Relational Complexity Network and Air Traffic Controllers' Workload and Performance
Relational Complexity Network and Air Traffic Controllers' Workload and Performance
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DOI：
10.1007/978-3-319-20373-7_49
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Jingyu Zhang;Feng Du
中科院分区：
文献类型：
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作者：
Jingyu Zhang;Feng Du
本文对现有的空中交通管制员工作负荷模型进行了综述。缺乏适当的汇总方法和生态有效性被认为是主要的不足之处。本文介绍了关系复杂性网络（Relational Complexity Network，RCN）框架，该框架基于两个思想：（1）用网络的方法来表示飞行器模式，使其与管制员的信息结构和动作空间相匹配;（2）管制员将主动地利用这种结构来执行任务。作为一个理论驱动的计算模型，RCN框架可用于（1）基于飞机级或配对级信息向管制员的工作负荷模型添加额外的预测能力;（2）预测管制员的公开操作行为;以及（3）理解从视觉分组到操作约束的各种影响。
This paper makes a review on current workload models of air traffic controllers. Lack of proper aggregation method and ecological validity were identified as major inadequacies. We introduce the relational complexity network (RCN) framework which is formed on two ideas: (1) using a network approach to represent the aircraft pattern matches the information structure and action space of controllers; (2) controllers will proactively utilize this structure to perform their task. As a theory-driven computational model, the RCN framework can be used to (1) add extra predictive power to the controllers’ workload models based on aircraft-level or pair-level information; (2) predict controllers’ overt operational behaviors; and (3) understand various effects from visual grouping to operational constraints.