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至 Covid-19 全球爆发之前水平的 88%，伦敦希思罗机场等欧洲主要枢纽再次处理超过 1000 次跑道起降（即着陆或起飞）平均每天。大量的空中交通对机场基础设施提出了很高的要求，其中跑道容量是最关键的瓶颈。需求与运力失衡会导致航班延误，这不仅会扰乱航空公司和乘客的行程，还会造成严重的财务后果和环境影响。为了减轻航班延误的风险，可以采取各种类型的干预措施。 “战略”干预是指在特定运营日期之前、在任何“实时”信息（例如天气状况、航空公司机组人员短缺）获知之前进行的干预。这些类型的干预措施通常涉及限制机场每小时可安排的到达和出发的数量。另一方面，“战术”干预是在特定行动日针对实时发生的事件而进行的干预。例如，空中交通管制员了解即将抵达航站楼空域的飞机的最新位置和预计到达时间，并可以使用这些信息来规划最有效的飞机着陆顺序，以最大限度地提高跑道吞吐率并减少停机时间。预期的机载等待时间。在目前的实践中，机场调度是通过称为“时刻协调”的过程进行的。机场时刻表必须符合机场容量声明，该声明对每小时预定跑道起降次数施加了限制。然而，即使机场的时间表与其容量声明一致，也不能保证该时间表下的延误将保持在“可接受”的范围内 - 因为实际上，这些延误取决于一系列随机因素（例如上游延误、天气状况）以及空中交通管制员实施的实时战术干预。我们建议开发一个新的机场时刻表优化框架，通过高保真、随机和动态的空中交通管制模型明确模拟机场延误，旨在确保最终的机场时刻表导致延误超过“可接受”的风险相对较低为了进一步详细说明，我们提出的优化框架由两个独立（但相关）的模块组成：1。首先，我们使用混合整数线性规划（MILP）模型来最小化时刻表偏差，其定义为机场时刻表与理想“基线”场景之间的总偏差量。该 MILP 公式包括限制可以在不同时间段安排的到达和出发数量的约束。2． MILP 在步骤 1 中给出的最佳时刻表被视为最终机场时刻表的“候选”。在此步骤中，我们使用机场排序问题的随机动态模型来测试候选时间表下的预期延误是否满足一组基于延误的性能标准，其中包括基于准时性和燃油排放的组件。这是一个战术优化问题，其中飞机排序决策是在不断变化的随机条件下做出的。如果满足绩效标准，则候选时间表将被接受为最终时间表，并且该过程完成。否则，我们返回步骤 1 并重新制定 MILP 的约束，使其“更严格”，以进一步限制特定时段内可以安排的航班数量。迭代地重复该过程（根据需要多次重新制定 MILP 约束），直到找到满足基于延迟的标准的候选调度。