Volume 26 Issue 8
Aug.  2026
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2026  >  26(8): 190-201
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HE Yin-zhang, LI Yi-lin, ZHAO Xiao-kang, ZHANG Jiu-peng, LI Yan. Prediction model for performance decay of airport runways based on CNN-xLSTM-Attention[J]. Journal of Traffic and Transportation Engineering, 2026, 26(8): 190-201. doi: 10.19818/j.cnki.1671-1637.2026.325
Citation: HE Yin-zhang, LI Yi-lin, ZHAO Xiao-kang, ZHANG Jiu-peng, LI Yan. Prediction model for performance decay of airport runways based on CNN-xLSTM-Attention[J]. Journal of Traffic and Transportation Engineering, 2026, 26(8): 190-201. doi: 10.19818/j.cnki.1671-1637.2026.325
shu

Prediction model for performance decay of airport runways based on CNN-xLSTM-Attention

doi: 10.19818/j.cnki.1671-1637.2026.325
  • 1.

    Key Laboratory of Smart Construction and Maintenance of Civil Aviation Airports, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China

Funds:

National Natural Science Foundation of China 52502429

More Information
  • Corresponding author: LI Yan, research assistant, PhD, E-mail: mhly@nwpu.edu.cn
  • Received Date: 2025-12-18
  • Accepted Date: 2026-05-26
  • Rev Recd Date: 2026-01-26
  • Publish Date: 2026-08-28
    Abstract
  • To accurately predict the performance decay trend of airport runways under complex working conditions, a combined CNN-xLSTM-Attention prediction model was established based on an improved deep learning architecture. A convolutional neural network was utilized to extract local features of multi-source input data such as environment, load, and structure, and an xLSTM model containing dual branches of sLSTM and mLSTM was introduced to enhance the capturing ability for temporal dependencies. On this basis, an Attention mechanism was combined to dynamically focus on key time steps and feature variables, and a Bayesian optimization algorithm was adopted to automatically search for key hyperparameters, thereby constructing a high-precision pavement condition index (PCI) decay prediction framework. Using measured time-series data from two runways in China, covering 6 types of key indicators such as service time, rainfall, and traffic load, the prediction accuracy and generalization ability of different models were calculated and analyzed, and SHAP and a generalized additive model were introduced to analyze the nonlinear influence mechanisms of key features. The research results indicate that compared to the traditional LSTM and single improved models, the CNN-xLSTM-Attention model has the optimal comprehensive performance, effectively solving the long-sequence gradient problem and the feature coupling challenge; the coefficient of determination (R2) of the test set reaches 0.923; the mean absolute percentage error (MAPE) is only 1.778%. The explanatory analysis reveals that service time (contribution rate of 44.8%) and annual average rainfall (contribution rate of 33.6%) are the primary factors dominating the decay of PCI; traffic load and temperature indicators show a significant threshold effect; increasing the surface course thickness has a significant inhibitory effect on delaying the decay. It is thus evident that the established pavement performance prediction model and its explanatory analysis method can accurately capture the temporal evolution patterns and key influence mechanisms of PCI, providing reliable data support for the phased scientific maintenance and operational decisions of airport runways.

     

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