K-Means Clustering for Identifying Traffic Accident Hotspots in Depok City
DOI:
https://doi.org/10.61098/jarcis.v2i1.182Keywords:
K-Means Clustering, Traffic Accidents, Depok City, Decision Support, Road SafetyAbstract
This study applies the K-Means clustering algorithm to support decision-making processes related to identifying traffic accident-prone areas in Depok City over a three-year period (2020-2022). Secondary data was obtained from the Traffic Accident Unit of the Depok Metro Police, encompassing monthly traffic accident recapitulations for each district. The data underwent preprocessing steps, including integration and selection of relevant attributes. Using RapidMiner, the data was clustered into three distinct groups, with the optimal number of clusters determined by the Davies-Bouldin Index (DBI), which yielded a score of 0.896, indicating a satisfactory clustering result. The findings reveal that four districts—Beji, Cimanggis, Pancoran Mas, and Sukmajaya—are identified as high-risk areas for traffic accidents. These results are expected to assist local authorities in implementing targeted safety measures. The study demonstrates that the K-Means clustering method is a viable tool for analyzing traffic accident data and can significantly contribute to improving road safety in urban areas
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F. Fatmah, V. P. Dewi, and Y. Priotomo, “Developing age-friendly city readiness: A case study from Depok city, Indonesia,” SAGE Open Med., vol. 7, p. 2050312119852510, 2019.
M. Hafizha, “Preferensi Masyarakat Dalam Memilih Apartemen Dengan Menggunakan Metode Fuzzy Analytical Hierarchy Process (F-Ahp) Di Kota Depok Population Preferences In Choosing Apartment Model Using Fuzzy Analytical Hierarchy Process (F-Ahp) Method In Depok City,” 2020.
E. Rustiadi, A. E. Pravitasari, Y. Setiawan, S. P. Mulya, D. O. Pribadi, and N. Tsutsumida, “Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm region,” Cities, vol. 111, p. 103000, 2021.
E. Macioszek and A. Granà, “The analysis of the factors influencing the severity of bicyclist injury in bicyclist-vehicle crashes,” Sustainability, vol. 14, no. 1, p. 215, 2021.
O. Tengilimoglu, O. Carsten, and Z. Wadud, “Implications of automated vehicles for physical road environment: A comprehensive review,” Transp. Res. part E Logist. Transp. Rev., vol. 169, p. 102989, 2023.
M. R. F. Amrozi and R. P. Isheka, “Optimizing the functional performance of road network using vulnerability assessment to cope with unforeseen road incidents,” in Journal of the civil engineering forum, 2022, vol. 8, no. 1, pp. 67–80.
M. Isradi, “The impact of Covid-19 on areas prone to traffic accidents in Depok City: Margonda Raya Road case study,” in Journal of World Conference (JWC), 2021, vol. 3, no. 2, pp. 241–251.
A. Supriadi and T. Oswari, “Analysis of Geographical Information System (GIS) design aplication in the Fire Department of Depok City,” Tech. Soc. Sci. J., vol. 8, p. 1, 2020.
M. Thibenda, D. M. P. Wedagama, and D. Dissanayake, “Drivers’ attitudes to road safety in the South East Asian cities of Jakarta and Hanoi: Socio-economic and demographic characterisation by Multiple Correspondence Analysis,” Saf. Sci., vol. 155, p. 105869, 2022.
T. Tjahjono, B. Swantika, A. Kusuma, R. Purnomo, and G. H. Tambun, “Determinant contributing variables to severity levels of pedestrian crossed the road crashes in three cities in Indonesia,” Traffic Inj. Prev., vol. 22, no. 4, pp. 318–323, 2021.
W. H. Organization, Pedestrian safety: a road safety manual for decision-makers and practitioners. World Health Organization, 2023.
A. Z. Siregar, M. Awaluddin, and Y. Wahyuddin, “Identification of Traffic Accidents Vulnerability Level Using Kernel Density And K-Medoids Methods (Case Study: Depok and Kalasan Districts, Sleman Regency),” J. Ilm. Geomatika, vol. 3, no. 1, pp. 23–35, 2023.
A. Sulhi, “Data mining technology used in an Internet of Things-based decision support system for information processing intelligent manufacturing,” Int. J. Informatics Inf. Syst., vol. 4, no. 3, pp. 168–179, 2021.
G. Zhang, Y. Li, and X. Deng, “K-means clustering-based electrical equipment identification for smart building application,” Information, vol. 11, no. 1, p. 27, 2020.
B. Lund and J. Ma, “A review of cluster analysis techniques and their uses in library and information science research: k-means and k-medoids clustering,” Perform. Meas. Metrics, vol. 22, no. 3, pp. 161–173, 2021.
C. Wu, F. Zhou, J. Ren, X. Li, Y. Jiang, and S. Ma, “A selective review of multi-level omics data integration using variable selection,” High-throughput, vol. 8, no. 1, p. 4, 2019.
N. Naheed, M. Shaheen, S. A. Khan, M. Alawairdhi, and M. A. Khan, “Importance of features selection, attributes selection, challenges and future directions for medical imaging data: a review,” Comput. Model. Eng. Sci., vol. 125, no. 1, pp. 314–344, 2020.
A. Zimmermann, “Method evaluation, parameterization, and result validation in unsupervised data mining: A critical survey,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 10, no. 2, p. e1330, 2020.
R. Rahim, J. T. Santoso, S. Jumini, G. Bhawika, D. Susilo, and D. Wibowo, “Unsupervised data mining technique for clustering library in Indonesia,” Libr. Philos. Pract., vol. 4866, 2021.
A. K. Singh, S. Mittal, P. Malhotra, and Y. V. Srivastava, “Clustering evaluation by davies-bouldin index (dbi) in cereal data using k-means,” in 2020 Fourth international conference on computing methodologies and communication (ICCMC), 2020, pp. 306–310.
F. Ros, R. Riad, and S. Guillaume, “PDBI: A partitioning Davies-Bouldin index for clustering evaluation,” Neurocomputing, vol. 528, pp. 178–199, 2023.
L. Zappia and A. Oshlack, “Clustering trees: a visualization for evaluating clusterings at multiple resolutions,” Gigascience, vol. 7, no. 7, p. giy083, 2018.
L. Zhao et al., “K‐means cluster analysis of characteristic patterns of allergen in different ages: Real life study,” Clin. Transl. Allergy, vol. 13, no. 7, p. e12281, 2023.
M. Rezaei, I. Cribben, and M. Samorani, “A clustering-based feature selection method for automatically generated relational attributes,” Ann. Oper. Res., vol. 303, no. 1, pp. 233–263, 2021.
M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means algorithm: A comprehensive survey and performance evaluation,” Electronics, vol. 9, no. 8, p. 1295, 2020.
A. Maghawry, R. Hodhod, Y. Omar, and M. Kholief, “An Approach to Optimize Multi-objective Problems Using Hybrid Genetic Algorithms Supported by Initial Centroid Selection Optimization Enhanced K-Means Based Selection Operator,” in Artificial Intelligence in Intelligent Systems: Proceedings of 10th Computer Science On-line Conference 2021, Vol. 2, 2021, pp. 64–87.
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