A hybrid SNN-STLSTM method for human error assessment in the high-speed railway system

Abstract

Over the years, many state-of-the-art technologies have reinforced safety in the high-speed railway driving process. However, the accident rate has not dropped significantly with the advanced technology onboard. The leading cause of this phenomenon is adverse human performance. The crucial aspects contributing to human performance are performance shaping factors (PSFs). The existing expert judgment techniques evaluate human error probabilities depending on the descriptions of PSFs’ influences on human performance. However, they cannot perform dynamic human error assessment with the real-time fluctuations of PSF interventions. To overcome this deficiency, we propose a deep learning-based method considering the instantaneous effect and time-dependent influence of PSF interventions on human performance and matching different neural network characteristics to human cognitive processes. The proposed SNN-STLSTM method combines the spiking neural network (SNN) auto-encoder and the sparse-temporal long short-term memory (STLSTM) network. The unsupervised SNN, a brain-like computational model, simulates the human brain’s response when PSF interventions occur instantaneously. Meanwhile, an auto-encoder architecture integrating the unsupervised SNN makes the representations learned by SNN more meaningful. In addition, for the irregularity of PSF interventions, the standard LSTM is extended to the architecture STLSTM to predict the long-term effects of PSFs on human performance. Finally, a case study of the Wuhan–Guangzhou (WH–GZ) high-speed railway (HSR) shows how Monte Carlo simulation based on Fault Tree Analysis is used to assess and collect human error data and then how PSF and human error data are used to train the whole framework. The comparative experiment and ablation study indicate that the proposed SNN-STLSTM method outperforms other artificial intelligence approaches. This study provides insightful findings that help to understand how and what aspects affect human performance in HSR operations. The proposed method can dynamically predict the human error probabilities of the drivers, which facilitates taking timely prevention measures and reduces the accident rate.