Abstract
To enhance the scientific rigor and precision of urban road maintenance decision-making, this study proposes an ensemble learning-based method for predicting the asphalt pavement condition index (IPCI). Based on historical pavement inspection and maintenance data for urban roads in Beijing from 2017 to 2024, a multidimensional feature dataset encompassing pavement usage indicators, performance indicators, and environmental impact indicators was constructed. Feature selection was performed using Pearson correlation coefficients and the LASSO method. Based on these selected features, 11 widely used machine learning models were trained, and their hyperparameters were optimized. After a comprehensive comparison of predictive accuracy, the five best-performing models, including XGBoost, LightGBM, random forest, gradient boosting tree, and CatBoost, were selected as base learners to construct an ensemble prediction model using weighted average voting regression. The experimental results demonstrate that the proposed ensemble model performs well on the test set, with R² = 0.8885, ERMSE = 0.057, and EMAE = 0.0431. The model exhibits an extremely narrow range of generalization errors, demonstrating its effectiveness in handling complex nonlinear relationships. SHAP-based interpretability analysis identifies pavement age, maintenance grade, annual average daily traffic, and alligator cracking as the key factors affecting IPCI. The study confirms that this ensemble prediction method can significantly improve the accuracy and interpretability of asphalt pavement condition prediction, providing theoretical support for formulating urban road maintenance specifications, optimizing the allocation of maintenance funds, and implementing preventive maintenance systems.
Publication Date
8-16-2026
DOI
10.14048/j.issn.1671-2579.2026.04.030
First Page
279
Last Page
286
Submission Date
August 2026
Recommended Citation
Lili, Dong; Shuqing, Li; Haijun, Liu; Chen, Chang; and Chao, Feng
(2026)
"Prediction of asphalt pavement condition index for urban roads based on ensemble learning,"
Journal of China & Foreign Highway: Vol. 46:
Iss.
4, Article 30.
DOI: 10.14048/j.issn.1671-2579.2026.04.030
Available at:
https://zwgl1980.csust.edu.cn/journal/vol46/iss4/30
Reference
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