Data-driven modeling of systemic delay propagation under severe meteorological conditions

The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large-scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.Comment: 9 pages, 5 figures. Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013

A Rolling Horizon Based Algorithm for Solving Integrated Airline Schedule Recovery Problem

Airline disruption incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated airline schedule recovery problem, which considers flight recovery, aircraft recovery and crew recovery simultaneously. First we built an integer programming model which is based on traditional set partitioning model but including flight copy decision variables. Then a rolling horizon based algorithm is proposed to efficiently solve the model. Our algorithm decomposes the whole problem into smaller sub-problems by restricting swapping opportunities within each rolling period. All the flights are considered in each sub-problem to circumvent ‘myopic’ of traditional rolling horizon algorithm. Experimental results show that our method can provide competitive recovery solution in both solution quality and computation time.published_or_final_versio

Allocating Air Traffic Flow Management Slots

In Europe, when an imbalance between demand and capacity is detected for air traffic network resources, Air Traffic Flow Management slots are allocated to flights on the basis of a First Planned First Served principle. We propose a market mechanism to allocate such slots in the case of a single constrained en-route sector or airport. We show that our mechanism provides a slot allocation which is economically preferable to the current one as it enables airlines to pay for delay reduction or receive compensations for delay increases. We also discuss the implementation of our mechanism through two alternative distributed approaches that spare airlines the disclosure of private information. Both these approaches have the additional advantage that they directly involve airlines in the decision making process. Two computational examples relying on real data illustrate our findings.Air Transportation, Market Mechanism Design, Air Traffic Flow Management slots, Collaborative Decision Making, SESAR.

Data-driven modeling of systemic delay propagation under severe meteorological conditions

Trabajo presentado en el 10th USA/Europe Air Traffic Management Research and Development Seminar (2013), celebrado en Chicago del 10 al 13 de junio de 2013.The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large- scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.PF receives support from the network Complex World within the WPE of SESAR (Eurocontrol and EU Commission). JJR acknowledges funding from the Ramón y Cajal program of the Spanish Ministry of Economy (MINECO). Partial support from MINECO and FEDER was received through projects MODASS (FIS2011-24785), FISICOS (FIS2007-60327) and INTENSE@COSYP (FIS2012-30634). Funding was also received from the EU Commission through projects EUNOIA FP7-DG.Connect-318367) and LASAGNE (FP7-ICT-318132).Peer reviewe

다중공항에서 지상 지연 프로그램 발생시 지연전파를 고려한 항공사의 운항 일정 변경

학위논문(석사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 문일경.본 연구의 목적은 항공 교통을 제어하는 중요한 수단 중 하나인 지상 지연 프로그램(GDP)이 발생할 경우 공항의 변경된 수용력에 대응하도록 항공사의 관점에서 항공편을 재조정하는데 도움을 주는 것이다. 단일 공항이 아닌 다중 공항으로 확장하여 동일한 공항뿐 아니라 다른 공항으로부터의 지연 전파를 고려했으며, 항공기 및 승무원의 계획된 일정에서 발생하는 현실적인 비용을 포함했다. GDP가 발행되면 항공사들은 변경된 시간대에 맞춰 항공편을 재조정할 수 있는 짧은 시간이 주어진다. 각 공항에는 수용력이 있으며, 특히 들어오는 항공기를 수용할 수 있는 용량인 공항 수용률(AAR)이 있다. 이 연구에서 비행 스케줄을 재조정하기 위해 혼합 정수 선형 프로그래밍 모델을 세웠다. 또한, 미래의 불확실성을 다루기 위해, MILP의 두 가지 버전을 사용하였다. AAR이 어느 시점에 다시 바뀌는 시나리오를 만든 후, 각 시나리오 별로 총 관련 비용을 최소화하는 솔루션을 도출하는 최적 모델과 모든 시나리오 솔루션의 총 관련 비용의 기댓값을 최소화하는 솔루션을 도출하는 추계 모델을 제시하고 서로 비교하였다The purpose of this thesis is to reschedule flights from the airline company’s perspective to correspond to the airport’s changed capacity in the event of a ground delay program (GDP), one of the important means of controlling air traffic. We considered delay propagation not only within the same airport but within other airports by extending the setup to include several airports rather than a single airport. We also included realistic costs from planned schedules of the aircraft and crew. When a GDP is issued, airlines are given a short time to reschedule flights in time for the changed slot. Each airport has its own capacity, especially the airport acceptance rate (AAR), which is a capacity that can accommodate incoming aircraft. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. To handle the uncertainty of future scheduling, two versions of the MILP model may be applied. With scenarios in which the AAR changes again, an optimal model that obtains a minimizing total relevant cost in each scenario solution and a stochastic model solution that obtains a minimizing expectation of the total relevant cost of all scenarios are presented and compared.Chapter 1 Introduction 1 Chapter 2 Literature review 3 Chapter 3 Mathematical model 5 3.0 Model description 5 3.1 Multi-airport Scenario-based Optimal Rescheduling Problem 10 3.2 Multi-airport Scenario-based Stochastic Rescheduling Problem 13 Chapter 4 Computational experiments 14 4.0 Settings 14 4.1 Experiment 1 16 4.2 Experiment 2 18 4.3 Experiment 3 19 4.4 Experiment 4 20 Chapter 5 Conclusions 25 Appendix 27 Appendix A. 27 Appendix B. 28 Bibliography 31 국문초록 35석