Coordination of Strategic and Tactical Interventions for Reducing Air Traffic Delays: A Case Study Based on Heathrow Airport

协调战略和战术干预以减少空中交通延误：基​​于希思罗机场的案例研究

• 批准号: EP/X039803/1
• 负责人: Robert Shone
• 金额: $ 7.83万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039803%2F1
• 关键词: Coordination; Strategic; Tactical; Interventions; Reducing

As of September 2022, flight numbers in Europe have returned to 88% of the levels seen prior to the global outbreak of Covid-19, and major European hubs such as London Heathrow are again processing more than 1000 runway movements (i.e. landings or take-offs) per day on average. Large volumes of air traffic impose heavy demands on airport infrastructure, with runway capacity being the most critical bottleneck. Demand-capacity imbalances result in flight delays, which not only disrupt airline and passenger itineraries but also have serious financial consequences and environmental impacts.In order to mitigate the risk of flight delays, various types of interventions are possible. "Strategic" interventions are those that are made far in advance of a particular day of operations, before any 'real-time' information (e.g. weather conditions, airline crew shortages) becomes known. These types of interventions typically involve restricting the numbers of arrivals and departures that can be scheduled per hour at an airport. On the other hand, "tactical" interventions are those that are made on a particular day of operations in response to events that unfold in real time. For example, air traffic controllers have knowledge of the latest positions and estimated arrival times of aircraft that are due to arrive in the terminal airspace and can use this information to plan the most efficient sequence of aircraft landings in order to maximise runway throughput rates and reduce expected airborne holding times.In current practice, airport scheduling is carried out via a process known as "slot coordination". Airport schedules are required to comply with airport capacity declarations, which impose limits on hourly numbers of scheduled runway movements. However, even if an airport's schedule is consistent with its capacity declaration, there is no guarantee that the delays seen under that schedule will remain within `acceptable' limits - as, in reality, these delays depend on a range of stochastic factors (e.g. upstream delays, weather conditions) as well as the real-time tactical interventions implemented by air traffic controllers. We propose to develop a new framework for airport schedule optimisation which explicitly models airport delays through a high-fidelity, stochastic and dynamic model of air traffic control and aims to ensure that the final airport schedule results in a relatively low risk of delays exceeding 'acceptable' levels.To elaborate further, our proposed optimisation framework consists of two separate (but related) modules:1. First, we use a mixed integer linear programming (MILP) model to minimise schedule displacement, which is defined as the total amount of deviation between an airport schedule and an ideal 'baseline' scenario. This MILP formulation includes constraints that restrict the numbers of arrivals and departures that can be scheduled in different time slots.2. The optimal schedule given by the MILP in Step 1 is regarded as a 'candidate' for the final airport schedule. In this step we use a stochastic, dynamic model of the airport sequencing problem to test whether or not the expected delays under the candidate schedule satisfy a set of delay-based performance criteria, which includes components based on punctuality and fuel emissions. This is a tactical optimisation problem in which aircraft sequencing decisions are made under continuously-evolving random conditions. If the performance criteria are satisfied, then the candidate schedule is accepted as the final schedule and the process is completed. Otherwise, we return to Step 1 and reformulate the constraints of the MILP, making them 'tighter' in order to further restrict the numbers of flights that can be scheduled in particular time slots. This process is repeated iteratively (reformulating the MILP constraints as many times as necessary) until a candidate schedule is found which satisfies the delay-based criteria.

截至2022年9月，欧洲的航班数量已恢复到2019年全球爆发之前所看到的水平的88％，伦敦希思罗机场等主要的欧洲枢纽每天再次处理1000多个跑道运动（即降落或起飞）。大量的空中交通对机场基础设施构成了巨大的需求，跑道容量是最关键的瓶颈。需求容量失衡导致航班延误，这不仅破坏了航空公司和乘客行程，而且会产生严重的财务后果和环境影响。为了减轻飞行延误的风险，可以进行各种干预措施。 “战略性”干预措施是在特定日期之前进行的，在任何'实时'信息（例如天气条件，航空公司船员短缺）之前，该干预措施已知。这些类型的干预措施通常涉及限制可以在机场安排的到达和出发的数量。另一方面，“战术”干预措施是在特定日期进行的，以实时发生的事件进行响应。例如，空中交通管制员了解将到达终端领空的最新位置和估计的飞机到达时间，并且可以使用此信息来计划最有效的飞机着陆点序列，以最大程度地提高跑道吞吐量，并减少预期的空中空运时间。机场时间表必须遵守机场容量声明，该声明对每小时的预定跑道运动施加限制。但是，即使机场的日程安排与其容量声明一致，也无法保证该时间表下的延迟将保持在“可接受的”限制范围之内 - 实际上，这些延迟取决于空中交通管家实施的一系列随机因素（例如，上游延误，天气条件，天气情况，天气情况，天气条件，天气条件，天气情况，天气条件）。我们建议开发一个新的机场时间表优化框架，该框架通过高保真，随机和动态的空中交通管制模型来模拟机场的延误，并旨在确保最终的机场时间表会导致延误的风险相对较低，超过“可接受的”水平。进一步详细介绍了两个单独的（但相关的）模量，以进一步详细介绍。首先，我们使用混合整数线性编程（MILP）模型来最大程度地减少计划位移，该模型定义为机场时间表和理想的“基线”方案之间的总偏差。该MILP公式包括限制可以在不同时间插槽中安排的到达和出发数量的约束。2。步骤1中MILP给出的最佳时间表被视为最终机场时间表的“候选人”。在此步骤中，我们使用机场测序问题的随机，动态模型来测试候选时间表下的预期延迟是否满足一组基于延迟的绩效标准，其中包括基于守时性和燃料排放的组件。这是一个战术优化问题，在连续发展的随机条件下，飞机测序决策做出。如果满足绩效标准，则将候选时间表作为最终时间表，并且该过程已完成。否则，我们返回步骤1并重新重新制定MILP的限制，使其“更紧密”，以进一步限制可以在特定时间段内安排的航班数量。此过程是迭代重复的（必要的多次重新定义MILP的约束），直到找到满足基于延迟标准的候选时间表为止。