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Optimizing Online Situation Awareness Probes in Air Traffic Management Tasks

机译:在空中交通管理任务中优化在线态势感知探针

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摘要

We examined the effectiveness of situation awareness probe questions in predicting sector performance and behavior in a human-in-the-loop simulation air traffic management (ATM) simulation with low (50%) and high (75%) traffic densities. Probes were presented online during the performance of the air traffic management task, and the accuracy and response latencies were measured. Hierarchical linear modeling was used to analyze the predictive power of each category type. Response latencies for conflict probe questions predicted performance metrics associated with separation assurance.
机译:我们在低密度(50%)和高密度(75%)的人工在环仿真空中交通管理(ATM)仿真中检查了态势感知探针问题在预测部门绩效和行为方面的有效性。在执行空中交通管理任务期间,在线提出了探针,并测量了准确性和响应延迟。分层线性建模用于分析每种类别类型的预测能力。冲突探针问题的响应等待时间可预测与分离保证相关的性能指标。

著录项

  • 来源
  • 会议地点 San Diego CA(US);San Diego CA(US);San Diego CA(US)
  • 作者单位

    California State University Long Beach, Center for the Study of Advanced Aeronautics Technologies 1250 N Bellflower Blvd. Long Beach, CA 90840, USA;

    California State University Long Beach, Center for the Study of Advanced Aeronautics Technologies 1250 N Bellflower Blvd. Long Beach, CA 90840, USA;

    California State University Long Beach, Center for the Study of Advanced Aeronautics Technologies 1250 N Bellflower Blvd. Long Beach, CA 90840, USA;

    California State University Long Beach, Center for the Study of Advanced Aeronautics Technologies 1250 N Bellflower Blvd. Long Beach, CA 90840, USA;

    California State University Long Beach, Center for the Study of Advanced Aeronautics Technologies 1250 N Bellflower Blvd. Long Beach, CA 90840, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    situation awareness measurement; air traffic management; NextGen;

    机译:态势感知测量;空中交通管理;下一代;

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