Intuition, the Accimap, and the question “why?” Identifying and classifying higher‐order factors contributing to road traffic collisions

Abstract

Although the physical and environmental factors contributing to road traffic collisions are often the most straightforward to identify, they may not necessarily be the most useful in terms of guiding road safety intervention design. From a sociotechnical systems perspective, it is the higher system factors that, if addressed, would provide the widest‐reaching benefits to system outcomes. That said, the identification of these higher‐order factors often requires a level of intuition or imagination; they are not always objectively obvious given the facts of a particular case. Here, we argue for the use of the Accimap approach to accident (or collision) analysis, supplemented with a modified version of the five whys causal analysis technique, for the support and structuring of this intuiting process. We use an analysis of a fatal motorcycle collision to illustrate how coroners in the United Kingdom could be supported in their task of identifying and summarizing factors in accordance with the levels of the Accimap, and according to a categorization scheme indicating how abstract those factors are (i.e., immediate, proximal, or distal). Our analysis identified 21 immediate, 12 proximal, and 33 distal factors contributing to the collision. It is these distal factors that are most likely to lead to longer‐term road safety improvements.

The application of systematic accident analysis tools to investigate food safety incidents

“…AcciMap is one of the most popular approaches among the systemic accident analysis tools Underwood & Waterson, 2014) and has a wide application having been used to assist accident investigations and accident analysis in a range of scenarios. The following is not an exhaustive list but presents examples of some of the areas of AcciMap application: FS (Diaz De Oleo et al, 2022;Nayak & Waterson, 2016); aviation (Branford, 2011;Thoroman et al, 2019Thoroman et al, , 2020; led outdoor activities (Salmon et al, 2017); maritime and ferry accidents (Jiang, 2016;Lee et al, 2017); mining (Stemn et al, 2020); oil and gas industry (Tabibzadeh & Meshkati, 2015); road traffic collisions with road users (e.g., pedestrian) and vehicles (Hamim et al, 2020a;Hamim et al 2020b;Hamim et al2022;Mcilroy et al, 2020;Read et al, 2013;Stefanova et al, 2022); and police armed response actions (Jenkins et al, 2010).…”

The Risks of Using Chatgpt to Obtain Common Safety-Related Information and Advice

“…An aspect of the response concerning risk data is that ChatGPT appears to use the National Highway Traffic Safety Administration (NHTSA), a US-based organisation, when explaining that phone use while driving is a leading cause of road crashes. However, causes for crashes can vary among jurisdictions depending on psychosocial, technological, legal, and infrastructural factors (Das et al, 2021;McIlroy et al, 2021). Thus, phone use while driving might not be a leading cause for crashes in some jurisdictions.…”

The application of a system‐based risk management framework and social network analysis to the Maritime Autonomous Surface Ship system: Who are the decision‐makers in the wider system?

“…The first step of modeling the UK MASS system was to identify the different actors, organizations, and decision‐makers in the UK MASS system by creating an actor map. Rasmussen's (1997) RMF is often used to analyse accidents, by producing an Accimap to show how a particular event occurred by considering the whole system and to suggest system recommendations to mitigate these risks in the future (Debrincat et al, 2013; Kant & Khobragade, 2022; McIlroy et al, 2021; Parnell et al, 2017; Stanton et al, 2019; Underwood & Waterson, 2014). Part of the Accimap approach is to create an actor map to show the different organizations, actors, and decision‐makers involved in the events leading to the accident and the position of those actors on the RMF (Svedung & Rasmussen, 2002).…”