Predictive Modeling of Urban Traffic Accident Severity in Türkiye's Centennial: Machine Learning Approaches for Sustainable Cities

Yıl 2023, Cilt: 16 Sayı: Türkiye Cumhuriyetinin 100. Yılı Özel Sayısı | Special Issue for the 100th Anniversary of the Republic of Türkiye, 395 - 406, 29.10.2023

Adem Korkmaz

https://doi.org/10.35674/kent.1353402

Öz

With their far-reaching implications for public health, urban development, and societal harmony, traffic accidents remain a global challenge. As the Republic of Türkiye marks its 100th year, predicting traffic accident severity assumes critical significance, aligning with the nation's aspirations for urban renewal and sustainable progress. This research harnesses the capabilities of machine learning (ML) to anticipate accident severities, shedding light on the critical roles of specific driver and vehicle characteristics. In-depth evaluation of various ML techniques—spanning from Random Forest (RF) and Gaussian Naive Bayes to k-NN, CatBoostClassifier, LightGBM, and Decision Trees—was undertaken, drawing on an expansive dataset that mirrors a spectrum of traffic situations. The RF algorithm demonstrated superior predictive prowess, with certain variables such as Engine_Capacity_(CC), Age_of_Driver, Age_of_Vehicle, Day_of_Week, and Vehicle_Type emerging as decisive factors in accident outcomes. Beyond highlighting RF's potential in accident severity prediction, the study emphasizes the significance of critical determinants. These insights offer a roadmap for stakeholders to craft specialized interventions, amplify public awareness efforts, and pioneer infrastructural upgrades, culminating in a vision of enhanced road safety. Furthermore, this investigation charts a course for Türkiye to foster a sustainable urban trajectory through informed urban and traffic planning initiatives.

Anahtar Kelimeler

Traffic Accidents, Severity Prediction, Machine Learning, Feature Importance, Driver Attributes, Road Safety

Türkiye'nin 100. Yılında Kentsel Trafik Kazası Şiddetinin Öngörü Modellemesi: Sürdürülebilir Şehirler İçin Makine Öğrenimi Yaklaşımları

Öz

Halk sağlığı, kentsel gelişim ve toplumsal uyum açısından geniş kapsamlı sonuçlarıyla trafik kazaları küresel bir sorun olmaya devam ediyor. Türkiye Cumhuriyeti'nin 100. yılını kutlarken, trafik kazası şiddetini tahmin etmek, ulusal kentsel yenileme ve sürdürülebilir ilerleme hedefleriyle uyumlu kritik bir öneme sahiptir. Bu araştırma, kazaların şiddetini önceden tahmin etmek için makine öğrenimi yeteneklerini kullanıyor ve belirli sürücü ve araç özelliklerinin kritik rollerini vurgulamaktadır. Random Forest (RF) ve Gaussian Naive Bayes'ten k-NN, CatBoostClassifier, LightGBM ve Decision Trees'a kadar çeşitli ML tekniklerinin derinlemesine değerlendirilmesi yapılmıştır; bu, bir dizi trafik durumunu yansıtan geniş bir veri kümesiyle gerçekleştirilmiştir. RF algoritması, Engine_Capacity_(CC), Age_of_Driver, Age_of_Vehicle, Day_of_Week ve Vehicle_Type gibi belirli değişkenlerin kaza sonuçlarındaki belirleyici faktörler olarak öne çıktığı üstün öngörü yeteneğiyle dikkat çekmektedir. Kazaların şiddetini tahmin etmede RF'nin potansiyelini vurgulamanın ötesinde, çalışma kritik belirleyicilerin önemini vurgulamaktadır. Bu içgörüler, paydaşların özelleştirilmiş müdahaleler tasarlamaları, kamuoyu farkındalık çalışmalarını güçlendirmeleri ve altyapıyı güncellemeleri için bir yol haritası sunar; bu, gelişmiş yol güvenliği vizyonuyla sonuçlanır. Ayrıca, bu araştırma, Türkiye'nin bilgilendirilmiş kentsel ve trafik planlama girişimleri aracılığıyla sürdürülebilir bir kentsel yol çizmesi için bir yol haritası sunar.

