Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3578-3583

Road network classification method based on improved density peak clustering

Yang Di1,2
Xu Wenyu1,2
Wang Peng1,2
1. College of Computer Science & Technology, Changchun University of Science & Technology, Changchun 130012, China
2. Jilin Joint Key Laboratory of Big Data Science & Engineering, Changchun 130012, China
DOI: 10.19734/j.issn.1001-3695.2023.04.0149

Abstract

The reasonable classification of urban road network is of great significance for the optimization of regional traffic control and the implementation of coordination strategies. In order to improve road traffic efficiency, this paper proposed an urban road network classification method based on density peak clustering algorithm. Firstly, it comprehensively considered the influence of static and dynamic factors at intersections to construct a correlation degree model for adjacent intersections, and provided a quantitative description for reasonably quantifying the correlation degree between intersections. Secondly, it proposed an improved density peak clustering algorithm that combined the correlation between adjacent intersections to partition the road network area. To address the problem that the local density in the density peak clustering algorithm varies greatly on different size data sets, it introduced the idea of KNN to re-describe the local density, and secondly, in order to avoid the subjectivity of the manual selection of the algorithm clustering center which could lead to error problem, using the elbow rule to realize the automatic selection of the clustering center. The experimental results show that compared with the improved Newman algorithm and Ncut algorithm, the improved proposed algorithm can reduce 12.5% and 22.8% respectively in optimizing the flat homogeneity of sub-regions, improve the effect of control sub-region division and make the region division effect more reasonable.

Foundation Support

吉林省教育厅科学研究项目(JJKH20230848KJ)
吉林省科技创新平台建设项目(YDZJ202302CXJD027)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.04.0149
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Algorithm Research & Explore
Pages: 3578-3583
Serial Number: 1001-3695(2023)12-009-3578-06

Publish History

[2023-06-26] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

杨迪, 徐文瑜, 王鹏. 基于改进密度峰值聚类的路网划分方法 [J]. 计算机应用研究, 2023, 40 (12): 3578-3583. (Yang Di, Xu Wenyu, Wang Peng. Road network classification method based on improved density peak clustering [J]. Application Research of Computers, 2023, 40 (12): 3578-3583. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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