Akıllı ulaşım sistemleri için çoklu veri türlerine dayalı hibrit sürücü davranışı sınıflandırma yaklaşımı

Öz

Akıllı ulaşım sistemlerinin gelişimiyle birlikte trafik güvenliği hâlâ kritik bir sorun olarak önemini korumaktadır. Bu çalışma, literatürde ilk kez OBD-II, akıllı telefon sensörleri, GPS ve çevresel ses verilerini bütünleştiren çoklu (multimodal) bir veri kümesi kullanarak sürücü davranışlarını sakin, normal ve agresif kategorilerine ayırmaktadır. Yeni oluşturulan bu çok kaynaklı veri kümesi, motor parametreleri, hareket sensörleri, yol özellikleri ve ortam seslerini kapsamaktadır. Farklı pencere uzunluklarında bölünen verilerden istatistiksel ve zaman–frekans tabanlı öznitelikler çıkarılmış, pencere boyutunun sınıflandırma doğruluğuna etkisi analiz edilmiştir. Support vector machine (SVM), Naïve Bayes (NB) ve multilayer perceptron (MLP) modelleri karşılaştırılmış; MLP en iyi bireysel sonuçları vermiştir. Bununla birlikte, çalışmanın özgün katkısı olan hibrit alt-uzay istifleme (hybrid subspace stacking) yöntemi, rastgele alt-uzay çeşitlendirmesi ve topluluk öğrenmeyi birleştirerek tüm modelleri geride bırakmış ve %95.69 doğrulukla en yüksek başarıyı sağlamıştır.

Anahtar Kelimeler

• Sürücü davranışı
• çoklu sensör füzyonu
• OBD-II
• Overpass API
• hibrit alt-uzay istifleme

A hybrid driving behavior classification approach based on multimodal data for intelligent transportation systems

Abstract

With the advancement of intelligent transportation systems, traffic safety remains a critical concern. This study classifies driver behaviors into calm, normal, and aggressive categories by integrating, for the first time in the literature, a multimodal dataset combining OBD-II, smartphone sensors, GPS, and environmental sound data. The newly created multi-source dataset encompasses engine parameters, motion sensors, road characteristics, and ambient sounds. The data were segmented using different window lengths, and statistical as well as time–frequency features were extracted to analyze the impact of window size on classification accuracy. Support vector machine (SVM), Naïve Bayes (NB), and multilayer perceptron (MLP) models were compared, with the MLP yielding the best individual performance. However, the study’s key novelty lies in the proposed hybrid subspace stacking method, which combines random subspace diversification and ensemble learning to surpass all baseline classifiers, achieving the highest accuracy of 95.69%.

Keywords

• Driving behavior
• multimodal sensor fusion
• OBD-II
• Overpass API
• hybrid subspace stacking

Kaynakça

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