Development of Future Scenarios: Prediction of Mental Workload in a Traffic Management Control Room

Zeilstra, Melcher; Wilms, Marit; Blommers, Fenneke; de Bruijn, David · 2022 · Crossref

DOI: 10.54941/ahfe100730

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of predicting mental workload in future, non-existent operational scenarios, specifically within the context of traffic management control rooms. Existing workload assessment instruments are typically limited to current jobs, relying on objective performance measures or subjective self-reports that cannot be applied to hypothetical situations. To overcome this, the authors developed the Objective Workload Assessment Technique (OWAT™), a generic instrument designed to assess and predict mental workload for jobs that do not yet exist. The study applies OWAT™ to the traffic management control room of Amsterdam, evaluating the impact of integrating new tunnels into the existing monitoring infrastructure. The methodology is based on the VACP method, which categorizes tasks into four information processing modalities: visual, auditory, cognitive, and psycho-motoric. The authors modified the original VACP scoring by replacing complex, military-derived weighting scores with a binary system (1 for presence, 0 for absence) to enhance generic applicability and reduce subjective bias. The validity of OWAT™ was established through high correlations with established instruments like SWORD and IWS. In the case study, traffic managers collaborated to define task clusters and model two-hour scenarios for both current and future states (2014 and 2018). Workload was calculated by aggregating OWAT™ scores for task occurrences during rush hours, both with and without disturbances, and comparing these against defined limits of acceptable workload. The results demonstrated that OWAT™ effectively identified workload bottlenecks and validated staffing requirements. For the current situation, workload during rush hours without disturbances was acceptable for three staff members. During disturbances, workload peaked to unacceptable levels for a single manager but remained manageable for two managers utilizing prioritization rules. In the future scenarios, the integration of the first new tunnel (2014) could be accommodated within the existing three-desk setup. However, the addition of the second new tunnel (2018) significantly increased workload, particularly during disturbances. The analysis concluded that an additional monitoring desk and staff member were necessary for the 2018 scenario to maintain acceptable workload levels. The participative nature of the assessment was well-received by the traffic managers. The significance of this work lies in the successful application of OWAT™ as a predictive tool for ergonomic design and staffing planning. By enabling the assessment of mental workload in non-existent situations, the instrument allows organizations to anticipate operational bottlenecks and optimize resource allocation before infrastructure changes are implemented. The study confirms that OWAT™ provides a practical, valid, and generic method for predicting workload, supporting strategic decisions in complex control room environments.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• mental demand
• workload measurement
• time perception

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: self report data
• Methodological Resource: metric or index
• Theoretical Contribution: theory or model