Airport Slot Research Articles

Progress and challenges in airport slot allocation

Most of Level 3 airports worldwide face severe congestion and flight delays. Optimal allocating airport slots is one of the most effective ways in airport demand management. Here we systematically review the progress of research in airport slots allocation, as well as the main techniques in setting airport declared capacity and allocating slots.

On-demand airport slot management: tree-structured capacity profile and coadapted fire-break setting and slot allocation

We propose an on-demand airport slot management (ODASM) approach to guide slot fire-break capacity setup and airport slot allocation. The ODASM consists of a tree-structured capacity profile and a coadapted fire-break setting and slot allocation model. The tree-structured capacity profile that is constructed using a decision tree aims to capture various fire-break capacity settings and their corresponding delays. It provides diversified fire-break schemes and delay references in the airport slot allocation process. The coadapted fire-break setting and slot allocation model are able to generate good coadaptation. That is, the fire-breaks are set to adapt to the preferences of airlines' requests to minimise the total displacements, and the airlines' requests are modified to adapt to the fire-breaks to satisfy a predetermined delay level. The approach has been tested at Shanghai Pudong Airport, and the results suggest that the ODASM can generate a moderate level of rescheduling while resulting in the expected delay that does not exceed a given acceptable delay level. It has been demonstrated that the ODASM can offer substantial advantages in contrast to a classic slot allocation method where the capacity are set as the maximum of the declared capacity. Moreover, due to the refined relationship between fire-break configuration and delay, the computation process of slot optimisation only takes a few minutes, which is completely viable for the application. This approach is compatible with the current slot guidelines while showing significant potential to improve the collaboration among stakeholders in slot management.

The on-time performance effects of a policy change in airport slot restrictions: A synthetic control analysis

This study examines the on-time performance (OTP) effects of the 2016 Federal Aviation Administration’s decision to remove takeoff and landing limits at Newark Liberty Airport. Using the synthetic control method, we compare Newark Liberty’s OTP to that of a weighted collection of comparable airports in the United States. Exploiting the regulatory change as a credibly exogenous shock to arrival OTP, we find that arrival delay minutes increased by up to 23 min from an average of 8 min, while the share of late-arriving flights increased by up to 14 percentage points. These results are robust to other measures of arrival OTP.

A Multi-Objective Genetic Algorithm for Airport Slot Allocation Decision-Making

A Multi-Objective Genetic Algorithm for Airport Slot Allocation Decision-Making

A Mechanism for Bilateral Airport Slot Trading in the Secondary Market

A Mechanism for Bilateral Airport Slot Trading in the Secondary Market

The Analysis of Slot Optimization Based on the Top Trading Cycle Method in China Hub Airport

China's aviation industry is facing a scarcer development space. In the absence of means to enhance airport capacity, airport slots have been attached great importance as a scarce resource. Due to the lagging development of China's aviation industry and its detailed fields, slot allocation in China is unscientific, and as a scarce resource, slot utilization in China has much room for improvement. After analyzing the current situation of China's slot allocation system, imperfect laws, regulations and slow marketization process, this paper proposes that the construction of the secondary market can be a substitute for China in the process of exploring the primary market mode, which can be used to significantly improve the utilization and efficiency of slot resources temporarily. This paper introduces in detail the operation method of the Top Trading Cycle (TTC) algorithm and the Top Trading Cycle-Collaborative Decision Making (TTC-CDM) algorithm in the slot allocation secondary market and the short-term potential of solving slot resource shortage, which provides directional guidance for the development space of China's aviation industry.

Research on the TTC-CDM Algorithm in Collaboration Decision Making of the Airport Slot

With the growing of the globalization, the civil aviation grew in popularity, the air transport industry has witnessed significant growth driven by advancements in science and technology. This progress has also led to the emergence of resource allocation inefficiencies at many airports. Apart from building more runways and airports, lifting the efficiency of existing capacities is a much more cost-effective strategy, where Top Trading Cycle (TTC) algorithm is a prospective solution. This studys application of TTC to the slot allocation problem is different from conventional TTC. Conventional Top Trading Cycle considers preferences of unilateral agents. In this study, there are two different kinds of agents that have their own preferences, airlines with flights and airport managers owning the slots. Therefore, this research's objective is to implement TTC to the slot allocation while respecting preferences of both sides, TTC-CDM algorithm would be useful. This study discussed this algorithm by performing a case study with modelling situation of airport slot allocation in a particular moment. Ultimately, the result of this case study is Pareto efficient.

