Self-Paced Ensemble-SHAP Approach for the Classification and Interpretation of Crash Severity in Work Zone Areas

Roksana Asadi (), Afaq Khattak, Hossein Vashani, Hamad R. Almujibah, Helia Rabie, Seyedamirhossein Asadi and Branislav Dimitrijevic
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Roksana Asadi: Department of Civil and Environmental Engineering New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA
Afaq Khattak: The Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
Hossein Vashani: Rutgers Business School, Rutgers University, Newark, NJ 07102, USA
Hamad R. Almujibah: Department of Civil Engineering, College of Engineering, Taif University, Taif City 21974, Saudi Arabia
Helia Rabie: Department of Economics, The Graduate Center, City University of New York, New York, NY 10016, USA
Seyedamirhossein Asadi: Department of Civil Engineering, K.N. Toosi University of Technology, Tehran 15433-19967, Iran
Branislav Dimitrijevic: Department of Civil and Environmental Engineering New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA

Sustainability, 2023, vol. 15, issue 11, 1-23

Abstract: The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting.

Keywords: work zones crashes; machine learning; self-paced ensemble; Shapley additive explanations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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