基于时间的矢量量化变分自动编码器（TimeVQ-VAE）的合成飞机轨迹生成
Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE
AI总结：
针对现代空中交通管理中数据稀缺等问题，本文提出适配TimeVQVAE的合成飞机轨迹生成方法，通过时频域处理等技术捕捉飞行动态，经评估其性能优于基线模型，生成轨迹具备良好可行性与代表性。
摘要：
在现代空中交通管理中，生成合成飞行轨迹已成为解决数据稀缺、保护敏感信息和支持大规模分析的有前景方案。本文提出一种新的轨迹合成方法，通过适配基于时间的矢量量化变分自动编码器（TimeVQVAE）实现。该方法利用时频域处理、矢量量化和基于Transformer的先验，捕捉飞行数据的全局和局部动态。通过离散化 latent 空间并集成Transformer先验，模型学习长程时空依赖，保留整个飞行路径的连贯性。我们使用大量质量、统计和分布指标，以及在开源空中交通模拟器中进行的可飞行性评估，对适配的TimeVQVAE进行评估。结果表明，TimeVQVAE优于时间卷积VAE基线，生成的合成轨迹在空间精度、时间一致性和统计特性上与真实飞行数据一致。此外，基于模拟器的评估显示，大多数生成的轨迹保持操作可行性，尽管偶尔的异常值强调可能需要额外的领域特定约束。总体而言，我们的研究结果强调多尺度表示学习对捕捉复杂飞行行为的重要性，并证明TimeVQVAE在为模型训练、空域设计和空中交通预测等下游任务生成代表性合成轨迹方面的前景。
英文摘要：
In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In this paper, we propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE). Our approach leverages time-frequency domain processing, vector quantization, and transformer-based priors to capture both global and local dynamics in flight data. By discretizing the latent space and integrating transformer priors, the model learns long-range spatiotemporal dependencies and preserves coherence across entire flight paths. We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator. Results indicate that TimeVQVAE outperforms a temporal convolutional VAE baseline, generating synthetic trajectories that mirror real flight data in terms of spatial accuracy, temporal consistency, and statistical properties. Furthermore, the simulator-based assessment shows that most generated trajectories maintain operational feasibility, although occasional outliers underscore the potential need for additional domain-specific constraints. Overall, our findings underscore the importance of multi-scale representation learning for capturing complex flight behaviors and demonstrate the promise of TimeVQVAE in producing representative synthetic trajectories for downstream tasks such as model training, airspace design, and air traffic forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
