Complex Traffic Network Analysis Method Based on a Multiscale Aggregation Model. (25th March 2022)

• Record Type: Journal Article
• Authors: Pei, Yingying; Zhu, Xia; Li, Guohong; Jin, Yongtao; Liu, Yuyan; Liu, Yuanping; Liu, Gang; Fan, Jiangxia
• Other Names: Yin Aijun Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-03-25
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/7311117
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Identifying the aggregation characteristics of the geospatial network is an important aspect of analyzing highway traffic networks. Based on the complex network theory, this paper studies the block aggregation characteristics of the highway traffic network and proposes an Improved PageRank Spectral Clustering (IPSC) Algorithm to divide the functional blocks of highway traffic networks. Firstly, the theoretical model of a highway traffic network is constructed by adding location attribute weight, geographical distance weight, road grade weight, and dynamic traffic congestion weight. Secondly, the improved PageRank algorithm is used to get the ranking of key nodes of the highway traffic network. The clustering center and the number of clusters are determined by the ranking of key nodes and the shortest path distance. Then the improved spectral clustering algorithm is used to divide the functional blocks of the highway transportation network and identify the special common blocks of the highway transportation network. Finally, the IPSC Algorithm is used to analyze the aggregation mode of complex traffic networks at city and district scales. Crossing the limit of administrative division, the division results of special common blocks of highway transportation networks are obtained. Maintaining the connectivity between blocks can improve the overall efficiency of highway transportation networks.