用遗传算法生成逼真机场延误数据，助力航空调度研究。
Generation of synthetic delay time series for air transport applications
- 采用简化遗传算法生成机场延误时间序列
- 生成数据与真实数据几乎无法区分，且保持高变异性
- 适用于航班延误传播分析，开源共享给科研界
合成数据生成正受到科学界越来越多关注，因其能解决数据稀缺和隐私问题，并开始应用于航空运输领域。本文针对欧洲和美国大规模航班运行数据，研究生成真实感强的机场延误时间序列问题。比较了三种模型：两种基于前沿深度学习算法，一种简化的遗传算法。结果显示，后者生成的时间序列几乎与真实数据无法区分，同时保持高度变异性。进一步在航班延误传播检测任务中验证了生成数据的有效性。最后，研究将合成数据公开，供学术界使用。
原文摘要 · Abstract (English)
The generation of synthetic data is receiving increasing attention from the scientific community, thanks to its ability to solve problems like data scarcity and privacy, and is starting to find applications in air transport. We here tackle the problem of generating synthetic, yet realistic, time series of delays at airports, starting from large collections of operations in Europe and the US. We specifically compare three models, two of them based on state of the art Deep Learning algorithms, and one simplified Genetic Algorithm approach. We show how the latter can generate time series that are almost indistinguishable from real ones, while maintaining a high variability. We further validate the resulting time series in a problem of detecting delay propagations between airports. We finally make the synthetic data available to the scientific community.
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