Amelia：面向机场场面运动预测的大型数据集与基准
Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting
AI总结：
针对航空出行需求增长下机场管制人员不足、安全事件增多及公共场面运动数据匮乏的问题，研究推出含42个美机场两年轨迹的Amelia-42数据集、配套工具、小型样本集及轨迹预测基准与基线模型，助力空中交通管理技术研发。
摘要：
航空旅行需求持续增长，给现有航空基础设施带来沉重压力。在美国，超过90%的机场管制塔台人员配备不足，未达到美国联邦航空管理局（FAA）及工会的标准，这在一定程度上导致了跑道冲突等安全临界事件的增加，凸显了推动空中交通管理技术进步、保障运行安全高效的必要性。数据驱动的终端空域预测模型有望应对这些挑战，但公共领域缺乏大规模场面运动数据集，阻碍了可扩展、泛化性强的方法研发。为解决这一问题，我们推出Amelia-42，这是首个通过FAA广域信息管理（SWIM）项目流式传输的原始机场场面运动报告大型合集，包含美国42个机场超过两年的轨迹数据（约9.19 TB）。我们开源了将该数据处理为结构化位置报告的工具，还在HuggingFace发布了Amelia42-Mini——每个机场15天的已处理样本数据集，方便用户使用。此外，我们构建了轨迹预测基准，包含Amelia10-Bench（基于10个机场292天数据的易用实验套件）以及Amelia-TF（一种基于transformer的多智能体轨迹预测基线模型）。所有资源可在项目网站获取：https://ameliacmu.github.io 与 https://huggingface.co/AmeliaCMU。
英文摘要：
Demand for air travel is rising, straining existing aviation infrastructure. In the US, more than 90% of airport control towers are understaffed, falling short of FAA and union standards. This, in part, has contributed to an uptick in near-misses and safety-critical events, highlighting the need for advancements in air traffic management technologies to ensure safe and efficient operations. Data-driven predictive models for terminal airspace show potential to address these challenges; however, the lack of large-scale surface movement datasets in the public domain has hindered the development of scalable and generalizable approaches. To address this, we introduce Amelia-42, a first-of-its-kind large collection of raw airport surface movement reports streamed through the FAA's System Wide Information Management (SWIM) Program, comprising over two years of trajectory data (~9.19 TB) across 42 US airports. We open-source tools to process this data into clean tabular position reports. We release Amelia42-Mini, a 15-day sample per airport, fully processed data on HuggingFace for ease of use. We also present a trajectory forecasting benchmark consisting of Amelia10-Bench, an accessible experiment family using 292 days from 10 airports, as well as Amelia-TF, a transformer-based baseline for multi-agent trajectory forecasting. All resources are available at our website: https://ameliacmu.github.io and https://huggingface.co/AmeliaCMU.
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。
