayi;WANG Haoran;DAI Shouchen;YANG Yufeng(School of Mechanical and Automotive Engineering,Qingdao University of Technology,Qingdao 266525,China)
出处 《青岛理工大学学报》 CAS 2023年第3期113-120,共8页 Journal of Qingdao University of Technology
基金 国家自然科学基金资助项目(51678320)。
关键词 交叉口关联性 聚类分析 子区划分 Newman快速划分算法 intersection correlation clustering analysis subarea dividing Fast Newman algorithm
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  • 1段后利,李志恒,张毅,胡坚明.交通控制子区动态划分模型[J].吉林大学学报(工学版),2009,39(S2):13-18. 被引量:12
  • 2Scott J. Social networks analysis: a handbook [ M ]. London : Sage Publications. 2000.
  • 3Kernighan B W, Lin S. A efficient heuristic procedure for partitioning graphs [ J ]. Bell System Technical Journal, 1970,49:291-307.
  • 4Pothen A, Simon H, et al. Partitioning sparse matrices with eigenvectors of graphs [ J ]. SIAM J. Matrix Anal. Appl. , 1990,11:430452.
  • 5Girvan M, Newman M E J. Community structure in social and biological networks [ J ]. Proc. Natl. Acad, 2002,99 : 7821-7826.
  • 6Wu F, Huberman B A. Finding communities in linear time: A physics approach[J]. Euro. Phys. J. B,2003, 38:331-338.
  • 7Newman M E J, Girvan M. Finding and evaluating community structure in networks [ J ]. Phys. Rev. E, 2004, 69: 026113.
  • 8Newman M E J. Fast algorithm for detecting community structure in networks[J]. Phys. Rev. E, 2004, 69:066133.
  • 9Lee J H, Lee H K. Distributed and cooperative fuzzy controllers for traffic intersections group[J]. IEEE Trans on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 1999, 29(2): 263-271.
  • 10Little J D C. The synchronization of traffic signals by mixed integer linear programming[J]. Operations Research, 1966, 14(1): 568-594.

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