Search Results (15)

The terminal maneuvering area (TMA) serves as a critical transition zone between upper enroute airways and airports, representing one of the most complex regions for managing high volumes of arrival and departure traffic. This paper presents the multi-route TMA scheduling problem as an optimization challenge aimed at optimizing TMA interventions, such as rerouting, speed control, time-based metering, dynamic minimum time separation, and holding procedures; the objective function minimizes schedule deviations and the accumulated holding time. Furthermore, the problem is formulated as a mixed-integer linear program (MILP) to facilitate finding solutions. A rolling horizon control (RHC) dynamic optimization framework is also introduced to decompose the large-scale problem into manageable subproblems for iterative resolution. To demonstrate the applicability and effectiveness of the proposed scheduling models, a hub airport—Chengdu Tianfu International Airport (ICAO code: ZUTF) in the Cheng-Yu Metroplex—is selected for validation. Numerical analyses confirm the superiority of the proposed models, which are expected to reduce aircraft delays, shorten airborne and holding times, and improve airspace resource utilization. This study provides intelligent decision support and engineering design ideas for the macroscopic-level collaborative optimization framework of the Integrated Arrival–Departure and Surface (IADS) system. Full article

Urban Air Mobility (UAM) has high potential as an ecofriendly transportation mode that can alleviate traffic congestion on the ground and reduce travel times by utilizing three-dimensional airspace. However, efficient vertiport operational plans are needed for UAM to become an accessible transportation mode for the public. In this study, the numerical analysis program MATLAB (R2023a) and the traffic simulation software VISSIM (PTV VISSIM 2024) were used to model vertiport operations and analyze the on-ground traffic environment, including vertiport capacity and UAM aircraft delays. Additionally, on-time performance was considered by applying uncertainties to the intervals between consecutive generations and the turnaround time to simulate situations where UAM aircraft cannot adhere to their scheduled arrival and departure times. Operational scenarios were developed by varying the interval time between UAM aircraft generated in the simulation (3–10 min) in two cases: (1) without considering the on-time performance and (2) considering the on-time performance. This study aimed to maximize vertiport capacity and minimize UAM aircraft delay times. In addition, the reduction of delay times and improvement of turnaround efficiency directly contribute to sustainable urban airspace management by lowering ground energy use and environmental impact. In Case 1, the vertiport was most efficient at an interval time of 7 min. In Case 2, capacity was maximized at an interval time of 6–7 min while delay times were minimized at an interval time of 8–10 min. The simulation results provide valuable insights for developing not only efficient but also environmentally responsible vertiport operational plans, contributing to the successful and sustainable implementation and scalability of UAM systems. Full article

Integrated arrival–departure and surface scheduling (IADS) is a critical research task in next-generation air traffic management that aims to harmonize the complex and interrelated processes of airspace and airport operations in the Metroplex. This paper investigates the microscopic-level collaborative optimization framework for IADS operations, i.e., the problem of coordinating aircraft scheduling on runways and taxiways. It also describes the mixed-integer linear programming (MILP) bi-layer decision support for solving this problem. In runway scheduling, a combined arrival–departure scheduling method is introduced based on our previous research, which can identify the optimal sequence of arrival and departure streams to minimize runway delays. For taxiway scheduling, the Multi-Route Airport Surface Scheduling Method (MASM) is proposed, aiming to determine the routes and taxi metering for each aircraft while minimizing the gap compared with the runway scheduling solution. Furthermore, this paper develops a feedback mechanism to further close the runway and taxiway schedule deviation. To demonstrate the universality and validity of the proposed bi-layer decision support method, two hub airports, Chengdu Shuangliu International Airport (ICAO code: ZUUU) and Chengdu Tianfu International Airport (ICAO code: ZUTF), within the Cheng-Yu Metroplex, were selected for validation. The obtained results show that the proposed method could achieve closed-loop decision making for runway scheduling and taxiway scheduling and reduce runway delay and taxi time. The key anticipated mechanisms of benefits from this research include improving the efficiency and predictability of operations on the airport surface and maintaining situational awareness and coordination between the stand and the tower. Full article

The benchmarking of CO₂ emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO₂ emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. Full article

