Airline and train operations are feasible thanks to the Command and Control systems which involve interactions between human operators, technology, and procedures. In view of the expected growing demand, significant changes in these C2 systems are in development in many countries. Such changes can lead to both positive and negative emergent behaviours. One of the promising approaches to capture this behaviour is agent-based modelling. In order to develop and implement a generic agent-based model for C2 systems, this paper compares and evaluates two C2 systems from the railway and airline domains. The paper conducts a systemic agent-based analysis that explores various dimensions including organizational structure; agents characteristics; operational uncertainties and workflows.

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This paper investigates whether mental workload can be classified in an operator setting using unobtrusive psychophysiological measures. Having reliable predictions of workload using unobtrusive sensors can be useful for adaptive instructional systems, as knowledge of a trainee’s workload can then be used to provide appropriate training level (not too hard, not too easy). Previous work has investigated automatic mental workload prediction using biophysical measures and machine learning, however less attention has been given to the level of physical obtrusiveness of the used measures. We therefore explore the use of color-, and infrared-spectrum cameras for remote photoplethysmography (rPPG) as physically unobtrusive measures. Sixteen expert train traffic operators participated in a railway human-in-the-loop simulator. We used two machine learning models (AdaBoost and Random Forests) to predict low-, medium- and high-mental workload levels based on heart rate features in a leave-one-out cross-validated design. Results show above chance classification for low- and high-mental workload states. Based on infrared-spectrum rPPG derived features, the AdaBoost machine learning model yielded the highest classification performance.

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The most dominant reason for the Dutch railways to innovate has been the expected increase in railway passengers and freight demand. To achieve this a large scale (re)design process of the railways as complex socio-technical system is needed. Since infrastructural expansion alone is not a sustainable option, solutions are also sought in innovative process optimizations. Train traffic and network controllers are responsible operators for the operational management of the railway system. With their experience, these operators hold unique knowledge about the system’s actual characteristics and its dynamics. Therefore, it is crucial to test and train any new system design with them, for example by gaming simulations. This dissertation focuses on the role of the human operator as part of a large-scale socio-technical system (re)design process of the Dutch railway system.@en

In innovating and designing new concepts in the railway sector, the Dutch railway infrastructure manager ProRail uses different types of simulations to identify and tackle possible bottlenecks in future infrastructure design. Computer simulation tools are used in earlier stages of the design process, followed by the application of gaming simulations where the design is fine tuned together with railway traffic operators before it is put into operation. This study focuses on providing insights into the use of a human-in-the-loop simulator in which an organizational research question investigates the impact of multiple geographical workspace designs, while in parallel human factors research is conducted to investigate the concept of team situation awareness from an academic research interest. Finding a balance between the practical and academic implications in one research design and its findings does not rely on a trivial approach. The current article aims to contribute on several levels: (1)to illustrate the balance between research for practice and research for academia through the applications of gaming simulations; (2)to illustrate the use of gaming simulations for railway traffic operations and (3)to provide insights in team SA development in railway traffic operations using gaming simulations.

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This study takes upon a group cognition perspective and investigates the cognition of railway traffic operations, in particular railway traffic and passenger traffic control. A table-top simulation environment is used to conduct the study, in which its design principles are elaborated upon. Network cognition is operationalized through communication content and flow and studied through social network analysis (SNA). SNA centrality metrics, such as degree, closeness and betweenness, are assessed in these networks. As part of the study, two cases are compared where operational procedures for disruption mitigation are varied. The dependent variables are the different types of communication network structures that are conceptualized for communication flow and semantic network structures for communication content. Although the quantitative comparisons between the two operational procedures regarding their communication flow and semantic networks showed no significant differences, this study provides a methodology to compare different conditions.

