RAMSTN: A Residual Attention Multistage Spatial-Temporal Network for Metro Track State Estimation

Compared with high-speed trains, the operation lines of metro are more complicated and diversified. The frequent start-up and braking of metro trains will aggravate rail wear, especially in the small-radius curved section of the line, which is more likely to produce a track irregularity state. To solve this problem, this article mainly proposes a new intelligent detection method for track irregularity. First, a new residual attention multistate gate control unit (RMGCU) is presented, featuring an innovative multistate hierarchical division mechanism in memory cells to improve the feature perception ability. Second, the elite opposition-based learning (EOBL) and Gaussian mutation strategy are introduced into the traditional Osprey optimization algorithm (OOA) to improve its performance, and the improved OOA is adopted to optimize the hyperparameters of CNN and RMGCU. Finally, a novel residual attention multistage spatial-temporal network (RAMSTN) is developed by fusing a CNN for multiband spatial feature extraction and RMGCU for enhanced temporal sequence modeling with hierarchical memory gating. The proposed RAMSTN effectively captures the spatial-temporal characteristics of vehicle body acceleration, enabling accurate end-to-end detection of vertical track irregularities. Experimental results demonstrate that the RAMSTN model achieves high accuracy in identifying the track irregularity value. This study provides both theoretical insights and practical tools for metro track condition evaluation and maintenance planning.