Explainable Boosting Machine-Based Analysis of Delay at Signalized Intersections

Abstract

Urban traffic signal control plays an important role in improving operational efficiency and reducing unnecessary delays in transportation networks. However, capturing the complex nonlinear relationships between traffic demand and signal timing parameters remains a major challenge under varying operating conditions. This study proposes an explainable artificial intelligence (XAI) framework for traffic signal delay analysis using scenario-driven operational data from a three-leg signalized intersection. An Explainable Boosting Machine (EBM) was developed to model the relationships between cycle time, phase split, arrival flow, and intersection delay, and was comparatively evaluated against Random Forest (RF) and XGBoost models using 5-fold cross-validation. The EBM model achieved high predictive performance (R2 = 0.89, RMSE = 1.94), while simultaneously providing transparent interpretation of variable effects through global importance analysis, shape functions, and pairwise interactions. The results indicate that cycle time and phase allocation are among the key factors influencing delay formation, with nonlinear interaction patterns observed under different traffic demand conditions. An interaction was observed between cycle length and Phase C allocation (north approach turning phase), suggesting a condition-dependent relationship with delay. Comparative analyses further demonstrated a general consistency between EBM and benchmark ensemble models in identifying influential operational variables. The proposed framework provides predictive capability and interpretable insights that may support data-driven traffic signal analysis and operational decision-making.

Keywords

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

Supporting Institution

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

Ethical Statement

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

Thanks

This article does not include an acknowledgements section.

Sinyalize Kavşaklarda Gecikmenin Açıklanabilir Tabanlı (EBM) Yöntem ile Analizi

Abstract

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.

Supporting Institution

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

Ethical Statement

Hazırlanan makalede etik kurul izni alınmasına gerek yoktur.

Thanks

Hazırlanan makalede herhangi bir teşekkür kısmı bulunmamaktadır.

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