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.

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.

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.

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.

Games used by organizations generate a wealth of valuable output in terms of knowledge. In order to maintain the produced knowledge, such as the explicit, e.g., logging and questionnaires, and implicit/tacit knowledge, e.g., experience from game sessions, a knowledge management system (KMS) should be employed. This paper starts by giving a brief description of the building blocks for a KMS and then proposes a methodology that combines three different methods, namely, semi-structured interviews, causal maps, and the Q-methodology, to illustrate how tacit knowledge from principal stakeholders (game designers and project team members) can be extracted as part of building a KMS. The proposed methodology is applied in a case study related to the railway sector.

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.