Anahtar Kelimeler

Trafik Kazaları, Şiddet Tahmini, Makine Öğrenimi, Özellik Önemi, Sürücü Özellikleri, Yol Güvenliği

Kaynakça

• Al Mamlook, R. E., Ali, A., Hasan, R. A., & Kazim, H. A. M. (2019, July). Machine learning to predict the freeway traffic accidents-based driving simulation. In 2019 IEEE National Aerospace and Electronics Conference (NAECON) (pp. 630–634). IEEE.
• Alkheder, S., AlRukaibi, F., & Aiash, A. (2020). Risk analysis of traffic accidents’ severities: An application of three data mining models. ISA transactions, 106, 213-220.
• Avşar, Y. Ö., Yıldırım, Z. B., & Pelin Çalışkanelli, S. (2023). Yol Tasarım ve İşletme Sorunlarının Trafik Kazaları Üzerindeki Etkisinin İncelenmesi: Buca Koop. Mahallesi Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 275-288.
• Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274-279.
• Bokaba, T., Doorsamy, W., & Paul, B. S. (2022). Comparative study of machine learning classifiers for modeling road traffic accidents. Applied Sciences, 12(2), 828.
• Chen, M. M., & Chen, M. C. (2020). Modeling road accident severity with comparisons of logistic regression, decision tree, and random forest. Information, 11(5), 270.
• Data, (2023). Road Safety Data. Url: https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data Access: 12.10.2023
• Elvik, R. (2013). Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accident Analysis & Prevention, 60, 254-267.
• Elvik, R. (2019). Handbook of road safety measures. Emerald Publishing Limited.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• Hastie, T., Tibshirani, R., & Friedman, J. (2013). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
• Huang, H., Chin, H. C., & Haque, M. M. (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis. Accident Analysis & Prevention, 40(1), 45-54.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: Springer.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
• Kaggle, (2023). UK Traffic Accidents, Url: https://www.kaggle.com/code/ambaniverma/uk-traffic-accidents/notebook Access: 25.08.2023
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). LightGBM: A highly efficient gradient-boosting decision tree. In Advances in Neural Information Processing Systems (pp. 3146–3154).
• Kumar, N., Acharya, D., & Lohani, D. (2020). An IoT-based vehicle accident detection and classification system using sensor fusion. IEEE Internet of Things Journal, 8(2), 869–880.
• Kumeda, B., Zhang, F., Zhou, F., Hussain, S., Almasri, A., & Assefa, M. (2019, June). Classification of road traffic accident data using machine learning algorithms. In 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN) (pp. 682–687). IEEE.
• Labib, M. F., Rifat, A. S., Hossain, M. M., Das, A. K., & Nawrine, F. (2019, June). Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE.
• Li, H., Jiang, H., Wang, D., & Han, B. (2018, July). An improved KNN algorithm for text classification. In 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC) (pp. 1081–1085). IEEE.
• Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
• Mitchell, T. M. (1997). Machine Learning. McGraw Hill.
• Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
• Özkan, T., & Lajunen, T. (2005). A new addition to DBQ: Positive Driver Behaviors scale. Transportation Research Part F: Traffic Psychology and Behaviour, 8(4-5), 355-368.
• Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (pp. 6639-6649).
• Qu, Y., Lin, Z., Li, H., & Zhang, X. (2019). Feature recognition of urban road traffic accidents based on GA-XGBoost in the context of big data. IEEE Access, p. 7, 170106–170115.
• Sangare, M., Gupta, S., Bouzefrane, S., Banerjee, S., & Muhlethaler, P. (2021). Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning—Expert Systems with Applications, 167, 113855.
• TUIK, (2022). Karayolu Trafik Kaza İstatistikleri, 2022. Url: https://data.tuik.gov.tr/Bulten/Index?p=Karayolu-Trafik-Kaza-Istatistikleri-2022-49513 Access:31.08.2023
• World Health Organization. (2018). Global status report on road safety 2018. World Health Organization.
• Yassin, S. S., & Pooja. (2020). Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach. SN Applied Sciences, 2, 1-13.
• Zhang, H. (2004). The optimality of Naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami Beach, Florida, pp. 562–567.