Safety risks and train drivers' perceptions: measurement of latent difficulties from the Taiwan railway

This research utilizes data from the Taiwan railway to investigate the applicability of the SHELL (software, hardware, environment, liveware) model and psychometric multidimensional Rasch methods in evaluating train drivers' risk perceptions across various hazardous scenarios. The study establishes a systematic approach to identify which risks are challenging to perceive and to characterize the traits associated with risky train drivers. Our findings indicate that certain drivers may be more prone to incurring higher risks. Safety supervisors can leverage this information to identify these high-risk individuals and implement tailored training programs aimed at mitigating potential risks proactively. Furthermore, the proposed methodology for assessing train drivers' risk perception can be adapted for application in other railway systems. The results derived from Rasch analysis provide railway safety managers with valuable insights, enabling them to minimize the likelihood of railway accidents.