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Aerospace
Special Issues
Next-Generation Airport Operations and Management
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Next-Generation Airport Operations and Management

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Air Traffic and Transportation".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 8125

Special Issue Editors

Prof. Dr. Eri Itoh

E-Mail Website
Guest Editor
1. Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
2. Department of Aeronautics and Astronautics, The University of Tokyo, Tokyo 113-8656, Japan
Interests: air traffic management; air transport; modeling and simulation; data science; flight control
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng-Lung (Richard) Wu

E-Mail Website
Guest Editor
School of Aviation, UNSW Australia, Kensington, NSW 2052, Australia
Interests: airline scheduling; airline operations; airport management; airport operations; passenger spending behaviour
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michael Schultz

E-Mail Website
Guest Editor
Institute of Flight Systems, Bundeswehr University Munich, 85577 Neubiberg, Germany
Interests: air transportation; data-driven and model-based environments; predictive analysis; integrated airspace and airport management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Airport operations and management are entering a new era, driven by advances in digital technologies, shifting passenger and cargo demands, and the imperative for more sustainable and efficient processes. From the optimization of passenger and baggage flow to the geoeconomics of air transport networks, there is a pressing need for interdisciplinary research that not only addresses immediate operational challenges but also anticipates the future demands of a rapidly evolving global aviation landscape.

This Special Issue, entitled “Next-Generation Airport Operations and Management”, aims to gather research and case studies showcasing innovative strategies, technologies, and frameworks that will shape the airports of tomorrow. We invite contributions that examine and propose solutions for diverse aspects of airport operations, including scheduling, infrastructure, data-driven insights, and collaborative decision making.

Potential topics include, but are not limited to, the following:

Airport Networks and Geoeconomics

  • Analysis and prediction of current and future air transport networks.
  • Modeling passenger and cargo movements, including demand forecasting.
  • Interdisciplinary approaches to the economic, political, and geographical factors influencing airport connectivity.

Data-Driven Operations and Scheduling

  • Advanced algorithms for gate assignment and resource allocation.
  • Predictive analytics and machine learning for real-time operational improvements.

Turnaround Management and Ground Handling

  • Process optimization techniques to minimize delays and enhance safety.
  • Collaborative decision making among airports, airlines, and ground handlers.

Passenger and Baggage Flow

  • Digital twin implementations and sensor-based monitoring.
  • Innovative solutions for queue management, wayfinding, and enhanced passenger experiences.

Smart Airport Infrastructure and the IoT

  • Internet of Things applications for real-time data collection and analysis.
  • Next-generation technology integration to improve operational efficiency and security.

Sustainability and Environmental Considerations

  • Approaches to reduce environmental impact at airports (e.g., emissions, noise).
  • Sustainable infrastructure development and green operations.

We welcome original research papers, case studies, and review articles that explore these topics from technological, operational, economic, and policy-oriented perspectives. Submissions that bridge theoretical frameworks with practical implementations—providing actionable insights for airport stakeholders—are particularly encouraged.

Join us in shaping the future of airport operations and management. By participating in this Special Issue, you will contribute to a deeper understanding of how airports can evolve to meet emerging challenges, harness cutting-edge technologies, and deliver unparalleled service while remaining resilient, efficient, and economically viable.

Prof. Dr. Eri Itoh
Dr. Cheng-Lung (Richard) Wu
Prof. Dr. Michael Schultz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven scheduling and gate assignment
  • turnaround management optimization
  • real-time passenger and baggage flow monitoring
  • collaborative decision-making
  • smart airport infrastructure
  • Internet of Things (IoT) solutions

