How do principles for human-centred automation apply to Disruption Management Decision Support?

While automation of signal and route setting is routine, the use of automation or decision support in disruption management processes is far less common. Such support offers significant advantages in optimising re-planning of both timetable and resources (crew and rolling stock), and has value in offering a 'shared view' of re-planning across the many actors manage disruption. If this vision is to be realised, however, disruption management decision support and automation must adhere to proven principles for effective human-agent cooperation. This paper synthesises data from a programme of work to understand user requirements for automated disruption support tools. It then compares these outputs with two frameworks for human-centred automation - one general (Klein et al's [2004] ten challenges for automation) and one transport specific (Balfe et al’s [2012] principles for transport automation). Emergent design requirements include the need for iterative modification of rescheduling parameters throughout a disruption, visibility of the reasoning behind options, accountability remaining in the hands of disruption controllers, and the need for the automated disruption support tools to take a multi-dimensional view of disruption that varies depending on the event encountered. The paper reflects on the practical utility of high-level design principles for automated disruption support tools

How do principles for human-centred automation apply to Disruption Management Decision Support?

While automation of signal and route setting is routine, the use of automation or decision support in disruption management processes is far less common. Such support offers significant advantages in optimising re-planning of both timetable and resources (crew and rolling stock), and has value in offering a 'shared view' of re-planning across the many actors manage disruption. If this vision is to be realised, however, disruption management decision support and automation must adhere to proven principles for effective human-agent cooperation. This paper synthesises data from a programme of work to understand user requirements for automated disruption support tools. It then compares these outputs with two frameworks for human-centred automation - one general (Klein et al's [2004] ten challenges for automation) and one transport specific (Balfe et al’s [2012] principles for transport automation). Emergent design requirements include the need for iterative modification of rescheduling parameters throughout a disruption, visibility of the reasoning behind options, accountability remaining in the hands of disruption controllers, and the need for the automated disruption support tools to take a multi-dimensional view of disruption that varies depending on the event encountered. The paper reflects on the practical utility of high-level design principles for automated disruption support tools

Human factors of future rail intelligent infrastructure

The introduction of highly reliable sensors and remote condition monitoring equipment will change the form and functionality of maintenance and engineering systems within many infrastructure sectors. Process, transport and infrastructure companies are increasingly looking to intelligent infrastructure to increase reliability and decrease costs in the future, but such systems will present many new (and some old) human factor challenges. As the first substantial piece of human factors work examining future railway intelligent infrastructure, this thesis has an overall goal to establish a human factors knowledge base regarding intelligent infrastructure systems, as used in tomorrow’s railway but also in many other sectors and industries. An in-depth interview study with senior railway specialists involved with intelligent infrastructure allowed the development and verification of a framework which explains the functions, activities and data processing stages involved. The framework includes a consideration of future roles and activities involved with intelligent infrastructure, their sequence and the most relevant human factor issues associated with them, especially the provision of the right information in the right quantity and form to the right people. In a substantial fieldwork study, a combination of qualitative and quantitative methods was employed to facilitate an understanding of alarm handling and fault finding in railway electrical control and maintenance control domains. These functions had been previously determined to be of immediate relevance to work systems in the future intelligent infrastructure. Participants in these studies were real railway operators as it was important to capture users’ cognition in their work settings. Methods used included direct observation, debriefs and retrospective protocols and knowledge elicitation. Analyses of alarm handling and fault finding within real-life work settings facilitated a comprehensive understanding of the use of artefacts, alarm and fault initiated activities, along with sources of difficulty and coping strategies in these complex work settings. The main source of difficulty was found to be information deficiency (excessive or insufficient information). Each role requires different levels and amounts of information, a key to good design of future intelligent infrastructure. The findings from the field studies led to hypotheses about the impact of presenting various levels of information on the performance of operators for different stages of alarm handling. A laboratory study subsequently confirmed these hypotheses. The research findings have led to the development of guidance for developers and the rail industry to create a more effective railway intelligent infrastructure system and have also enhanced human factors understanding of alarm handling activities in electrical control

How do principles for human-centred automation apply to Disruption Management Decision Support?

While automation of signal and route setting is routine, the use of automation or decision support in disruption management processes is far less common. Such support offers significant advantages in optimising re-planning of both timetable and resources (crew and rolling stock), and has value in offering a 'shared view' of re-planning across the many actors manage disruption. If this vision is to be realised, however, disruption management decision support and automation must adhere to proven principles for effective human-agent cooperation. This paper synthesises data from a programme of work to understand user requirements for automated disruption support tools. It then compares these outputs with two frameworks for human-centred automation - one general (Klein et al's [2004] ten challenges for automation) and one transport specific (Balfe et al’s [2012] principles for transport automation). Emergent design requirements include the need for iterative modification of rescheduling parameters throughout a disruption, visibility of the reasoning behind options, accountability remaining in the hands of disruption controllers, and the need for the automated disruption support tools to take a multi-dimensional view of disruption that varies depending on the event encountered. The paper reflects on the practical utility of high-level design principles for automated disruption support tools

