Revealing the Complexity of Road Transport with Accimaps

McIlroy, Rich C.; Plant, Katherine L.; Stanton, Neville A.

doi:10.1007/978-3-319-96074-6_9

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Rasmussen's risk management framework serves as the bedrock for the AcciMap technique, which conceptualizes systems as intricate entities with multiple tiers, where events emerge from the intricate interplay between individuals, technology, and organizations at these levels (McIlroy et al, 2018; Rasmussen, 1997). This systemic perspective delves deeper into the behavior of sociotechnical systems during accidents, unraveling the intricate interactions and correlations among factors contributing to accidents (Li et al, 2019; Salmon et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

A dynamic system‐based model for analyzing human factors: Enhanced AcciMap with spherical fuzzy dynamic Bayesian network approach

Tohidizadeh,

Zarei,

Ghalenoei

et al. 2024

Hum Ftrs & Erg Mfg Svc

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In today's interconnected global economy, maritime trade is a pillar of prosperity, yet maritime accidents loom as a formidable challenge. The intricate nature of these accidents, coupled with rapid technological advancements, necessitates the evolution of systematic analysis methods. Conventional systemic approaches, while valuable, struggle to encapsulate the intricate web of mutual and dynamic dependencies inherent in these incidents. Furthermore, the call for more quantitative support in decision‐making and the ability to account for emergent factors has become increasingly imperative. This study aims to analyze maritime accidents by introducing a quantitative and dynamic model. The endeavour begins with establishing an extended Accident Map‐based model, a robust framework that unveils a sophisticated accident causation model. This preliminary action establishes the groundwork for integrating an innovative Spherical Fuzzy Set, navigating the complex landscape of knowledge acquisition. The subsequent phase charts a transformative course by mapping the model onto a dynamic Bayesian Network to conduct a forward and backward analysis. The essence of the model lies in its dynamic nature, allowing for real‐time updates that reflect the evolving maritime accidents risk factors. The approach is validated through a partial benchmark exercise, a reality check, an independent peer review, and a sensitivity analysis. The model can explore emerging contributing factors, reduce uncertainty, and consider relationships between factors that yield designing more effective safety measures.

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