Optimal network-wide adjustments of initial airport slot allocations with connectivity and fairness objectives

Due to serious demand-supply imbalances, many airports around the world are highly congested. Access to these highly congested (Level 3, coordinated) airports is controlled through the use of the IATA World Airport Slot allocation Guidelines (WASG). At an individual airport, slots requested by each airline are allocated at the airport under consideration independently without taking into account the interactions between slots allocated at different airports. However, in order for the air-transport network to operate seamlessly, ensuring network-wide connectivity of flights, and the interdependencies existing between the slots allocated at individual airports need to be considered. Several models have been proposed in the literature to deal with the optimum allocation of slots at a single airport. However, the literature currently does not adequately address the network-wide slot allocation problem. In this paper, we are introducing a novel approach to address the network-wide slot allocation problem. Our approach considers as an input the individual airport schedules generated during the slot allocation process at individual airports and optimally adjusts them to ensure network-wide flight connectivity by taking into account the interdependencies existing between flights connecting pairs of airports.To this end, we propose bi-objective mathematical models, which consider schedule efficiency and inter-airline fairness objectives, and incorporate the importance that different airports have for the functioning of the air transport network, using the IATA connectivity indices and the betweenness centrality measures. We solve the proposed models using the ε − constraint method to investigate trade-offs between network-wide schedule efficiency and fairness, and we investigate the effect of these trade-offs on the airlines and the airports. Results from the application of the proposed models to a test network suggest that the consideration of the contribution of the airports to network connectivity affect the way that the total network-wide schedule displacement is distributed among the airports. Specifically, we found that the use of the IATA connectivity index tends to allocate less schedule displacement to airports with frequent flights to many destinations, while the use of the betweenness centrality measure allocates less schedule displacement to airports that are more critical in ensuring the connectivity of other airports in the network.

Using multi-criteria performance measurement models to evaluate the financial, operational and environmental sustainability of airlines

Airline sustainability, defined as both decarbonization and long-term financial survival, has become critical especially since the onset of the COVID-19 pandemic. Our paper investigates the multi-dimensional effects of the COVID-19 crisis on airlines’ sustainability performance using data from 56 airlines for the period before, during and early after the pandemic (2017–2021). We develop and use a novel multi-criteria MEREC–CoCoSo/Borda performance measurement model tailored to the airline industry. Our results suggest that while operational results have dominated sustainability weights throughout the period, the financial pillar has gained considerably in importance while the decarbonization criterion decreased in weight in 2020. However, from 2021, decarbonization started to become more important again in the “new normal” and sector recovery. In terms of overall and sustained sustainability, low-cost carriers and small full-service carriers with predominantly domestic networks are ranked as best performers. Renewing fleet, attaching decarbonization conditions to government aid and reviewing airport slot policies are important to prepare the aviation industry for the next pandemic or disruption.

Reassigning Departure Slots with Preferences of the Airline and Passengers

The recovery of a departure flight is an important part of airline disruption management. The commonly used optimization objectives for assessing airport slot-scheduling efficiency are delay cost and delay time. However, the current literature does not consider the preferences of airlines and passengers in the recovery process simultaneously. The objective of this paper is to focus on the departure slot reassignment problem and develop a bi-objective optimization model. We introduce a metric for the price of fairness and formulate the airport slot scheduling problem as a bi-objective optimization model that considers the trade-off between the total airline delay cost and total passenger delay time. To quantify the trade-off between total airline delay costs and total passenger delay time, we apply the metric of the price of fairness in the bi-objective model, treating the total airline delay cost as an efficiency metric and the total passenger delay time as a fairness metric, calculating the price of fairness for each Pareto solution. An adaptive non-dominated sorting genetic algorithm-II based on dominant strengths (ANSGA2-DS) is developed to solve this problem. More precisely, three improved operations are presented: the improved fast dominant strength sorting method, new crowding distance improvement, and an adaptive elitist retention strategy. Three scenarios derived from a Chinese airline’s operation data are applied to the proposed bi-objective model and algorithm. The experimental findings demonstrate that the proposed model and method can effectively and efficiently address the problem. This may provide a basis for airline operation controllers to achieve a generally acceptable solution.

