Special Issue "Application of Multiagent Systems and Artificial Intelligence Techniques in Aviation (Volume II)"

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Alexei Sharpanskykh
Website
Guest Editor
Air Transport and Operations, Delft University of Technology, Faculty of Aerospace Engineering
Interests: Mathematical and computational modeling and analysis of safety and resilience of complex sociotechnical systems in aviation; Application of multiagent systems and artificial intelligence techniques in aviation; Exploration and development of analysis methods (for example, model checking techniques) and tools for complex sociotechnical systems. Development of tools and techniques for simulation of sociotechnical systems.
Special Issues and Collections in MDPI journals
Dr. António J.M. Castro
Website
Guest Editor
LIACC (Laboratory of Artificial Intelligence and Computer Science), University of Porto, 4099-002 Porto, Portugal
Interests: distributed systems; multi-agent systems in general; organization structure in distributed systems/MAS; agent oriented software engineering; intelligent user interfaces; learning (machine learning); evolutionary computing; autonomy; MAS and agents in aerospace; disruption management in airline/airport operations, space operations and air traffic control
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Methods and tools from the areas of multiagent systems (MAS) and artificial intelligence (AI) have been gaining more and more popularity in aerospace. Next to current, highly-popular Big Data and machine learning techniques stemming from statistical AI, approaches from symbolic AI, based on rules, ontologies, mathematical logics, and formal reasoning are also applied in diverse areas of aerospace, such ATM, aircraft design, airport operations, maintenance, swarming of satellites, and UAS/UAV. A new direction of multiagent organizations and agent-based modelling and simulation (ABMS) of air transport and space operations, which includes interaction between humans and technical systems, is also growing in popularity.

The techniques, methods, and tools in AI, and MAS, and ABMS in particular, advance rapidly with every passing year, thus opening up new opportunities for diverse engineering applications in airspace. AI- and MAS-based solutions have repeatedly demonstrated more robustness, flexibility, and scalability than more traditional top-down approaches. However, the full potential of these novel techniques in application to airspace is to be determined.

This Special Issue welcomes a whole range of contributions, in which AI, MAS, and ABMS techniques are developed and/or applied to aerospace.

Topics of interest include, but are not limited to:

- Autonomous agents and multiagent systems in aerospace applications
- Knowledge representation, reasoning, and logic in aerospace applications
- Agent-based modelling and simulation of sociotechnical systems in aerospace
- Robotics, perception, and vision in aerospace applications
- Big Data, machine learning, and data mining in aerospace applications
- Planning and scheduling in air transport
- Industrial aerospace applications of AI, MAS and ABMS

Dr. Alexei Sharpanskykh
Dr. António J.M. Castro
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 papers will be 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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1000 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.

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Data-Driven Analysis of Airport Security Checkpoint Operations
Aerospace 2020, 7(6), 69; https://doi.org/10.3390/aerospace7060069 - 29 May 2020
Abstract
Airport security checkpoints are the most important bottleneck in airport operations, but few studies aim to empirically understand them better. In this work we address this lack of data-driven quantitative analysis and insights about the security checkpoint process. To this end, we followed [...] Read more.
Airport security checkpoints are the most important bottleneck in airport operations, but few studies aim to empirically understand them better. In this work we address this lack of data-driven quantitative analysis and insights about the security checkpoint process. To this end, we followed a total of 2277 passengers through the security checkpoint process at Rotterdam The Hague Airport (RTM), and published detailed timing data about their journey through the process. This dataset is unique in scientific literature, and can aid future researchers in the modelling and analysis of the security checkpoint. Our analysis showed important differences between six identified passenger types. Business passengers were found to be the fastest group, while passengers with reduced mobility (PRM) and families were the slowest two groups. We also identified events that hindered the performance of the security checkpoint, in which groups of passengers had to wait long for security employees or other passengers. A total of 335 such events occurred, with an average of 2.3 passengers affected per event. It was found that a passenger that had a high luggage drop time was followed by an event in 27% of the cases, which was the most frequent cause. To mitigate this waiting time of subsequent passengers in the security checkpoint process, we performed an experiment with a so-called service lane. This lane was used to process passengers that are expected to be slow, while the remaining lanes processed the other passengers. It was found that the mean throughput of the service lane setups was higher than the average throughput of the standard lanes, making it a promising setup to investigate further. Full article
Show Figures

Figure 1

Open AccessArticle
Agent-Based Distributed Planning and Coordination for Resilient Airport Surface Movement Operations
Aerospace 2020, 7(4), 48; https://doi.org/10.3390/aerospace7040048 - 19 Apr 2020
Abstract
Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in [...] Read more.
Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in complex systems, especially during disturbances. In contrast to the centralised approaches, distributed planning is performed by several agents, which coordinate plans with each other. This research evaluates the contribution of agent-based distributed planning and coordination to the resilience of airport surface movement operations when runway reconfigurations occur. An autonomous Multi-Agent System (MAS) model was created based on the layout and airport surface movement operations of Schiphol Airport in the Netherlands. Within the MAS model, three distributed planning and coordination mechanisms were incorporated, based on the Conflict-Based Search (CBS) Multi-Agent Path Finding (MAPF) algorithm and adaptive highways. MAS simulations were run based on eight days of real-world operational data from Schiphol Airport and the results of the autonomous MAS simulations were compared to the performance of the real-world human operated system. The MAS results show that the distributed planning and coordination mechanisms were effective in contributing to the resilient behaviour of the airport surface movement operations, closely following the real-world behaviour, and sometimes even surpassing it. In particular, the mechanisms were found to contribute to more resilient behaviour than the real-world when considering the taxi time after runway reconfiguration events. Finally, the highway included distributed planning and coordination mechanisms contributed to the most resilient behaviour of the airport surface movement operations. Full article
Show Figures

Figure 1

Back to TopTop