The Difficulty to Break a Relational Complexity Network Can Predict Air Traffic Controllers' Mental Workload and Performance in Conflict Resolution. (March 2021)

• Record Type: Journal Article
• Authors: Zhang, Jingyu; E, Xiaotian; Du, Feng; Yang, Jiazhong; Loft, Shayne
• Is Part Of: Human factors. Volume 63:Number 2(2021)
• Page Start: 240
• Page End: 253
• Publication Date: 2021-03
• Subjects: relational complexity network -- workload -- air traffic controllers -- conflict resolution -- network disentangling -- error -- operation; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720819880646
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: To test the network disentangling model for explaining air traffic controllers' (ATCos) conflict resolution performance. The network rigidity index (NRI), and the steps to break the relational complexity network following a central-available-node-first rule, was hypothesized to explain the overall task demand, whereas marginal-effort-decrease rule was expected to explain the actual operational outcome. Background: Understanding the conflict resolution process of ATCos is important for aviation safety and efficiency. However, linear models are insufficient. We proposed a new model that ATCos behavior can be largely considered as a process to break the relational complexity network, in which nodes represent the aircraft while links represent the cognitive complexity to understand the aircraft dyad relationship. Method: Twenty-one professional ATCos completed 27 conflict resolution scenarios that varied in the NRI and other control variables. Multilevel regression analyses were performed to understand the influence of the NRI on the number of interventions, mental workload, and unresolved rate. A cross-validation was performed to evaluate the predictive power of the model. Results: NRI influenced ATCos intervention number in a curvilinear manner, which further leads to ATCo's mental workload. The deviance between the number of interventions and the NRI was strongly linked with unresolved rate. Cross-validation suggests that the models predictions are robust. Conclusion: The network disentangling model provides a useful theory-driven way to explain controllers' conflict resolution workload and other important performance outcomes such as intervention probability. Application: The proposed model can potentially be used for workload management, sector design, and intelligent decision support tool development.