Slot allocation for a multiple-airport system considering airspace capacity and flying time uncertainty

Prior research on slot allocation has focused on a single airport, with little attention paid to the multiple-airport systems (MAS) that consist of at least two major airports. Scheduled flights at different airports may have conflicts regarding shared fixes (i.e., route points) or routes, thus causing airspace congestion and flight delays. Traffic demand at a critical fix depends on both the departure/arrival time of the flights and the flying times between the airport and the fix, whereas flying times exhibit a stochastic nature due to various factors such as air traffic control strategies, aircraft performance, and weather. In this paper, we develop a chance-constrained slot allocation model for an MAS that optimizes slot allocation for multiple airports while considering fix capacity constraints. To capture the uncertainty of flying times, stochastic chance constraints are formulated and a scenario generation method is proposed to solve the model. We apply our model to allocate slots in the MAS of Guangdong–Hong Kong–Macao Greater Bay area. The results show that the schedules generated by the proposed model outperform those from the certainty model and the original schedules. Traffic flow at critical fixes is more robust to various operating scenarios with the cost of a small number of increased slot displacements. Our findings highlight the importance of flying time uncertainty in allocating slot and airspace capacity, and the proposed model provides a useful tool for slot coordinators seeking to effectively manage airport slots in an MAS.

Multi-objective airport slot scheduling incorporating operational delays and multi-stakeholder preferences

Airport Slot Allocation (ASA) is a multi-objective, multi-stakeholder decision process that aims to maximise airport capacity utilisation and mitigate delays. Existing ASA studies have considered objectives associated with the slot scheduling process per se without considering the implications of the alternative schedules, represented by the generated efficient frontier, on the operational delays, i.e., the delays that will be encountered when a selected airport schedule will be implemented. Furthermore, one notes the absence of an integrated methodology that incorporates the preferences of all ASA stakeholders/experts in selecting the most preferable airport schedule. This gap becomes more evident when considering the dearth of empirical data with respect to ASA scheduling performance metrics. The ASA framework proposed herein taps the above gaps by considering operational delays and eliciting airport slot schedules through the integration of multi-stakeholder preferences. Computational results using empirical preference data demonstrate the decision-support in determining the most preferable airport slot scheduling solution (or solutions) and its implications on expected delays.

Optimizing airport slot scheduling problem using optimization algorithms.

The primary objective of airport managementworldwide is always to make it easier to provide transportation services and minimize latency. This could be accomplished by controlling the movement of travelers through the airport's different checkpoints for passports, baggage handling, customs, and bothdeparture and arrival lobbies. As one of the biggestpassenger terminals around theworld and among the most attractive destinations for visitors during the Hajj pilgrimage, this paper concentrates on enhancingthe movement of travelersin the King Abdulaziz International Airport's pilgrimage stationin the Kingdom of Saudi Arabia. Several optimization methods are used to better schedule the phases within the airport terminals as well as the assignment of arriving flights to vacant airport portals. These include the differential evolution algorithm (DEA), harmony search algorithm, genetic algorithm (GA), flower pollination algorithm (FPA), and black widow optimization algorithm. The findings demonstrated the potential sites for the development of airport stages, which may assist decision-makers in improving operational efficiency in the future. The simulation results showed that GA was more efficient in most of the experiments than the alternative algorithms for small population sizes in terms of the quality of the solutions obtained and the convergence rates. In contrast, DEA performed better in the larger population sizes. The outcomes also showed that FPA performed better than its rivals in identifying the optimal solution in terms of the overall duration of passenger waiting time.

Bi-objective airport slot scheduling considering scheduling efficiency and noise abatement

This paper aims to explore the contribution of slot allocation to noise abatement at schedule coordinated airports. To this end, we first build a single-airport bi-objective model that incorporates scheduling efficiency and noise reduction. We then find and analyze a solution that maximizes airport noise reduction with the greatest allocation efficiency. The price of noise reduction is proposed to gain the trade-off between efficiency and environmental benefits. Our research shows that slot scheduling can reduce noise at Shanghai Hongqiao International Airport. The maximum noise reduction schedule shows high fluctuation in the temporal distribution of slots. The price of noise reduction is very high under the maximum noise reduction schedule, and the required displacement to reduce one unit of noise increases sharply as the noise reduction value rises. Besides, the airport capacity expansion plays an essential role in reducing the price of noise abatement.