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Background. Debriefing is an intrinsic part of games for learning and proper
debriefing can also be beneficial to research games. However, the literature
on how to debrief research games is sparse and only provides the professional
with an abstract topic guide.
Aim. The purpose of this study was to design a framework for the debriefing of
research games that are used in ongoing innovation processes.
Method. We used the literature on debriefing and experimental research and our
experience as game designers to build a framework that tackles the context,
substance and method of debriefing research games.
Results. Our framework provides three contributions. First, it shows how
the context in which a research game is applied sometimes impacts the
functionality of the game in negative ways. This can be helped by designing both
the game and the debriefing together. Second, we operationalize validity to a
greater extent, as this is the core of a good research game. Third, we provide
a methodology for debriefing professionals that opens up the black box of the
gaming simulation session.
Conclusion. The debriefing framework provides a method to collectively assess
the validity, reliability and robustness of the causal claims associated with the
research conducted.@en

Previous research on situation awareness (SA) predominantly focused on its explicit, reasoned, conscious features rather than on the implicit, intuitive, unconscious aspects that are often identified with expert operators. This research investigated implicit levels of SA of train traffic controllers (TTCs) in order to contribute to the body of knowledge on rail human factors research and SA. A novel approach was used to uncover levels of implicit SA through a set of three analyses: (1) fairly low SAGAT values with correlations between SAGAT scores and multiple performance indicators; (2) negative correlations between work experience and SAGAT scores; and (3) structurally lower level-1 SA (perception) scores in comparison to level-2 SA (comprehension) scores in accordance with Endsley's three-level model. Two studies were conducted: A pilot study – which focused on SA measurements with TTCs in a monitoring mode (N = 9) – and the main study, which involved TTCs from another control center (N = 20) and three different disrupted conditions. In the pilot study, SA was measured through the situation-awareness global assessment technique (SAGAT), perceived SA and observed SA, and performance was measured through punctuality and unplanned stops of trains before red signals. In the main study, SA was measured through SAGAT, and perceived SA and multiple performance indicators, such as arrival and departure punctuality and platform consistency, were assessed. In both studies, the set of three analyses showed consistent and persistent indications of the presence of implicit SA. Endsley's three-level model and related SAGAT method can be constrained by the presence of these intuitive, unconscious processes and inconsistent findings on correlations between SAGAT scores and performance. These findings provide insights into the SA of TTCs in the Netherlands and can support the development of training programs and/or the design of a new traffic management system.

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Objective: The aim of this study was to examine individual markers of resilience and obtain quantitative insights into the understanding and the implications of variation and expertise levels in train traffic operators’ goals and strategic mental models and their impact on performance. Background: The Dutch railways are one of the world’s most heavy utilized railway networks and have been identified to be weak in system and organizational resilience. Method: Twenty-two train traffic controllers enacted two scenarios in a human-in-the-loop simulator. Their experience, goals, strategic mental models, and performance were assessed through questionnaires and simulator logs. Goals were operationalized through performance indicators and strategic mental models through train completion strategies. Results: A variation was found between operators for both self-reported primary performance indicators and completion strategies. Further, the primary goal of only 14% of the operators reflected the primary organizational goal (i.e., arrival punctuality). An incongruence was also found between train traffic controllers’ self-reported performance indicators and objective performance in a more disrupted condition. The level of experience tends to affect performance differently. Conclusion: There is a gap between primary organizational goals and preferred individual goals. Further, the relative strong diversity in primary operator goals and strategic mental models indicates weak resilience at the individual level. Application: With recent and upcoming large-scale changes throughout the sociotechnical space of the railway infrastructure organization, the findings are useful to facilitate future railway traffic control and the development of a resilient system.@en

Background. The Dutch railway company ProRail is performing large-scale capacity upgrades to their infrastructure network. As part of these upgrades, ProRail uses gaming simulations to help prepare train traffic controllers for new infrastructure situations. Researching the validity of these gaming simulations is essential, since the conclusions drawn from gaming simulation use may result in decisions with large financial and social impact for ProRail and Dutch train passengers. Aim. In this article, we aim to investigate the validity of the gaming simulations for training traffic controllers for new situations in rail infrastructure. We also aim to contribute to the discussion on the minimum level of fidelity required to develop and conduct gaming simulations in a valid way. Method. We investigate the validity by using training sessions in conjunction with questionnaires. We based the approach and questionnaires on the earlier work of Raser. Results. Our results show that the validity of the gaming simulation ranges from medium to good. They also show that while the fidelity of the gaming simulation is not like the real-world operating conditions, this does not reduce validity to low levels. Conclusions. We conclude that the gaming simulation used in this study was of medium to good validity. We also conclude that maximum fidelity is not required in order to run a valid gaming simulation session.@en