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account. Full article

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. Full article

Extensive research in predicting annual passenger throughput has been conducted, aiming at providing decision support for airport construction, aircraft procurement, resource management, flight scheduling, etc. However, how airport operational throughput is affected by convective weather in the vicinity of the airport and how to predict short-term airport operational throughput have not been well studied. Convective weather near the airport could make arrivals miss their positions in the arrival stream and reduce airfield efficiency in terms of the utilization of runway capacities. This research leverages the learning-based method (MB-ResNet model) to predict airport hourly throughput and takes Hartsfield–Jackson Atlanta International Airport (ATL) as the case study to demonstrate the developed method. To indicate convective weather, this research uses Rapid Refresh model (RAP) data from the National Oceanic and Atmospheric Administration (NOAA). Although it is a comprehensive and powerful weather data product, RAP has not been widely used in aviation research. This study demonstrated that RAP data, after being carefully decoded, cleaned, and pre-processed, can play a significant role in explaining airfield efficiency variation. Applying machine learning/deep learning in air traffic management is an area worthy of the attention of aviation researchers. Such advanced artificial intelligence techniques can make use of big data from the aviation sector and improve the predictability of the national airspace system and, consequently, operational efficiency. The short-term airport operational throughput predicted in this study can be used by air traffic controllers and airport managers for the allocations of resources at airports to improve airport operations. Full article

This paper demonstrates the effectiveness of the Extended First-Come, First-Served (EFCFS) scheduler for integrated arrival and departure scheduling by comparing the scheduling results with the recorded operational data at Incheon International Airport (ICN), Republic of Korea. The EFCFS scheduler can handle multiple capacity- or flow-rate-related constraints along the path of each flight, which is represented by a node–link graph structure, and can solve large-scale problems with low computational cost. However, few studies have attempted a systematic verification of the EFCFS scheduler by comparing the scheduling results with historical operational data. In this paper, flights are scheduled between gates and runways on the airport surface with detailed constraints such as runway wake turbulence separation minima and conflict-free taxiing. The scheduler is tested using historical flight data from 15 August 2022 at ICN. The input schedule is generated based on the flight plan data extracted from the Flight Operation Information System (FOIS) and airport surface detection equipment data, and the results are compared with the times extracted from the FOIS data. The scheduling results for 500 aircraft show that the average takeoff delay is reduced by about 19 min, while the average landing delay is increased by less than one minute when the gate occupancy constraint is not considered. The results also confirm that the EFCFS effectively utilizes the available time slots to reduce delays by switching the original departure or arrival orders for a small number of flights. Full article

The aircraft ferry service is an important link in the transfer of travelers between the remote parking stand and the terminal. By analyzing the process of inbound and outbound ferry service, the relationship between the in-place time of each ferry on the same flight is clarified. A ferry service splitting method considering the difference in in-place time is proposed. Based on this, a dynamic programming scheduling model for ferries is innovatively developed. Considering the impact of flight arrival and departure uncertainty on the in-place time of ferry service, the model is transformed into a dynamic programming stochastic model with opportunity constraints. The optimization objective of this model is to minimize the number of ferries used. To describe the model more easily, a sample average approximation technique is introduced to transform the stochastic model into a deterministic model with confidence. A genetic algorithm based on Monte Carlo random sampling is designed to solve the model. The experimental results based on the actual operation data of an airport in South China show that the dynamic splitting method considering the difference in the in-place time of the ferry can improve the resource utilization efficiency; the scheduling scheme based on the dynamic planning stochastic model has better robustness. Full article

Managing aircraft turnaround is a complex process due to various factors, including passenger handling. Airport ground handling, resource planning, optimal manpower, and equipment utilisation are some cost-cutting strategies, particularly for airlines and ground handling service teams. Scheduled aircraft arrival and departure times are critical aspects of the entire ground management and passenger handling process. This research aimed to optimise airport ground resource allocation for multiple aircraft using machine learning-based prediction methodologies to enhance the prediction of aircraft arrival time, an uncontrollable variable. Our proposed models include a multiple linear regression (MLR) model and a multilayer perceptron (MLP)-based model, both of which are used for predicting round-trip arrival times. Additionally, we developed a MLP-based model for multiclass classification of arrival delays based on departure time and delay from the same airport. Under normal weather conditions and operational scenarios, the models were able to predict round-trip arrival times with a root mean squared error of 8 min for each origin–destination pair and classify arrival delays with an average accuracy of 93.5%. Our findings suggest that machine learning-based approaches can be used to predict round-trip arrival times based on the departure time from the same airport, and thereby accurately estimate the number of actual flight movements per hour well in advance. This predictability enables optimised ground resource planning for multiple aircraft based on constrained airport resource deployment and utilisation. Full article

