A slim integrated with empirical study and network analysis for human error assessment in the railway driving process

Abstract

Human error assessment is essential work to guarantee the safety of the locomotive driving process. Success Likelihood Index Method (SLIM) is a well-known approach applied to determine human error probability (HEP) for railway critical operations. However, the dependencies between performance shaping factors (PSFs) make it difficult to evaluate the accurate weights of PSFs. To overcome this deficiency, a hybrid SLIM utilizes empirical study and Complex Network to assess HEP accounting for the dependencies during the weighting procedure of PSFs. With the aid of Human Factors Analysis and Classification System, 611 accident/incident reports are investigated to explore the links, then three key parameters are tailored to acquire the weights of PSFs by network analysis. Finally, the SLIM approach is performed to evaluate HEPs of typical human errors. In order to validate this proposed method, we apply Monte Carlo simulation to obtain the state of system reliability. The calculated results are consistent with the simulation results and conform to the experience and knowledge in railway operation. The hybrid SLIM approach is useful to reduce the likelihood of occurrence of errors, and improve the overall safety level in the railway driving process and other industries.