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Train traffic control is a complex task in a dynamic environment. Different actors have to cooperate to meet strong requirements regarding safety, punctuality, capacity utilization, energy consumption, and more. The GMOC model has been developed and utilized in a number of studies in several different areas. This thesis describes GMOC and uses train traffic control as the application area for evaluating its utility.

The GMOC model has its origin in control theory and relates to concepts of dynamic decision making. Human operators in complex, dynamic control environments must have clear goals, reflecting states to reach or to keep a system in. Mental models contain the operator’s knowledge about the task, the process, and the control environment. Systems have to provide observability, means for the operator to observe the system’s states and dynamics, and controllability, allowing the operators to influence the system’s states. GMOC allows us to constructively describe complex environments, focusing on all relevant parts. It can be utilized in user-centred system design to analyse existing systems, and design and evaluate future control systems.

Our application of GMOC shows that automation providing clear observability and sufficient controllability is seen as transparent and most helpful. GMOC also helps us to argue for visualization that rather displays the whole complexity of a process than tries to hide it.

Our studies in train traffic control show that GMOC is useful to analyse complex work situations. We identified the need to introduce a new control strategy improving the traffic plan by supporting planning ahead. Using GMOC, we designed STEG, an interface implementing this strategy. Improvements that have been done to observability helped the operators to develop more adequate mental models, reducing use of cognitive capacity but increasing precision of the operative traffic plans. In order to improve the traffic controllers’ controllability, one needs to introduce and share a real-time traffic plan, and provide the train drivers with up-to-date information on the surrounding traffic. Our studies indicate that driver advisory systems, including such information, reduce the need for traffic re-planning, improve energy consumption, and increase quality and capacity of train traffic.

There is strong motivation for having rail technology that is both international and interoperable. The practice, however, of moving technology that works well in one operational setting to another is not straightforward. This paper takes one type of technology, traffic control automation, and looks at variability between two contexts – GB and Sweden. The output from this work is a socio-technical framework which will be used to asses the viability of applying new advances in traffic management across a number of EU countries.

Earlier studies of human operators in complex and dynamic work situations have demonstrated the importance of understanding the operator’s goal, mental model, observability, and controllability. Based on this model we have been able to analyse and design control systems and user interfaces supporting efficient control and high situation awareness. We now extended this model to include collaboration between different actors in complex control environments. This paper describes basic elements of the model, extension to collaboration, and its application to understand important problems and prerequisites for development of control systems in train traffic control in Sweden.

During the past decade, train Driver Advisory Systems (DAS) have been an emerging topic. Many projects have been started with the goal to increase punctuality and decrease energy consumption. We found that many systems provide too little information, e.g. only a speed advice, and thus do neither improve drivers’ skills nor their Situation Awareness (SA) of the traffic situation. In collaboration with train drivers, we developed key concepts for future DAS. This paper discusses today’s problems and principles to solve them. We explain how SA can be supported and how this can improve overall quality of train traffic.

The demands on modern railway traffic are high. More efficiency is required, meaning better utilisation of infrastructure capacity and reduced energy consumption, while safety and on time performance have to be guaranteed. The operation of railway traffic includes many actors in different roles, at different organisations, as well as many different technical systems. Improved collaboration between the actors and more intelligence in control systems are needed to increase efficiency. However, today’s systems for automation are often badly integrated in the control systems and can come in conflict with the human operators’ tasks. Systems for exchange of central information such as the current traffic plan do not exist, making it impossible for train drivers to follow updated plans in case of disrupted traffic. This paper identifies the different roles involved in railway traffic and explores the concept of a real-time traffic plan (RTTP) to coordinate their collaboration. We present a Swedish system that is implemented and in use, allowing re-planning, and sharing of such an RTTP. We discuss general and specific design solutions for systems for train traffic control and driver advice and explain a way of introducing automation that supports the traffic controllers without interfering with their planning.

This article describes the importance ofusing a user-centred deployment process.The article is based on a case study of twotrain traffic control centres where the samesystem, STEG, was deployed with differentcontexts.