摘要： 空域交通复杂度是影响民航运行效率和安全的重要因素。为了进一步提高空域交通复杂度的评估精度,提出一种基于多尺度空域交通时空图像和深度度量学习的复杂度评估方法。具体而言,将交通流数据以像素点的方式填充到网格化的目标空域图像中,并在时空两个维度进行插值,以充分捕捉交通流在时间和空间上的动态变化,最终生成20组不同尺度的空域交通时空图像集;接着,提出一种基于深度度量学习的空域交通复杂度评估模型,该模型以多尺度空域交通图像集作为输入,使用排序代理锚损失函数优化样本在高维嵌入空间的距离分布,使同类样本之间的距离更紧密,而异类样本之间的距离更分散;最后,利用分类器对空域交通复杂度进行5个等级的评估。实验采用了中南空域的实际交通数据生成多尺度时空图像集,并进行了一系列对比实验。实验结果表明,空域交通图像集的时空尺度对评估结果有重要影响,与其他评估方法相比,所提方法能够显著提高空域交通复杂度评估的准确率。

关键词: 空域交通复杂度, 多尺度时空图像, 空域交通图像集, 深度度量学习, 损失函数

Abstract: Airspace traffic complexity is an important factor affecting the efficiency and safety of civil aviation operations. To further improve the accuracy of airspace traffic complexity assessments, this study proposes a complexity evaluation method based on multi-scale airspace traffic spatio-temporal images and deep metric learning. Specifically, traffic flow data are mapped to grid-based target airspace images in the form of pixels. Spatio-temporal interpolation is performed to capture the dynamic changes in traffic flow over time and space, resulting in the generation of 20 sets of spatio-temporal airspace traffic images at different scales. Then, an airspace traffic complexity assessment model based on deep metric learning is proposed, which takes multi-scaled airspace traffic image sets as the input. The model uses a ranking proxy anchor loss function to optimize the distribution of the sample distances in the high-dimensional embedding space, such that the same-class samples are closer and the different-class samples are more dispersed. Experiments are conducted using real traffic data from the south-central airspace to generate a multi-scale spatio-temporal image set, followed by a series of comparative experiments. The results show that the spatio-temporal scale of the airspace traffic image sets has considerable impact on the assessment results. Compared with existing assessment methods, the proposed method can significantly improve the assessment performance of airspace traffic complexity.

Key words: airspace traffic complexity, multi-scale spatio-temporal image, airspace traffic image sets, deep metric learning, loss function

中图分类号: 

• TP391