Human factors of future rail intelligent infrastructure

The introduction of highly reliable sensors and remote condition monitoring equipment will change the form and functionality of maintenance and engineering systems within many infrastructure sectors. Process, transport and infrastructure companies are increasingly looking to intelligent infrastructure to increase reliability and decrease costs in the future, but such systems will present many new (and some old) human factor challenges. As the first substantial piece of human factors work examining future railway intelligent infrastructure, this thesis has an overall goal to establish a human factors knowledge base regarding intelligent infrastructure systems, as used in tomorrow’s railway but also in many other sectors and industries. An in-depth interview study with senior railway specialists involved with intelligent infrastructure allowed the development and verification of a framework which explains the functions, activities and data processing stages involved. The framework includes a consideration of future roles and activities involved with intelligent infrastructure, their sequence and the most relevant human factor issues associated with them, especially the provision of the right information in the right quantity and form to the right people. In a substantial fieldwork study, a combination of qualitative and quantitative methods was employed to facilitate an understanding of alarm handling and fault finding in railway electrical control and maintenance control domains. These functions had been previously determined to be of immediate relevance to work systems in the future intelligent infrastructure. Participants in these studies were real railway operators as it was important to capture users’ cognition in their work settings. Methods used included direct observation, debriefs and retrospective protocols and knowledge elicitation. Analyses of alarm handling and fault finding within real-life work settings facilitated a comprehensive understanding of the use of artefacts, alarm and fault initiated activities, along with sources of difficulty and coping strategies in these complex work settings. The main source of difficulty was found to be information deficiency (excessive or insufficient information). Each role requires different levels and amounts of information, a key to good design of future intelligent infrastructure. The findings from the field studies led to hypotheses about the impact of presenting various levels of information on the performance of operators for different stages of alarm handling. A laboratory study subsequently confirmed these hypotheses. The research findings have led to the development of guidance for developers and the rail industry to create a more effective railway intelligent infrastructure system and have also enhanced human factors understanding of alarm handling activities in electrical control

Seeing the woods for the trees: the problem of information inefficiency and information overload on operator performance

One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information and increasing situation awareness is so tempting (and in safety critical domains often mandatory) that has become an obstacle for designers and operators. This research examined this challenge within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al., 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem solving scenario within railways. Results indicated that presenting users with information corresponding to their cognitive task (and no more) improves the performance of their problem solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs

Modelling decision-making within rail maintenance control rooms

This paper presents a cognitive task analysis to derive models of decision-making for rail maintenance processes. Maintenance processes are vital for safe and continuous availability of rail assets and services. These processes are increasingly embracing the ‘Intelligent Infrastructure’ paradigm, which uses automated analysis to predict asset state and potential failure. Understanding the cognitive processes of maintenance operators is critical to underpin design and acceptance of Intelligent Infrastructure. A combination of methods, including observation, interview and an adaptation of critical decision method, was employed to elicit the decision-making strategies of operators in three different types of maintenance control centre, with three configurations of pre-existing technology. The output is a model of decision-making, based on Rasmussen’s decision ladder, that reflects the varying role of automation depending on technology configurations. The analysis also identifies which types of fault were most challenging for operators and identifies the strategies used by operators to manage the concurrent challenges of information deficiencies (both underload and overload). Implications for design are discussed

Seeing the woods for the trees: the problem of information inefficiency and information overload on operator performance

One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information and increasing situation awareness is so tempting (and in safety critical domains often mandatory) that has become an obstacle for designers and operators. This research examined this challenge within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al., 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem solving scenario within railways. Results indicated that presenting users with information corresponding to their cognitive task (and no more) improves the performance of their problem solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs

A framework to support human factors of automation in railway intelligent infrastructure

Technological and organisational advances have increased the potential for remote access and proactive monitoring of the infrastructure in various domains and sectors – water and sewage, oil and gas and transport. Intelligent Infrastructure (II) is an architecture that potentially enables the generation of timely and relevant information about the state of any type of infrastructure asset, providing a basis for reliable decision-making. This paper reports an exploratory study to understand the concepts and human factors associated with II in the railway, largely drawing from structured interviews with key industry decision-makers and attachment to pilot projects. Outputs from the study include a data-processing framework defining the key human factors at different levels of the data structure within a railway II system and a system-level representation. The framework and other study findings will form a basis for human factors contributions to systems design elements such as information interfaces and role specifications

Whom do we include and when? Participatory design with vulnerable groups

This article makes three contributions to participatory design (PD) research and practice with vulnerable groups: 1) a framework for understanding stakeholder engagement over the course of a PD project; 2) approaches to making user engagement and PD activities more inclusive; and 3) an analysis of how the design and power dynamics of PD projects affect vulnerable groups’ participation. A map of engagement is developed to evaluate stakeholder involvement from initial problem definition to design outcome. The map is applied to three projects aimed at increasing inclusion of vulnerable groups in the planning of public sector services. The first looks at codesign activities to support decision-making in the context of intellectual disabilities; the second looks at culturally diverse youth navigating crisis without adequate assistance from public services; and the third examines nursing students adapting to work in the health sector without accommodations for learning disabilities. Comparing the projects reveals patterns in project planning and execution, and in stakeholder relationships. The article analyses how users are defined, engaged and supported in PD; how proxies shape vulnerable groups’ involvement and PD projects as a whole; and opportunities for greater inclusion when the entire PD project is taken into account