Efficient Track Irregularity Prediction Using TimeGAN and BLS-SVR

Monitoring track irregularity is vital for ensuring the safety and comfort of railway transportation. Accurate predictions of track irregularities allow infrastructure managers (IMs) to develop cost-effective maintenance strategies. This paper presents a novel track irregularity prediction method to tackle the challenges of nonlinearity, randomness, and data scarcity in irregularity datasets. The proposed approach enhances data fidelity by leveraging an improved time-series generative adversarial network (TimeGAN) integrated with a stationary gamma process (SGP) and statistical feature constraints to capture temporal dependencies and distributional patterns. Subsequently, Bayesian least-squares support vector regression (BLS-SVR) is employed to predict track irregularity changes over time using a multistep prediction method based on the Monte Carlo algorithm. Experimental results show that the proposed method significantly outperforms minimum-description-length-based rail track deterioration adaptive segmentation framework (MDL-RTDAS), support vector regression (LS-SVR), linear regression (LR), convolutional long short-term memory (ConvLSTM), and temporal convolutional network-long short-term memory (TCNLSTM), achieving average root-mean-square error (RMSE) reductions of 39.03%, 21.46%, 48.24%, 27.36%, and 25.95%, respectively. The corresponding R2 values increased by 42.26%, 14.31%, 47.98%, 9.58%, and 7.60%, respectively. In terms of mean absolute error (MAE), the reductions are 46.76%, 37.77%, 22.04%, 27.24%, and 25.30%, respectively. For mean absolute percentage error (MAPE), the improvement reached 45.22%, 37.94%, 26.50%, 28.42%, and 23.45%, respectively.