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. / KAJT / FTTS

A widespread and persistent memory error that people commit on a daily basis is the post-completion error (PCE; i.e., forgetting to complete the final step of a procedural task). PCEs occur in the railroad industry when a locomotive conductor changes the direction of a rail switch but fails to report this change. This particular error could contribute to unsafe conditions as another train traveling on the same track could derail. Although training can help reduce some of the factors leading to unsafe conditions on the rail, research has demonstrated that PCEs are different from other errors of omission in that they cannot be eliminated through training, which makes them a difficult problem to address. Therefore, there is a need to explore new remedial actions designed to reduce PCEs. The current study investigated the effectiveness of a theoretically motivated intervention at reducing PCEs in trainyard operations, where making these errors could be life-threatening. Twenty-eight undergraduates completed trainyard tasks within a high-fidelity simulator. Each participant received the behavioral intervention in one block and no intervention in another. Specifically, participants were required to perform an additional task designed to remind participants of the post-completion (PC) step. The intervention significantly reduced PCE rates in the context of trainyard operations, on average, by 65%. We discuss implications of these results on reducing trainyard accidents, and how this outcome can contribute to the literature on the cause of PCEs.