Development of Train Operation Risk Assessment System Based on a Data-Driven Bayesian Network

Risks of severe train accidents vary along a railway corridor by track sections with different infrastructure, rolling stock, and operational characteristics because of the complicated nature of train driving environment. Therefore, this research develops a Train Operational Risk Assessment System (TORAS) that assists train drivers to recognize the level of risk for upcoming track sections to enhance their situational awareness. This research proposes a five-phase approach that integrates the Human Factors Analysis and Classification System (HFACS) with Bayesian networks (BN) to identify potential risk events through historical accident data and selected environmental and operational factors that may contribute to the occurrence of risk events. A BN constructed using historical Automatic Train Protection (ATP) data quantifies the relationships between various risk factors while calculating the probabilities of risk events. The results show that the new framework can predict track-section-specific train driving risks by using train operational and route information. TORAS can enhance train driver’s situational awareness by informing them of the risk levels of different track sections in advance, which enables proper caution and proactive preparation, improving the overall safety of train operations by ensuring that drivers are well-informed and able to respond effectively to varying risk conditions.