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English(EN) Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport

人工智能预测亚特兰大机场飞机滑行决策

研究人员开发了一个两阶段的人工智能系统,用于预测哈兹菲尔德-杰克逊亚特兰大国际机场的飞机滑行决策。该系统利用历史航班数据、飞机特性和天气来预测飞机将选择哪个跑道出口以及是否会穿越起飞跑道。XGBoost和LightGBM等机器学习模型在预测出口选择方面取得了高精度,但预测跑道穿越机动动作更具挑战性。 AI

影响 这项研究展示了人工智能通过预测复杂的地面运行来提高空中交通管制效率和安全性的潜力。

排序理由 学术论文,详细介绍了机器学习在特定领域的创新应用。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Alex Porcayo, Yutian Pang, Maria Thomas, John-Paul Clarke ·

    Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport

    arXiv:2606.11017v1 Announce Type: new Abstract: Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jackson Atlanta International Airport (KATL) and proposes a two-stage, data-driven de…

  2. arXiv cs.LG TIER_1 English(EN) · John-Paul Clarke ·

    Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport

    Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jackson Atlanta International Airport (KATL) and proposes a two-stage, data-driven decision aid that mirrors controller workflow. Sta…