Topological analysis of risks in hazardous materials transportation systems using fitness landscape theory and association rules mining

Determining the failure modes of hazardous materials transportation systems, considering the coupled effects of risk factors, is crucial for ensuring transportation safety. This study proposes a coupled topological analysis method for hazardous materials road transport risks, based on association rule mining and fitness landscape theory. This method can reflect the correlations and evolutionary patterns of risk factors, thereby providing a basis for formulating risk mitigation strategies. Firstly, text mining techniques are employed to identify critical risk factors and gather a structured dataset comprising 165 entries. Secondly, association rule algorithms are used to uncover potential relationships among sub-factors, employing the Apriori algorithm with set thresholds to extract strong association rules, which are then mapped into a landscape model depicting the coupled evolution of system risk factors. Finally, by employing a defined fitness function, typical system failure paths are further analyzed topologically. The results indicate that directly mining failure paths from sub-risk factors can elucidate more detailed system failure mechanisms. Coupled failure modes involving human and environmental factors warrant particular attention. Vehicle factors often lead to accidents without further evolution, necessitating the establishment of corresponding inspection mechanisms.