Considering traditional methods’ low accuracy and machine learning methods’ lack of interpretability, this paper proposed a pavement performance model for IRI prediction based on XGBoost, and introduced SHAP to enhance the interpretability of individual features of the model. The data used are from the America LTPP data. Firstly, data cleaning and preprocessing were conducted. Secondly, four prediction models were built based on classical algorithms, namely, LightGBM, XGBoost, SVM, and multiple linear regression. Then, by comparison, it was found that XGBoost performed better. Finally, parameter tuning for this model was performed, with the RMSE as 0.317, MAE as 0.219, and R2 as 0.742. In addition, considering the prediction model’s lack of transparency, SHAP is utilized to perform the feature importance analysis and identify the main factors affecting the pavement performance, which can help the highway sector to improve the reliability of their subsequent prediction model analysis.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.