Speed Adaptability Assessment of Railway Balise Transmission Module Using a Deep-Adaptive-Attention-Based Encoder-Decoder Network

The accurate forecast of the number of effective telegram frames (NETF) received by the balise transmission module (BTM) at different speeds is the key to assessing its speed adaptability. However, the NETF index has a complex correlation with multiple BTM transmission parameters and exhibits highly nonlinear variations in the speed dimension, which poses a challenge to the NETF series prediction. Thus, a deep-adaptive-attention-based model is proposed in this article, which combines two enhanced attention mechanisms into the long short-term memory encoder-decoder network to thoroughly explore the spatial-speed relationship between the historical multivariate series and the future NETF series. Specifically, the proposed model consists of an encoder with a dual-phase spatial-attention mechanism and a decoder with an adaptive speed-attention mechanism, which can jointly capture the stable spatial correlation and relevant long-term speed-dependent information of the input multivariate series across all speed steps for the robust and accurate prediction of the NETF series. Experimental results on real-world BTM datasets demonstrate that the proposed model can predict the NETF series with the lowest local predicted error and highest overall prediction accuracy, compared with other state-of-the-art attention-based methods.