Identification of an algorithm for the analysis and study of urban road network trajectories

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.298274

Keywords:

congestion point, grid K-means clustering algorithm, trajectory clustering, parallel computing

Abstract

The object of this study is a clustering algorithm using various technologies.

This paper compares clustering algorithms that are more commonly used to analyze urban road network trajectories, the growth curve model, with the elbow method and the x-means algorithm. Experiments were conducted with various volumes of big data to determine calculation time, accuracy, and ways to increase calculation time. These results can be used to manage traffic jams in congested areas and city streets. Considering the widespread use of clustering algorithms for solving various problems, this study proposes to introduce GCM, SPGK methods for monitoring and analyzing the state of congestion on city roads. The work was carried out in the following steps: research and selection of methods based on efficiency and time, implementation of parallel computing technologies to improve computation speed, demonstration of the selected method based on collected data from a real city with visualization of the results. The growth curve model algorithm has been proven to be almost 5 times more effective than the elbow method and the x-means algorithm. The time allocated for data processing has been calculated. An increase in the volume of processed data showed an almost stable execution time t = 3 s for the GCM algorithm for data with a volume of up to almost 2,000 units. The effectiveness of SPGK-means was shown for different values of the number of points. Models of the Chengdu transport network obtained using a clustering algorithm with maximum grid density of neighborhoods are presented. There are some deviations between the grid and the road network due to the large grid size. This error is explained by an error of up to one between the points and the real grid.

The results obtained clearly show how optimization of congested roads can be influenced. They provide information to obtain data on available routes, which allows you to analyze the road network individually and as a whole

Author Biographies

Lyazat Naizabayeva, International IT University

Doctor of Technical Sciences, Professor

Department of Computer Science

Gulzat Turken, Al-Farabi Kazakh National University

Department of Computer Science

Zukhra Abdiakhmetova, Al-Farabi Kazakh National University

PhD

Department of Computer Science

Zhanerke Temirbekova, Al-Farabi Kazakh National University

PhD

Department of Computer Science

Maxatbek Satymbekov, Al-Farabi Kazakh National University

Acting Associate Professor

Department of Computer Science

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Identification of an algorithm for the analysis and study of urban road network trajectories

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Published

2024-04-30

How to Cite

Naizabayeva, L., Turken, G., Abdiakhmetova, Z., Temirbekova, Z., & Satymbekov, M. (2024). Identification of an algorithm for the analysis and study of urban road network trajectories. Eastern-European Journal of Enterprise Technologies, 2(3 (128), 14–27. https://doi.org/10.15587/1729-4061.2024.298274

Issue

Vol. 2 No. 3 (128) (2024): Control processes

Section

Control processes

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Copyright (c) 2024 Lyazat Naizabayeva, Gulzat Turken, Zukhra Abdiakhmetova, Zhanerke Temirbekova, Maxatbek Satymbekov

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