At the limit? Using operational data to estimate train driver human reliability.

Human reliability analysis plays an important role in the safety assessment and management of rail operations. This paper discusses how the increasing availability of operational data can be used to develop an understanding of train driver reliability. The paper derives human reliability data for two driving tasks, stopping at red signals and controlling speed on approach to buffer stops. In the first of these cases, a tool has been developed that can estimate the number of times a signal is approached at red by trains on the Great Britain (GB) rail network. The tool has been developed using big data techniques and ideas, recording and analysing millions of pieces of data from live operational feeds to update and summarise statistics from thousands of signal locations in GB on a daily basis. The resulting driver reliability data are compared to similar analyses of other train driving tasks. This shows human reliability approaching the currently accepted limits of human performance. It also shows higher error rates amongst freight train drivers than passenger train drivers for these tasks. The paper highlights the importance of understanding the task specific performance limits if further improvements in human reliability are sought. It also provides a practical example of how big data could play an increasingly important role in system error management, whether from the perspective of understanding normal performance and the limits of performance for specific tasks or as the basis for dynamic safety indicators which, if not leading, could at least become closer to real time.

From ethnography to the EAST method: a tractable approach for representing distributed cognition in Air Traffic Control.

Command and control is a generic activity involving the exercise of authority over assigned resources, combined with planning, coordinating and controlling how those resources are used. The challenge for understanding this type of activity is that it is not often amenable to the conventional experimental/methodological approach. Command and control tends to be multi-faceted (so requires more than one method), is made up of interacting socio and technical elements (so requires a systemic approach) and exhibits aggregate behaviours that emerge from these interactions (so requires methods that go beyond reductionism). In these circumstances a distributed cognition approach is highly appropriate yet the existing ethnographic methods make it difficult to apply and, for non-specialist audiences, sometimes difficult to meaningfully interpret. The Event Analysis for Systemic Teamwork method is put forward as a means of working from a distributed cognition perspective but in a way that goes beyond ethnography. A worked example from Air Traffic Control is used to illustrate how the language of social science can be translated into the language of systems analysis. Statement of Relevance: Distributed cognition provides a highly appropriate conceptual response to complex work settings such as Air Traffic Control. This paper deals with how to realise those benefits in practice without recourse to problematic ethnographic techniques.

Event Analysis of Systemic Teamwork (EAST): a novel integration of ergonomics methods to analyse C4i activity.

C4i is defined as the management infrastructure needed for the execution of a common goal supported by multiple agents in multiple locations and technology. In order to extract data from complex and diverse C4i scenarios a descriptive methodology called Event Analysis for Systemic Teamwork (EAST) has been developed. With over 90 existing ergonomics methodologies already available, the approach taken was to integrate a hierarchical task analysis, a coordination demand analysis, a communications usage diagram, a social network analysis, and the critical decision method. The outputs of these methods provide two summary representations in the form of an enhanced operation sequence diagram and a propositional network. These offer multiple overlapping perspectives on key descriptive constructs including who the agents are in a scenario, when tasks occur, where agents are located, how agents collaborate and communicate, what information is used, and what knowledge is shared. The application of these methods to live data drawn from the UK rail industry demonstrates how alternative scenarios can be compared on key metrics, how multiple perspectives on the same data can be taken, and what further detailed insights can be extracted. The ultimate aim of EAST is, by applying it across a number of scenarios in different civil and military domains, to provide data to develop generic models of C4i activity and to improve the design of systems aimed at enhancing this management infrastructure.