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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 in, KAJT, FTTS

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 in, KAJT, FTTS

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 in, KAJT, FTTS

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 in, KAJT, FTTS

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 in, KAJT, FTTS

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., Future Train Traffic Control

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., Future Train Traffic Control

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., Future Train Traffic Control

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., Future Train Traffic Control

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., Future Train Traffic Control