Multi-objective fuzzy rule-based prediction and uncertainty quantification of aircraft taxi time
Multi-objective fuzzy rule-based prediction and uncertainty quantification of aircraft taxi time
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DOI：
10.1109/itsc.2017.8317826
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Jun Chen;Michal Weiszer;E. Zareian;M. Mahfouf;O. Obajemu
中科院分区：
文献类型：
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作者：
Jun Chen;Michal Weiszer;E. Zareian;M. Mahfouf;O. Obajemu
不断增长的空中交通需求和高度互联的航空运输网络给航空业带来了巨大的压力，要求航空业优化空中交通管理（ATM）相关性能，并开发强大的ATM系统。最近在准确的飞机滑行时间预测方面所做的努力表明，在生成更有效的滑行路线和时间表方面取得了重大进展，使其他关键的机场禁区操作（如跑道排序和登机口分配）受益。然而，很少有研究一直致力于量化与滑行飞机的不确定性。基于飞机的确定性和准确的滑行时间预测生成的路线和时间表在不确定性下可能不具有弹性，这是由于诸如变化的天气条件、操作场景和飞行员行为等因素，从而损害系统范围的性能，因为滑行延迟可以在整个网络中传播。因此，本文的主要目的是利用多目标模糊规则为基础的系统，以更好地量化这种不确定性的基础上，历史的飞机滑行数据。初步结果表明，所提出的方法可以捕捉不确定性，在一个更翔实的方式，因此代表了一个有前途的工具，以进一步发展强大的出租车规划，以减少延误，由于不确定的出租车时间。
The ever growing air traffic demand and highly connected air transportation networks put considerable pressure for the sector to optimise air traffic management (ATM) related performances and develop robust ATM systems. Recent efforts made in accurate aircraft taxi time prediction have shown significant advancement in generating more efficient taxi routes and schedules, benefiting other key airside operations, such as runway sequencing and gate assignment. However, little study has been devoted to quantification of uncertainty associated with taxiing aircraft. Routes and schedules generated based on deterministic and accurate taxi time prediction for an aircraft may not be resilient under uncertainties due to factors such as varying weather conditions, operational scenarios and pilot behaviours, impairing system-wide performance as taxi delays can propagate throughout the network. Therefore, the primary aim of this paper is to utilise multi-objective fuzzy rule-based systems to better quantify such uncertainties based on historic aircraft taxiing data. Preliminary results reveals that the proposed approach can capture uncertainty in a more informative way, and hence represents a promising tool to further develop robust taxi planning to reduce delays due to uncertain taxi times.