Search Results (89)

Departure punctuality (KPI01) and ATFM slot adherence (KPI03) have been emphasized by the International Civil Aviation Organization as key performance indicators (KPIs) in the Global Air Navigation Plan. To address the inherent conflict between these two objectives in departure scheduling, a multi-objective optimization model is proposed that aims to simultaneously enhance departure punctuality, ATFM slot adherence, and taxiing efficiency. A simulated annealing algorithm based on a resource transmission mechanism was developed to solve the model effectively. Based on full-scale operational data from Nanjing Lukou International Airport in June 2023, the empirical results confirm the model’s effectiveness in two primary dimensions: (1) Significant improvement in taxiing efficiency: The average unimpeded taxi-out time was reduced by 6.4% (from 17.2 to 16.1 min). The number of flights with taxi-out times exceeding 30 min decreased by 58%. For representative taxi routes (e.g., stand 118 to runway 6), the excess taxi-out time was reduced by 82.3% (from 5.61 to 1.10 min). (2) Enhanced operational punctuality: Departure punctuality improved by 10.7% (from 67.9% to 78.7%), while ATFM slot adherence increased by 31.2% (from 64.6% to 95.8%). This study presents an innovative departure scheduling approach and offers a practical framework for improving collaborative operational efficiency among airports, air traffic management units, and airlines. Full article

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

By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at the same waypoint can be avoided by simultaneously assigning an optimal route to each flight between the airport and waypoint and determining its practical departure time. Furthermore, several real-world constraints, including the safe interval between any two aircraft at the same waypoint and the maximum allowable delay for each flight, have been incorporated into the proposed model. The primary objective is minimization of both total carbon emissions and delay times for all flights across all airports. A feasible set of non-dominated solutions were obtained using a two-stage heuristic approach-based NSGA-II. Finally, we present a case study of four airports and three waypoints in the Beijing–Tianjin–Hebei region of China to test our study. Full article

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

The goal of this paper is to optimize mission schedules for vertical airports (vertiports in short) to satisfy the different needs of stakeholders. We model the problem as a resource-constrained project scheduling problem (RCPSP) to obtain the best resource allocation and schedule. As a new approach to solving the RCPSP, we propose answer set programming (ASP). This is in contrast to the existing research using MILP as a solution to the RCPSP. Our approach can take complex scheduling restrictions and stakeholder-specific requirements. In addition, we formalize and include stakeholder needs using a knowledge representation and reasoning framework. Our experiments show that the proposed method can generate practical schedules that reflect what stakeholders actually need. In particular, we show that our approach can compute optimal schedules more efficiently and flexibly than previous approaches. We believe that this approach is suitable for the dynamic and complex environments of vertiports. Full article

An airport slot is the core resource in the air transportation system. In most busy airports in China, airline demand significantly exceeds the available slot capacity. Scientific and reasonable slot allocation techniques and methods can improve the operational efficiency and benefits of multi-airport systems. Existing research has predominantly addressed slot allocation optimization for individual airports; however, there are differences in the functional positioning and resource allocation during multi-airport slot optimization, which makes cooperative optimization in the context of multi-airport slot allocation difficult. The dynamic sharing of airspace capacity in multi-airport systems is crucial for optimizing airport slot allocation and improving resource utilization efficiency. This study develops a multi-objective optimization model incorporating coupled airspace capacity relationships within multi-airport systems and the fairness of airlines and airports in order to realize the optimal utilization of multi-airport system resources, considering specialized 24 h airport slot coordination parameter patterns and slot firebreaks in China. Finally, the validity and scalability of the model are verified using real flight data from three airports in the Beijing airport terminal area, and simulations are conducted to verify the model. The findings provide a solid reference for the optimization of airport slot timetables in multi-airport systems, having both important theoretical value and practical significance. Full article

Airline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the challenge of introducing a new route from a mega-hub to a new destination, while maintaining the existing flight network and leveraging arrivals from spoke airports to ensure connectivity. First, a mixed-integer nonlinear mathematical model was formulated to produce a global optimal solution at a lower time granularity, but it became computationally intractable at higher granularities due to the exponential growth in constraints and variables. Second, a genetic algorithm (GA) was employed to demonstrate scalability and flexibility, delivering near-optimal, high-granularity schedules with significantly reduced computational time. Empirical validation using real-world data from 37 spoke airports revealed that, while the exact model minimized waiting times and maximized profit at lower granularity, the GA provided nearly comparable profit at higher granularity. These findings guide airline managers seeking to optimize passenger connectivity and cost efficiency in competitive global markets. Full article

