Developing Bayesian Belief Networks to Support Risk-Based Decision Making in Railway Operations

Abstract

This paper presents research conducted to model the factors influencing Railway Lookout behaviour in a Bayesian Belief Network (BBN). Railway Lookouts are responsible for monitoring the approach of trains and warning their colleagues to clear the track before the train passes. As such, it is a responsible job which requires a high degree of vigilance and lapses in vigilance can have major consequences. The work presented here has attempted to identify and model the factors affecting vigilance in a BBN in order to provide decision support for judging the risks involved in Lookout operations on a daily basis. An understanding of the lookout task was achieved through the use of interviews, workshops, an on-site observation, and a literature review including internal reports from the infrastructure manager and from these the model was developed. The paper details the factors included and their justification and presents the initial results of the BBN. The final Bayesian Network reveals that a Lookout’s efficacy within the lookout task is not sot solely determined by vigilance factors but also auditory, visual and interpersonal factors. The Network shows that Vigilance is dependent on a number of other factors, divided among internal, external, and environmental attention. The paper also discusses the implications of the Network, its potential applications, and possible avenues of further research.

Keywords: Rail Human Factors, Vigilance, Bayesian Belief Network

DOI: 10.54941/ahfe100733