Machine learning and mixed reality for smart aviation: applications and challenges

The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface

Airports are the main bottlenecks in the Air Traffic Management (ATM) system. The predicted 84% increase in global air traffic in the next two decades has rendered the improvement of airport operational efficiency a key issue in ATM. Although the operations on runways, taxiways, and aprons are highly interconnected and interdependent, the current practice is not integrated and piecemeal, and overly relies on the experience of air traffic controllers and stand allocators to manage operations, which has resulted in sub-optimal performance of the airport surface in terms of operational efficiency, capacity, and safety. This thesis proposes a mixed qualitative-quantitative methodology for integrated and joint optimisation of runways, taxiways, and aprons, aiming to improve the efficiency of airport surface operations by integrating the operations of all three resources and optimising their coordination. This is achieved through a two-stage optimisation procedure: (1) the Integrated Apron and Runway Assignment (IARA) model, which optimises the apron and runway allocations for individual aircraft on a pre-tactical level, and (2) the Integrated Dynamic Routing and Off-block (IDRO) model, which generates taxiing routes and off-block timing decisions for aircraft on an operational (real-time) level. This two-stage procedure considers the interdependencies of the operations of different airport resources, detailed network configurations, air traffic flow characteristics, and operational rules and constraints. The proposed framework is implemented and assessed in a case study at Beijing Capital International Airport. Compared to the current operations, the proposed apron-runway assignment reduces total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time and fuel consumption respectively by 15.5%, 15.28%, 45.1%, [58.7%, 35.3%, 16%] (RWY01, RWY36R, RWY36L) and 6.6%; gated assignment is increased by 11.8%. The operational feasibility of this proposed framework is further validated qualitatively by subject matter experts (SMEs). The potential impact of the integrated apron-runway-taxiway operation is explored with a discussion of its real-world implementation issues and recommendations for industrial and academic practice.Open Acces

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Optimal route design of electric transit networks considering travel reliability

Travel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl\u27s road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Exitus: An Agent-Based Evacuation Simulation Model For Heterogeneous Populations

Evacuation planning for private-sector organizations is an important consideration given the continuing occurrence of both natural and human-caused disasters that inordinately affect them. Unfortunately, the traditional management approach that is focused on fire drills presents several practical challenges at the scale required for many organizations but especially those responsible for national critical infrastructure assets such as airports and sports arenas. In this research we developed Exitus, a comprehensive decision support system that may be used to simulate large-scale evacuations of such structures. The system is unique because it considers individuals with disabilities explicitly in terms of physical and psychological attributes. It is also capable of classifying the environment in terms of accessibility characteristics encompassing various conditions that have been shown to have a disproportionate effect upon the behavior of individuals with disabilities during an emergency. The system was applied to three unique test beds: a multi-story office building, an international airport, and a major sports arena. Several simulation experiments revealed specific areas of concern for both building managers and management practice in general. In particular, we were able to show (a) how long evacuations of heterogeneous populations may be expected to last, (b) who the most vulnerable groups of people are, (c) the risk engendered from particular design features for individuals with disabilities, and (d) the potential benefits from adopting alternate evacuation strategies, among others. Considered together, the findings provide a useful foundation for the development of best practices and policies addressing the evacuation concerns surrounding heterogeneous populations in large, complex environments. Ultimately, a capabilities based approach featuring both tactical and strategic planning with an eye toward the unique problems presented by individuals with disabilities is recommended

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

Modal analysis of offshore monopile wind turbine: An analytical solution

An analytical solution of the dynamic response of offshore wind turbines under wave load with nonlinear Stokes’s wave theory and wave–structure and soil–foundation interactions is developed. Natural frequencies and the corresponding modes are obtained. The effect of the wave–structure interaction, the added mass, the foundation stiffness, and the nacelle translational and rotational inertia on the motion of the structure is investigated. The nonlinear loading provided by the drag term of Morison’s equation is successfully handled. A parametric study to examine the effect of the structural parameters on the dynamic response is conducted, and the results of the proposed analytical solution are compared to numerical ones. The proposed method has the following advantages: (a) it is accurate and straightforward because of its analytical nature, (b) it does not ignore the drag term in the wave loading by keeping its nonlinearity nature, (c) the structure of the wind turbine is modeled as a continuous system, (d) it takes into account the effect of the rotational and translational inertia of the nacelle on the dynamic response, and (e) it provides an interpretation of the effect of the sea level variation in changing the natural frequencies.acceptedVersio

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors