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SUBJECTIVE AIR TRAFFIC COMPLEXITY ESTIMATION USING ARTIFICIAL NEURAL NETWORKS

机译：基于人工神经网络的主观空中交通复杂度估计

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摘要

Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controUers. However, there is a need to make a method for complexity estimation which can be used without constant controller input So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajec-tory-based).
机译：空中交通复杂性通常被定义为监视和管理特定空中交通状况的难度。由于这是一种心理构造，因此最好的衡量复杂性的方法就是空中交通管制员。然而，需要制作一种复杂度估计的方法，该方法可以在没有恒定控制器输入的情况下使用。到目前为止，大多数使用线性模型。在这里，探索了使用人工神经网络进行复杂性估计的可能性。遗传算法已用于搜索最佳的人工神经网络配置。结论是，人工神经网络的性能与线性模型一样好，复杂度估计中的剩余误差只能解释为评估者之间或评估者内部的不可靠性。与线性模型相比，人工神经网络的优势之一是不必基于操作概念（传统的与基于交通的概念）对数据进行过滤。

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