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

17 pages, 2039 KB  
Article
Airport Taxiway–Gate Joint Scheduling Problem: A Multi-Objective Optimization Approach Based on a Spatiotemporal Graph
by Jinghan Du, Hongwei Li, Weining Zhang, Weijun Pan and Jianan Yin
Aerospace 2026, 13(4), 384; https://doi.org/10.3390/aerospace13040384 - 18 Apr 2026
Viewed by 193
Abstract
The optimization of gate allocation and taxiway routing represents a critical challenge in enhancing airport ground operations performance. To simultaneously address these two closely coupled tasks, their interconnected processes are first modeled as flows in a spatiotemporal graph. On this basis, we develop [...] Read more.
The optimization of gate allocation and taxiway routing represents a critical challenge in enhancing airport ground operations performance. To simultaneously address these two closely coupled tasks, their interconnected processes are first modeled as flows in a spatiotemporal graph. On this basis, we develop a multi-objective optimization approach that accounts for both temporal and spatial factors across different operational aspects, effectively balancing the diverse needs of travelers, carriers, and airport authorities. To mitigate differences in scale and preference among various optimization objectives, min-max normalization combined with the linear weighting method is employed to transform the multi-objective problem into a single-objective one, which is solved by binary integer linear programming. Based on the actual operational data of Terminal 1 at Shanghai Pudong International Airport, three typical scenarios of different complexity are constructed for validation purposes. Performance comparisons with the state-of-the-art methods demonstrate the superiority of the proposed model in terms of various operational costs and parameter sensitivity. The integrated scheduling solution offers airport operators a reliable and efficient decision-making tool with practical applicability. Full article
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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20 pages, 2607 KB  
Article
A Data-Driven Methodology for Developing a Future Design Day Flight Schedule (DDFS)
by Eunji Kim, Seokjae Yun and Hojong Baik
Aerospace 2026, 13(3), 293; https://doi.org/10.3390/aerospace13030293 - 19 Mar 2026
Viewed by 277
Abstract
The design day flight schedule (DDFS) plays a pivotal role in airport simulation and infrastructure planning. Despite its importance, previous studies and global guidelines offer only broad recommendations for DDFS preparation, lacking detailed methodologies and empirical validation. This study proposes a systematic, data-driven [...] Read more.
The design day flight schedule (DDFS) plays a pivotal role in airport simulation and infrastructure planning. Despite its importance, previous studies and global guidelines offer only broad recommendations for DDFS preparation, lacking detailed methodologies and empirical validation. This study proposes a systematic, data-driven approach for generating a future DDFS that accounts for projected demand, airline behavior, and regional traffic characteristics. Leveraging historical flight operation data and probabilistic distributions, the proposed method captures existing patterns and anticipated market changes comprehensively. To realistically define each flight’s operational characteristics, a structured 10-step procedure is employed to generate and assign attributes—such as aircraft type, origin/destination airport, and turnaround time—based on empirical patterns and logical constraints. The proposed approach is applied to Incheon International Airport as a case study, demonstrating its practical utility and scalability. The generated DDFSs are shown to be consistent with target-year forecasts in terms of peak-hour operations and fleet composition, with deviations remaining within a small error range. Additional validation confirms that key operational characteristics, including airline shares, connection patterns, and turnaround times, are reproduced with acceptable accuracy. By bridging the gap between high-level guidance and implementable practice, this study contributes a replicable framework for future DDFS generation and provides actionable insights for airport planners aiming to better anticipate operational demands. Full article
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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24 pages, 2089 KB  
Article
Assessment of the Coupling and Coordination Ability of Airport Agglomerations
by Yu Sun, Lei Liang, Xiaolei Chong, Zhenglei Chen, Jing Xue and Zijian Deng
Aerospace 2026, 13(3), 239; https://doi.org/10.3390/aerospace13030239 - 4 Mar 2026
Viewed by 348
Abstract
Airport agglomeration coupling coordination is a key indicator of healthy regional aviation development. This study constructs an evaluation index system from three dimensions—airport production, infrastructure construction, and network support—and assesses the coupling coordination capability of China’s four major airport agglomerations using the entropy [...] Read more.
Airport agglomeration coupling coordination is a key indicator of healthy regional aviation development. This study constructs an evaluation index system from three dimensions—airport production, infrastructure construction, and network support—and assesses the coupling coordination capability of China’s four major airport agglomerations using the entropy weight method and a coupling coordination model. Furthermore, an Airport Consistency Index is innovatively introduced as the reciprocal of the coefficient of variation, and an overall coordination degree is developed under the framework of “balanced average level + consistency correction.” By incorporating the inverse coefficient of variation, the proposed index explicitly assesses airport agglomeration dispersion in coordination performance, thereby mitigating the risk that a strong performance at leading airports masks structural imbalances within the system. This refinement enhances the diagnostic precision of the overall coordination assessment by integrating both average development level and internal convergence. Based on calculations for 2020–2024, the overall coordination ranking is Beijing–Tianjin–Hebei, Guangdong–Hong Kong–Macao Greater Bay Area, Yangtze River Delta, and Chengdu–Chongqing. The Beijing–Tianjin–Hebei agglomeration shows strong and stable coordination with limited sensitivity to external conditions, whereas the Yangtze River Delta is more environmentally sensitive due to its large number of airports. The Greater Bay Area demonstrates solid coordination with substantial synergy potential, while Chengdu–Chongqing exhibits relatively weak coordination and considerable room for improvement. The proposed model effectively evaluates the overall coordination degree of airport agglomerations and supports targeted development recommendations. Full article
