Prediksi Klasifikasi Kecelakaan Lalu Lintas di Kota Surakarta dengan Menggunakan Metode Regresi Logistik Multinomial
DOI:
https://doi.org/10.47134/scbmej.v1i4.3159Keywords:
Keselamatan Transportasi, Klasifikasi Kecelakaan Lalu Lintas, Machine Learning, Regresi Logistik MultinomialAbstract
Kecelakaan lalu lintas adalah sebuah permasalahan dalam bidang transportasi yang membutuhkan perhatian khusus. Untuk menurunkan frekuensi kecelakaan, khususnya di Kota Surakarta yang menjadi fokus penelitian ini, sangat penting untuk mengkaji klasifikasi kecelakaan lalu lintas. Tujuan dari penelitian ini adalah untuk membuat model klasifikasi kecelakaan di Kota Surakarta dengan menggunakan regresi logistik multinomial, sebuah metode dalam machine learning. Variabel yang digunakan dalam penelitian ini adalah klasifikasi kecelakaan, tipe tabrakan, kondisi cahaya, cuaca, fungsi jalan, kelas jalan, kondisi geometrik jalan, kondisi permukaan jalan, dan kemiringan jalan. Data kecelakaan yang digunakan dalam penelitian ini mencakup tahun 2018 hingga 2022. Berdasarkan temuan dari penelitian ini, terdapat enam variabel yang secara signifikan memengaruhi peningkatan klasifikasi kecelakaan menjadi kecelakaan berat dan empat variabel yang secara signifikan memengaruhi peningkatan klasifikasi kecelakaan menjadi kecelakaan sedang. Meskipun akurasi model mencapai 72%, model yang dihasilkan masih perlu ditingkatkan dalam hal klasifikasi pada kelas minoritas. Penelitian di masa depan diharapkan dapat menangani data yang tidak seimbang sebagaimana yang terjadi dalam penelitian ini agar dapat memperbaiki kinerja model yang dihasilkan.
References
Abdulhafedh, A. (2017). Incorporating the Multinomial Logistic Regression in Vehicle Crash Severity Modeling: A Detailed Overview. Journal of Transportation Technologies, 07(03), 279–303. https://doi.org/10.4236/jtts.2017.73019 DOI: https://doi.org/10.4236/jtts.2017.73019
Abdel-Salam, R. (2022). RIECNN: real-time image enhanced CNN for traffic sign recognition. Neural Computing and Applications, 34(8), 6085–6096. https://doi.org/10.1007/s00521-021-06762-5 DOI: https://doi.org/10.1007/s00521-021-06762-5
Aljasim, M. (2022). E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model. Sensors, 22(5). https://doi.org/10.3390/s22051858 DOI: https://doi.org/10.3390/s22051858
Al Mamlook, R. E., Kwayu, K. M., Alkasisbeh, M. R., & Frefer, A. A. (2019). Comparison of Machine Learning Algorithms for Predicting Traffic Accident Severity. IEEE Jordan International Conference on Electrical Engineering and Information Technology (JEEIT), 272–276. DOI: https://doi.org/10.1109/JEEIT.2019.8717393
Astarita, V. (2023). Developing new hybrid grey wolf optimization-based artificial neural network for predicting road crash severity. Transportation Engineering, 12. https://doi.org/10.1016/j.treng.2023.100164 DOI: https://doi.org/10.1016/j.treng.2023.100164
Attenberg, J., Batuwita, R., Chawla, N. V., Chen. Sheng, Ertekin, Ş., Japkowicz, N., He, H., Hoens, T. R., Liu, X.-Y., Palade, V., Weiss, G. M., & Zhou, Z.-H. (2013). Imbalanced Learning. In H. He & Y. Ma (Eds.), Imbalanced Learning. Wiley-IEEE Press. https://doi.org/10.1002/9781118646106 DOI: https://doi.org/10.1002/9781118646106
Chang, L. Y., & Wang, H. W. (2006). Analysis of Traffic Injury Severity: An Application of Non-parametric Classification Tree Techniques. Accident Analysis and Prevention, 38(5), 1019–1027. https://doi.org/10.1016/j.aap.2006.04.009 DOI: https://doi.org/10.1016/j.aap.2006.04.009
Data Kecelakaan Surakarta (2018-2022). (2023). Polres Kota Surakarta.