The Impact Monitor Project is a European initiative designed to develop an impact assessment toolbox and framework, targeting the European aviation sector. The proposed framework is not only aimed at the environment, economics, and operations but also the societal impacts of new technologies and aircraft configurations. The toolbox works by setting out the key steps in the impact assessment cycle and presenting guidance, tips, and best practices. Led by DLR, the consortium includes research institutions and universities that have contributed their expertise and tools to develop the collaborative assessment toolbox and framework. The project defines three use cases by considering three assessment levels: aircraft, airport, and air transport system. This article focuses on Use Case 2 on continuous descent operations (CDOs) at the aircraft and airport levels. It describes the workflow proposal, along with the tools involved. The collaborative approach showcases integrating these tools and using collaborative strategies enabled by CPACS (Common Parametric Aircraft Configuration Schema) and RCE (remote component environment). The list of tools includes Scheduler (DLR; flight schedule simulation), AirTOp (NLR; TMA simulation), Dynamo/Farm (UPC; trajectory simulation and assessment), LEAS-iT (NLR; emissions simulation), Tuna (NLR; noise simulation), AECCI (ONERA; emissions simulation), TRIPAC (NLR; third-party risk simulation), and SCBA (TML; social and economic impact assessment). Interactions with other use cases of the project will be demonstrated via new aircraft configurations stemming from the use case at the aircraft level of the project. The results demonstrate the workflow’s feasibility, the cooperation among the tools to obtain and refine the outcomes, as well as the analysis of the operational scenario of a generic airport, CAEPport, which has been extensively used in previous Clean Sky 2 projects. Full article

This study aimed to assess the potential operational implications of integrating Advanced Air Mobility (AAM) traffic at the Orlando International Airport (MCO) Class Bravo airspace. Researchers developed corridor prototypes within MCO’s airspace to analyze potential traffic conflicts and wake turbulence risks between MCO’s commercial and AAM traffic. Furthermore, an AAM ecosystem at MCO was developed to enable the simultaneous integration of realistic MCO and AAM traffic paths. The ecosystem was created on a series of operational assumptions derived from the FAA’s AAM implementation plans and concepts of operation. The findings of this study revealed that the AAM ecosystem (corridor designs and operational schedule) had little to no impact on existing commercial air traffic operations based on the assumptions made for this analysis. Additionally, the assessment revealed that integrating 22 aircraft/airframes could result in an efficient operational infrastructure with no traffic or wake turbulence conflicts with existing commercial air traffic at MCO. This groundbreaking study marks one of the initial evaluations of AAM integration at a major international airport in the United States. Full article

With the significant potential of Unmanned Aircraft Vehicles (UAVs) extending throughout various fields and industries, their proliferation raises concerns regarding potential risks within the national airspace system (NAS). To enhance the safe and efficient integration of UAVs into airport environments, this paper presents an analysis of temporal statistical patterns in flight traffic, the predictive modeling of future traffic trends using machine learning, and the identification of optimal time windows for UAV operations within airports. The framework was developed using historical Automatic Dependent Surveillance–Broadcast (ADS-B) data obtained from the OpenSky Network. Historical flight data from Class B, C, and D airports in California are processed, and statistical analysis is carried out to identify temporal variations in flight traffic, including daily, weekly, and seasonal trends. A recurrent neural network (RNN) model incorporating Long Short-Term Memory (LSTM) architecture is developed to forecast future flight counts based on historical patterns, achieving mean absolute error (MAE) values of 4.52, 2.13, and 0.87 for Class B, C, and D airports, respectively. The statistical analysis findings highlight distinct traffic patterns across airport classes, emphasizing the practicality of utilizing ADS-B data for UAV flight scheduling to minimize conflicts with manned aircraft. Additionally, the study explores the influence of external factors, including weather conditions and dataset limitations on prediction accuracy. By integrating machine learning with real-time ADS-B data, this research provides a framework for optimizing UAV operations, supporting airspace management and improving regulatory compliance for safe UAV integration into controlled airspace. Full article

