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

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

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

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.

Keywords:

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

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