TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing

目前迫切需要更好地利用现有的航空基础设施，因为预计到2035年，空中交通将增长1.5倍。许多机场在接近最大运力的情况下运营，欧盟委员会认识到有必要增加运力以满足需求。此外，低效率的运营会导致延误、拥堵，增加燃料成本和噪音水平，给包括机场、航空公司、乘客和当地居民在内的所有利益攸关方带来不便。一个关键问题是对飞机的地面移动进行路线安排和调度。虽然地面运动只占整个飞行的一小部分，但飞机发动机在滑行速度下的低效运行可能会导致相当大的燃油消耗。这一点尤其适用于地面操作更为复杂的较大机场，但也适用于短途业务，滑行在整个飞行中所占的比例更大。据估计，仅滑行过程中燃烧的燃油就占短途航班燃油消耗量的6%，导致全球每年燃烧500万吨燃油。该项目旨在研究一个考虑多种因素的决策支持系统，以提供更健壮的滑行路线。目前用于飞机滑行路线和调度的决策支持系统有几个局限性：1)它们考虑的唯一目标是最小化滑行时间，而忽略了其他重要因素。这些其他因素包括考虑发动机的性能，这与燃料消耗、环境影响和成本有关。在燃料和成本方面有效的航线和时刻表因此由于考虑一维目标而受到影响。2)在航线和时刻表的规划中不考虑机身动态。因此，发出的征税指令可能很难执行，使得遵守分配的路线变得不现实。3)出租车时间通常基于飞机的平均速度。这是一种过于简单化的意思，即任何滑行动作如果超出预期持续时间，都会影响其他飞机的滑行。此外，如果采用过于保守的时间缓冲来吸收不确定性，将导致机场整体运营效率下降。4)很难为路线和调度系统指定滑行速度和启发式规则：它们取决于机场布局和运营要求，而这些要求可能会随着空中交通流量的不同而变化。因此，必须针对特定的机场和运营约束重新配置路线和调度系统。5)由于滑行速度的变化性和飞机模型的过于简单化，人们缺乏对现实环境中自动路线和调度可以获得多少好处的理解。过境将直接解决当前系统的这些局限性，以更好地利用现有的机场基础设施，并减少不断增长的航空业对环境的影响。多目标优化算法将与飞机型号相结合，以平衡减少滑行时间、成本和排放。我们的目标是使路线和调度系统能够轻松地重新配置到任何机场。这种不确定性将被直接纳入规划，导致健壮的滑行，并通过环路试验进行验证。TRANSIT的目标是在解决这种具有挑战性的问题所需的广泛学科和领域(工程学、运筹学和计算机科学)之间合作调查这样一个系统及其相关的好处。与领先的行业利益攸关方合作，并与知名学者进行磋商，确保工作是前沿的，同时反映行业合作伙伴的需求。

There is an imminent need to make better use of existing aviation infrastructure as air traffic is predicted to increase 1.5 times by 2035. Many airports operate at near maximum capacity, and the European Commission recognises the necessity to increase capacity to satisfy demand. In addition, inefficient operations lead to delays, congestion, and increased fuel costs and noise levels inconveniencing all stakeholders, including airports, airlines, passengers and local residents. A critical issue is routing and scheduling the ground movements of aircraft. Although ground movement is only a small fraction of the overall flight, the inefficient operation of aircraft engines at taxiing speed can account for a significant fuel burn. This applies particularly at larger airports, where ground manoeuvres are more complex, but also for short-haul operations, where taxiing represents a larger fraction of an overall flight. It is estimated that fuel burnt during taxiing alone represents up to 6% of fuel consumption for short-haul flights resulting in 5m tonnes of fuel burnt per year globally. This project aims to investigate a decision support system which considers multiple factors to provide more robust taxiing routes. Current decision support systems for routing and scheduling taxiing aircraft suffer from several limitations:1) The only objective they consider is minimising taxi time, ignoring other important factors. These other factors include taking into account engine performance which is linked to fuel consumption, environmental impact and cost. Routes and schedules, which are efficient in terms of fuel and cost, are therefore compromised as a result of considering a one dimensional objective.2) Airframe dynamics are not taken into account during planning of routes and schedules. Consequently, the taxing instructions issued may be hard to follow, making compliance with the allocated routes unrealistic.3) Taxi time is typically based on average speeds of aircraft. This is an over-simplification meaning that any taxiing manoeuvre which falls outside the expected duration can affect the taxiing of other aircraft. Furthermore, if the approach of including overly conservative time buffers to absorb uncertainty is adopted, the resulting overall airport operating efficiency will be degraded.4) It is difficult to specify taxiing speeds and heuristic rules for routing and scheduling systems as: they depend on airport layout and operational requirements, which can vary throughout the day according to the volume of air traffic. Consequently, routing and scheduling systems have to be reconfigured for specific airports and operational constraints.5) Due to variability in taxi speed and over-simplistic models of aircraft, there is lack of understanding as to how much benefit can be achieved by automated routing and scheduling in real-world settings. TRANSIT will directly address these limitations of current systems, to make better use of existing airport infrastructure and lessen the impact of the growing aviation sector on the environment. Multi-objective optimisation algorithms will be integrated with models of aircraft to balance the reduction of taxi time, cost and emissions. We aim to make the routing and scheduling system easily reconfigurable to any airport. The uncertainty will be directly incorporated in planning, resulting in robust taxiing, verified by pilot-in-the-loop trials.TRANSIT aims to investigate such a system and its associated benefits in collaboration across a broad range of disciplines and fields (Engineering, Operational Research, and Computer Science) needed to tackle such challenging problem. Cooperation with leading industrial stakeholders, and consultation with established academics, ensure that the work is cutting edge while reflecting needs of the industrial partners.