Dong, S. (2022). Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations. International Journal of Environmental Research and Public Health, 19(5). https://doi.org/10.3390/ijerph19052925 DOI: https://doi.org/10.3390/ijerph19052925
Hasanin, T., Khoshgoftaar, T. M., Leevy, J. L., & Bauder, R. A. (2019). Severely imbalanced Big Data challenges: investigating data sampling approaches. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0274-4 DOI: https://doi.org/10.1186/s40537-019-0274-4
Hussein, N. H. (2022). A Comprehensive Survey on Vehicular Networking: Communications, Applications, Challenges, and Upcoming Research Directions. IEEE Access, 10, 86127–86180. https://doi.org/10.1109/ACCESS.2022.3198656 DOI: https://doi.org/10.1109/ACCESS.2022.3198656
Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression (2nd ed.). John Wiley & Sons. DOI: https://doi.org/10.1002/0471722146
Jumlah Kecelakaan, Korban Mati, Luka Berat, Luka Ringan, dan Kerugian Materi, 2022. (2022). Badan Pusat Statistik. https://www.bps.go.id/id/statistics-table/2/NTEzIzI=/jumlah-kecelakaan--korban-mati--luka-berat--luka-ringan--dan-kerugian-materi.html
Kadri, N. (2023). New LSTM Deep Learning Algorithm for Driving Behavior Classification. Cybernetics and Systems, 54(4), 387–405. https://doi.org/10.1080/01969722.2022.2059133 DOI: https://doi.org/10.1080/01969722.2022.2059133
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-6849-3 DOI: https://doi.org/10.1007/978-1-4614-6849-3
Marino, M. (2023). New frontiers in the risk assessment of ship collision. Ocean Engineering, 274. https://doi.org/10.1016/j.oceaneng.2023.113999 DOI: https://doi.org/10.1016/j.oceaneng.2023.113999
Road Traffic Injuries. (2023, December 13). World Health Organization. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
Sammut, C., & Webb, G. I. (2011). Encyclopedia of Machine Learning. Spinger. https://doi.org/10.1007/978-0-387-30164-8 DOI: https://doi.org/10.1007/978-0-387-30164-8
Sattar, K. (2023). Transparent deep machine learning framework for predicting traffic crash severity. Neural Computing and Applications, 35(2), 1535–1547. https://doi.org/10.1007/s00521-022-07769-2 DOI: https://doi.org/10.1007/s00521-022-07769-2
Sharma, A. (2022). Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach. IEEE Open Journal of Vehicular Technology, 3, 1–14. https://doi.org/10.1109/OJVT.2021.3138354 DOI: https://doi.org/10.1109/OJVT.2021.3138354
Shiran, G., Imaninasab, R., & Khayamim, R. (2021). Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques and Artificial Neural Network: A Modeling Comparison. Sustainability (Switzerland), 13(10), 1–23. https://doi.org/10.3390/su13105670 DOI: https://doi.org/10.3390/su13105670
Sumathi, S., Rajappa, S. V, Kumar, L. A., & Paneerselvam, S. (2022). Machine Learning for Decision Sciences with Case Studies in Python (1st ed.). CRC Press. DOI: https://doi.org/10.1201/9781003258803-1
Wongvorachan, T., He, S., & Bulut, O. (2023). A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining. Information (Switzerland), 14(1). https://doi.org/10.3390/info14010054 DOI: https://doi.org/10.3390/info14010054
Yang, K. (2022). Classification and Evaluation of Driving Behavior Safety Levels: A Driving Simulation Study. IEEE Open Journal of Intelligent Transportation Systems, 3, 111–125. https://doi.org/10.1109/OJITS.2022.3149474 DOI: https://doi.org/10.1109/OJITS.2022.3149474
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Copyright (c) 2024 Brilian Lucky Fauzan, Tuti Agustin, Amirotul Musthofiah Hidayah Mahmudah
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