Market-Based Allocation of Airport Slots: The PAUSE Auction Mechanism and Extensions

Market-Based Allocation of Airport Slots: The PAUSE Auction Mechanism and Extensions

Market-Based Allocation of Airport Slots: The PAUSE Auction Mechanism and Extensions

Market-Based Allocation of Airport Slots: The PAUSE Auction Mechanism and Extensions

Algorithmic mechanism design for egalitarian and congestion-aware airport slot allocation

We propose a game-theoretic model and a mechanism design solution to allocate slots fairly at congested airports. This mechanism: (a) ensures that the slots are allocated according to the true valuations of airlines, (b) provides fair opportunities for flights connecting remote cities to large airports, and (c) controls the number of flights in each slot to minimize congestion. Drawing inspiration from economic theory, this mechanism allocates the slots based on an affine maximizer allocation rule and charges payments to the airlines to incentivize them to participate in the allocation process and reveal their actual valuations. The allocation also optimizes the occupancy of each slot to keep them as uncongested as possible. The formulation solves an optimal integral solution in strongly polynomial time. We conduct experiments on the data collected from two primary airports in India. We also compare our results with existing allocations and an allocation based on the International Air Transport Association (IATA) guidelines. The computational results show that our mechanism is more egalitarian and generates 20%−30% higher social utility than the IATA based state-of-the-art approach and current allocations.

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules several months in advance; thus, predicting flight delays at the strategic stage is critical for airport slot allocation and airlines’ operation. However, less work has been dedicated to predicting flight delays at the strategic phase. This paper proposes machine learning methods to predict the distributions of delays. Three metrics are developed to evaluate the performance of the algorithms. Empirical data from Guangzhou Baiyun International Airport are used to validate the methods. Computational results show that the prediction accuracy of departure delay at the 0.65 confidence level and the arrival delay at the 0.50 confidence level can reach 0.80 without the input of ATFM delay. Our work provides an alternative tool for airports and airlines managers for estimating flight delays at the strategic phase.

Incorporating slot valuation in making airport slot scheduling decisions

Airport Slot Allocation (ASA) seeks to manage scarce airport resources by matching airline requests to airport slots. The ASA process is carried out by coordinators, who prioritise airline requests through the consideration of policy rules that aim to serve airlines and the travelling public, improve airport capacity utilisation, connectivity, and competition. Even though the ASA involves both airlines and coordinators, existing studies model the ASA without considering the interactions between airlines’ preferences and the rules and priorities that are considered by the coordinators. This paper introduces an ASA problem variant in which the assignment of airport slots is modelled by considering the interrelation between airlines’ preferences and coordinators’ prioritisation, expressed through time-dependent valuation functions. Through the development of a Mixed Integer Programming Model (MIP) and a preference-based algorithm that integrate the developed valuation functions, we propose request-to-slot assignments that are Pareto optimal per se, ergo guaranteeing that airlines and coordinators have no incentive to reject or alter the proposed schedules. Computational results demonstrate the ability of the proposed MIP and algorithm in supporting coordinators on decisions relating to which requests will be displaced or rejected so as to achieve improved capacity utilisation.

The effects of airport slot allocation method on competition: Empirical analysis of competition through slots at JFK International Airport

Purpose- This study aims to indicate the effects of the current slot allocation method specified in the IATA Worldwide Slot Guidelines, which is used to allocate airport slots to airlines, on airline transport market concentration and competition. Methodology- John F. Kennedy Airport (JFK), which allocates slots to airlines with the IATA method, was taken as a sample in the study. Airline’s JFK slot data obtained from the Federal Aviation Administration's Slot Administration Office were analysed with the Herfindahl-Hirschman Index (HHI) and the n-Firm Concentration Ratio (CRn). Findings- According to the findings obtained from the analyses, it has been stated that the concentration in the JFK International Airport market is high and the competition is low. It has been determined that the majority of the slots are owned by 3 long-established US airlines using the airport as their base airport, and the remaining slots are shared by many airlines. Conclusion- As a result of the study, it is revealed how the historical rights and pool system in the current slot allocation method affect concentration, competition, new entrants to the market and the power of new entrants to increase their shares. Keywords: Airport slot, market concentration, competition, Herfindahl-Hirschman, CRn JEL Codes: B22, D40, L93