Decision support tools for arrival sequencing and scheduling could assist air traffic controllers in managing the arrival aircraft in terminal areas. However, one critical issue is that the current method for dealing with the arrival sequencing and scheduling problem does not consider the dynamic traffic situation and the human working experience, which results in a deviation between the scheduled and actual landing sequences. This paper develops a data-driven method to address this issue. Firstly, the random forest model is applied to predict the estimated time of arrival (ETA). During the ETA prediction, the trajectory, operation, and airport-related factors that could increase the prediction accuracy are considered. Secondly, the landing sequence is obtained by sorting the predicted ETAs. Thirdly, two optimization methods are proposed to generate the scheduled time of arrival (STA). The former uses the predicted ETAs as inputs and then directly optimizes the landing sequence and the STA. The latter uses both the predicted ETA and the landing sequence as inputs for further optimization. Finally, these proposed methods are evaluated with three sets of historical data on arrival operations at Changsha Huanghua International Airport (ZGHA). The results show that the RF-based ETA prediction method could improve scheduling performance. Moreover, the proposed optimization methods could provide controllers with a more appropriate decision advisory. Such advisories could simultaneously reduce the operation efficiency indicators (average/maximum delay or dwell time) and the operation complexity indicators (Kendall rank correlation or position shift). Full article

Gate allocation has always been a fundamental but critical issue in the daily operation of airports, which is related to service quality and schedule efficiency. In order to obtain reasonable and efficient gate allocation results, in this paper, a multi-commodity network flow model is proposed to describe the gate allocation process in flight flow, based on which a multi-objective optimization model is constructed. It not only comprehensively considers the flight information of aircraft arrivals and departures, but also integrates the broader interests of passengers, airlines, and airports. To solve it, a linear weighting technique is applied. In addition, K-means cluster analysis is used to explore different weight combinations, and on this basis, the idle time of the gate is introduced as a performance evaluation index to guide the selection of the final weight. By analyzing the optimization results of actual operation data, the proposed model significantly balances the interests of multiple parties and the number of flights at each gate and has a relatively high gate-utilization rate. It can provide rich decision support and a reasonable allocation scheme for airport management to a certain extent. Full article

The Aircraft Arrival/Departure Problem (AADSP) is the core problem in current runway system, even has become the bottleneck to prevent the improvement of the airport efficiency. This paper studies the single runway AADSP. A Mixed Integer Programming (MIP) model is constructed and an algorithm named Ant Colony based on Rank 2 Matrix Approximation (RMA-AC) method is proposed. Numerical results validate that the new algorithm, as well as the new model, exhibits better performance than CPLEX and the traditional two-phase algorithm. The runway efficiency enhanced by RMA-AC, within 20 s computation, is about 2–5% even for the 800 aircraft sequences. It is a promising method to improve the efficiency of the future aircraft scheduling system. Full article

There has been a lot of research on flight delays. But it is more useful and difficult to estimate the departure delay time especially three hours before the scheduled time of departure, from which passengers can reasonably plan their travel time and the airline and airport staff can schedule flights more reasonably. In this paper, we develop a Spatio-temporal Graph Dual-Attention Neural Network (SGDAN) to learn the departure delay time for each flight with real-time conditions at three hours before the scheduled time of departure. Specifically, it first models the air traffic network as graph sequences, what is, using a heterogeneous graph to model a flight and its adjacent flights with the same departure or arrival airport in a special time interval, and using a sequence to model the flight and its previous flights that share the same aircraft. The main contributions of this paper are using heterogeneous graph-level attention to learn the influence between the flight and its adjacent flight together with sequence-level attention to learn the influence between the flight and its previous flight in the flight sequence. With aggregating features from the learned influence from both graph-level and sequence-level attention, SGDAN can generate node embedding to estimate the departure delay time. Experiments on a real-world large-scale data set show that SGDAN produces better results than state-of-the-art models in the accurate flight delay time estimation task. Full article

The flight rescheduling problem is one of the major challenges of air traffic issue. Unforeseen bad weather conditions stimulate air traffic congestion and make the initial scheduling infeasible, resulting in significant economic losses for passengers and airlines. Furthermore, due to rigorous environmental legislations, flight rescheduling becomes a more complicated problem, as it has to deal with flight delays on the one hand, and carbon emissions on the other hand. In this paper, we address the flight rescheduling problem with an environmental requirement subject to the air capacity limitation due to bad weather conditions. A new strategy is proposed to minimize the disruption effects on planned flights, which adopted ground delay, longer route change, flight cancellation, as well speed adjustment to arrive at a scheduled time. Firstly, the objective of this study is to determine the economical flights plan in line with the new available air capacity. Secondly, by considering the environmental impact of the kerosene consumption, we illustrate the contribution of an economical decision to aircraft emissions. Experiment results are provided to show the efficiency of the proposed strategies and genetic algorithm as the used optimization method. Furthermore, the impacts of carbon tax and cost of arrival delay on the flights carbon emissions are studied. Full article