Application Of Clustering Method In Identifying Traffic Accident Patterns Using K-Means Algorithm In Padang City

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Published Oct 8, 2024
https://doi.org/10.24036/int.j.emerg.technol.eng.educ..v1i1.12
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Vol. 1 No. 1 (2024)

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Adeng Pratama
a:1:{s:5:"en_US";s:23:"Padang State University";}
Yeka Hendriyani
Universitas Negeri Padang
Khairi Budayawan
Universitas Negeri Padang
Syafrijon

Abstract

ABSTRACT


Traffic safety in Padang City has become a major concern due to the increasing number of accidents over the past few years. To identify accident patterns and accident-prone areas, this study applies a clustering method using the K-Means algorithm. Accident data from 2021 to 2023 were analyzed based on variables such as the number of accidents, the number of vehicles involved, and the number of victims. The results of the analysis show that K-Means Clustering with Chebyshev distance is able to group accident locations into three clusters based on the level of vulnerability. The first cluster includes roads that are not prone to accidents, the second cluster includes roads that are prone to accidents, and the third cluster identifies roads with the highest accident rates.

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How to Cite
Pratama, A., Hendriyani, Y., Budayawan , K., & Syafrijon. (2024). Application Of Clustering Method In Identifying Traffic Accident Patterns Using K-Means Algorithm In Padang City. International Journal of Emerging Technology and Engineering Education, 1(1), 26–32. https://doi.org/10.24036/int.j.emerg.technol.eng.educ.v1i1.12

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