Explainable Boosting Machine-Based Analysis of Delay at Signalized Intersections

Öz

Urban traffic signal control plays an important role in improving system efficiency and reducing unnecessary delays. As traffic conditions become increasingly variable, capturing the complex interactions between demand and signal timing parameters becomes increasingly difficult. This study introduces an explainable artificial intelligence (XAI) framework for traffic signal analysis based on scenario-driven operational data. Using an Explainable Boosting Machine (EBM), the proposed model captures the relationships between cycle time, green phase allocation, arrival flows, degree of saturation, and intersection delay. Beyond predictive accuracy, the model provides transparent explanations of how each parameter contributes to delay formation through global feature importance and shape functions. The findings show that explainable AI models can simultaneously deliver high predictive accuracy and interpretable insights, supporting data-driven signal control strategies that contribute to efficient and sustainable urban mobility.

Anahtar Kelimeler

• signalized intersection
• control delay
• explainable boosting machine
• data-driven modeling
• predictive modeling

Destekleyen Kurum

There is no conflict of interest with any person / institution in the article prepared.

Etik Beyan

There is no need to obtain permission from the ethics committee for the article prepared.

Teşekkür

This article does not include an acknowledgements section.

Sinyalize Kavşaklarda Gecikmenin Açıklanabilir Tabanlı (EBM) Yöntem ile Analizi

Öz

Kentsel trafik sinyal sitemlerinin kontrolü, sistem verimliliğinin artırılmasında ve gereksiz gecikmelerin azaltılmasında önemli bir rol oynamaktadır. Trafik koşullarının giderek daha değişken hale gelmesiyle birlikte, talep ile sinyal zamanlama parametreleri arasındaki karmaşık etkileşimlerin yakalanması da zorlaşmaktadır. Bu çalışma, senaryo temelli işletim verilerine dayanan, trafik analizine yönelik açıklanabilir yapay zekâ (XAI) tabanlı bir çerçeve sunmaktadır. Explainable Boosting Machine (EBM) kullanılarak geliştirilen önerilen model; çevrim süresi, yeşil faz , geliş akımları, doygunluk derecesi ve kavşak gecikmesi arasındaki ilişkileri ortaya koymaktadır. Tahmin doğruluğunun yanı sıra, model özellik önemi ve şekil fonksiyonları aracılığıyla her bir parametrenin gecikme oluşumuna nasıl katkı sağladığını açıklamaktadır. Elde edilen bulgular, açıklanabilir yapay zekâ modellerinin yüksek tahmin doğruluğunu yorumlanabilir biçimde sunabildiğini ve veriye dayalı sinyal kontrol stratejilerinin geliştirilmesini destekleyerek etkin ve sürdürülebilir kentsel hareketliliğe katkı sağladığını göstermektedir.

Anahtar Kelimeler

• sinyalize kavşak
• gecikme analizi
• makine öğrenmesi
• veriye dayalı modelleme
• tahminleyici modelleme

Destekleyen Kurum

Hazırlanan makalede herhangi bir kişi/kurum ile çıkar çatışması bulunmamaktadır.

Etik Beyan

Hazırlanan makalede etik kurul izni alınmasına gerek yoktur.

Teşekkür

Hazırlanan makalede herhangi bir teşekkür kısmı bulunmamaktadır.

Kaynakça

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