Capacity–flow balance constitutes the primary challenge in strategic slot allocation. Both air traffic flow and airport flow are significantly influenced by departure/arrival times of flights. However, due to various uncontrollable factors such as flow control, delay propagation, and weather conditions, the actual departure/arrival times of flights inevitably deviate from their schedules. This reflects the inherent uncertainty in flight slot execution, which directly introduces uncertainty into capacity–flow analysis. In this paper, we develop an uncertainty slot allocation model for the multi-airport system (MAS), which incorporates slot execution deviation as an uncertainty factor with fix capacity restrictions formulated as chance constraints to balance robustness and optimality. To solve the model, we employ an equivalent model transformation approach and develop a scenario generation methodology. We applied our model to the MAS of Beijing–Tianjin for slot allocation. The results show that when the violation probability $\alpha \in [0,0.2]$ , the model achieved fully robust optimization. Even when $\alpha$ increases to 0.4, under all scenario combinations, at the selected fix, compared with the results of the deterministic model and original schedules, the number of peak flow time windows in the expected traffic statistics decreased by 84.6% and 75%, respectively, and the average maximum values of traffic in the maximum traffic statistics decreased by 31.1% and 33.5%, respectively. Furthermore, the incorporation of the chance constraint provides slot coordinators with flexible optimization solutions based on their acceptable risk levels. Full article

With the rapid expansion of the aviation industry, traditional static scheduling methods have become inadequate to meet the increasingly complex demands of efficient airport operations. To enhance the operational efficiency of multi-runway airports, this paper introduced a two-stage dynamic departure scheduling method based on continuous Markov chains. The pushback rate control strategy was extended to multi-runway scenarios to identify the optimal taxiway queue threshold in stage I. In stage II, the pushback rate control strategy with a known queue threshold was introduced into a multi-objective optimization model, aiming to minimize flight delays and operational costs including pushback waiting times, taxi fuel consumption, and environmental impact. Then, continuous-time Markov chains (CTMC) were employed to track aircraft state transitions in the taxiway queue, and a nested whale optimization algorithm was proposed to optimize both the pushback sequence and runway resource allocation. Results indicate that the proposed method reduced the average taxiway queue time by 55.58%, with delay reductions of up to 73.06%, offering significant cost savings and environmental benefits while improving flight punctuality. This innovative approach highlights the potential for optimizing airport resource scheduling in complex and dynamic environments. Full article

This paper discusses SESAR’s Auto-Steer Taxi at Airport (ASTAIR) project, which seeks to advance airport ground operations including engine-off taxiing to move towards sustainable airports. The ASTAIR concept integrates human–AI teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR’s AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describe the operational challenges involved in implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human–automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization. Full article

It is particularly important to develop efficient air–rail intermodal path planning methods for making full use of the advantages of air–rail intermodal networks and providing passengers with richer and more reasonable travel options. A Time-Expanded Graph (TEG) is used to model the timetable information of public transportation providing a theoretical basis for public transportation path planning. However, if the TEG includes a large amount of data such as train stations, airports, train and air schedules, the network scale will become very large, making path planning extremely time-consuming. This study proposes an XGBoost-based heuristic path planning algorithm (XGB-HPPA) for large scale air–rail intermodal networks, which use the XGBoost model to predict transfer stations before path planning, and quickly eliminate unreasonable transfer edges by adding a heuristic factor, reducing the network scale, thus accelerating the computation speed. Comparative results indicate that XGB-HPPA can markedly enhance computational speed within large-scale networks, while obtaining as many valid solutions as possible and approximating the optimal solution. Full article

To reduce airport operational costs and minimize environmental pollution, an increasing number of airports are transitioning from fuel-powered to electric ground-handling vehicles. However, the limited battery capacity of electric vehicles and the need for charging make the scheduling of these vehicles more complex. To address this scheduling problem, this paper proposes an electric ground-handling vehicle scheduling algorithm that combines the MAMBA model with an attention-based neural network. The MAMBA model is designed to process multi-dimensional features such as flight information, vehicle locations, service demands, and time window constraints. Subsequently, an attention mechanism-based neural network is developed to dynamically integrate vehicle states, service records, and operational and charging constraints, in order to select the most suitable flights for electric ground-handling vehicles to service. The experiments use flight data from Xiamen Gaoqi International Airport and compare the proposed method with CPLEX solvers, existing heuristic algorithms, and custom heuristic algorithms. The results demonstrate that the proposed method not only effectively solves the electric ground-handling vehicle scheduling problem and provides high-quality solutions, but also exhibits good scalability in different parameter settings and real-time scheduling scenarios. Full article