Quantitative risk assessment of railway intrusions with text mining and fuzzy Rule-Based Bow-Tie model

With the increasing traveling speed of railway transportation, rail right-of-way intrusions can cause high-consequence accidents and pose severe challenges to railway safety. Although intrusion detection technologies have been widely studied and applied, they can only support in-event inspection and post-event control. In the current complex environment, there is a critical need to analyze the causal chain of railway intrusions and mitigate safety risks before or during the risk evolution process. This paper developed a novel methodological framework on the cause-consequence model based on the text mining techniques and fuzzy bow-tie modeling to systematically investigate the railway intrusion risks. In order to mine both critical factors and their interrelationships, a lexical co-occurrence analysis was carried out on a customized corpus of intrusion accident recordings. Then structured bow-tie diagrams were developed based on the networks generated by unstructured data. To overcome the data uncertainty issue, this paper utilized cause-consequence-based probabilistic analysis and fuzzy theory to quantify the risks involving the occurrence probability of top events and outcomes in terms of expert judgements. The application of the proposed bow-tie model was demonstrated based on the case of the Hualien Derailment accident. The findings based on the bow-tie model and historical accidents in this research have systematically summarized basic events and causal chains. Ultimately, they can be utilized by researchers and practitioners both to identify the critical risk factors and enhance railway safety via proactive and reactive measures.