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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23 pages, 1074 KB  
Article
Intent-Driven UAM Scheduling: An Explainable Hybrid AI Framework
by Jeongseok Kim and Kangjin Kim
Aerospace 2026, 13(2), 165; https://doi.org/10.3390/aerospace13020165 - 10 Feb 2026
Viewed by 464
Abstract
This paper presents a hybrid AI framework for rescheduling tasks within UAM vertiports. This scheduling challenge is approached as a resource-constrained project scheduling problem (RCPSP), typically solved via mixed-integer linear programming (MILP). However, unlike ideal models, real-world UAM operations are messy, and operator [...] Read more.
This paper presents a hybrid AI framework for rescheduling tasks within UAM vertiports. This scheduling challenge is approached as a resource-constrained project scheduling problem (RCPSP), typically solved via mixed-integer linear programming (MILP). However, unlike ideal models, real-world UAM operations are messy, and operator requests are frequently ambiguous. To handle this uncertainty, the proposed framework pairs a Bayesian network to infer intent via dialogue with Answer Set Programming (ASP) to categorize specific ambiguity types. Once the input is clarified, the system generates new MILP constraints and recalculates the schedule, allowing the operator to instantly verify changes against the initial plan. Full article
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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26 pages, 1045 KB  
Article
Enhanced Evaluation Model on Emergency Response Effectiveness at Civil Airports: A Theoretical and Empirical Study
by Hao Sun, Pei Zhu, Jiahe Miao, Lin Wang, Tao Wang, Lizhi Fang, Hongxia Dou and Wenfei Yu
Aerospace 2025, 12(12), 1082; https://doi.org/10.3390/aerospace12121082 - 4 Dec 2025
Cited by 1 | Viewed by 870
Abstract
The Civil Aviation Administration of China mandates comprehensive evaluations of emergency response effectiveness. However, existing studies predominantly focus on evaluating response capabilities rather than actual effectiveness. This leads to evaluation results deviating from reality. Other studies evaluating response effectiveness are mostly limited by [...] Read more.
The Civil Aviation Administration of China mandates comprehensive evaluations of emergency response effectiveness. However, existing studies predominantly focus on evaluating response capabilities rather than actual effectiveness. This leads to evaluation results deviating from reality. Other studies evaluating response effectiveness are mostly limited by incomplete indicator systems and flawed algorithms. To address the questions, this study takes runway unsafe events as the study subject, focuses on evaluating response effectiveness, examines common evaluation methodologies, identifies critical gaps in indicator systems, and discusses traditional algorithmic vulnerabilities. Utilizing the Delphi method and fuzzy Analytic Hierarchy Process, this study establishes a four-tier indicator system encompassing evaluation objectives, phases, processes, and elements and proposes an optimized model incorporating scoring criteria, indicator weights, and correction coefficients designed to mitigate inherent algorithmic vulnerabilities prevalent in traditional methodologies. Finally, two simulation verifications based on real incidents demonstrate that the model of emergency response effectiveness at civil airports has a notable improvement in evaluation accuracy. Full article
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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23 pages, 769 KB  
Article
Enhancing Urban Air Mobility Scheduling Through Declarative Reasoning and Stakeholder Modeling
by Jeongseok Kim and Kangjin Kim
Aerospace 2025, 12(7), 605; https://doi.org/10.3390/aerospace12070605 - 3 Jul 2025
Cited by 1 | Viewed by 1721
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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25 pages, 9450 KB  
Article
Flight Connection Planning for Low-Cost Carriers Under Passenger Demand Uncertainty
by Wenhao Ding, Max Z. Li and Eri Itoh
Aerospace 2025, 12(7), 574; https://doi.org/10.3390/aerospace12070574 - 24 Jun 2025
Cited by 3 | Viewed by 3439
Abstract
As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disruptions, creating challenges for network [...] Read more.
As low-cost carriers (LCCs) continue expanding their networks and enhancing profitability through connecting services, passenger demand has become a critical factor in flight connection planning. However, demand is inherently uncertain due to economic cycles, seasonal fluctuations, and external disruptions, creating challenges for network design. This study proposes a flight connection planning model tailored to LCC operations that explicitly accounts for demand uncertainty. The model determines the optimal set of connecting itineraries to introduce over the existing network of flights, identifies promising transfer airports, and provides passenger allocation strategies across flights. We apply the model to Spring Airlines’ real-world network to evaluate its effectiveness. Results show that the proposed model outperforms the deterministic benchmark in feasibility and stability under varying demand scenarios. Specifically, under the same constraint of selecting up to 10 transfer airports, our model increases the number of connecting itineraries by 59.5% compared to the deterministic model and achieves a more balanced passenger distribution. Across 10 representative demand scenarios, the average standard deviation of load factors is reduced by 26.1% compared to the deterministic benchmark. Moreover, the deterministic solution yields a 22.9% failure rate for planned connections, while our model maintains 100% feasibility. These findings highlight the model’s value as a resilient, practical decision-support tool for airline planners. Full article
(This article belongs to the Special Issue Next-Generation Airport Operations and Management)
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