Advanced Air Mobility (AAM) represents a transformative approach to urban and regional transportation, leveraging Uncrewed Aerial Vehicles (UAVs) and electric Vertical Take-Off and Landing (eVTOL) aircraft to provide safe, efficient, and environmentally friendly mobility solutions. The rapid growth of AAM introduces complex operational challenges, including trajectory optimisation, airspace integration, and risk management, which are influenced by factors such as weather variability, traffic density, and regulatory constraints. Effective analysis and optimisation of AAM operations require the integration of flight dynamics, meteorological data, and advanced computational methods to ensure safety, efficiency, and reliability. Research in this area aims to develop strategies that reduce operational risk, enhance route planning, and enable the seamless integration of AAM into existing air traffic management systems, paving the way for the widespread adoption of autonomous and semi-autonomous aerial transport.

The rapid development of Advanced Air Mobility (AAM) presents significant operational challenges that must be addressed to ensure safe, efficient, and reliable aerial transportation. Current AAM operations face difficulties in optimising flight trajectories, managing operational risk, and integrating UAVs and eVTOL aircraft into existing airspace systems. Factors such as dynamic weather conditions, airspace congestion, and regulatory constraints further complicate route planning and mission execution. Without systematic analysis and optimisation, AAM operations may face inefficiencies, increased safety risks, and limitations in scalability.This research aims to address these challenges by developing methodologies for the analysis and optimisation of AAM operations. By combining trajectory optimisation algorithms, risk assessment frameworks, and meteorological data integration, it is possible to improve flight efficiency while minimising operational hazards. Additionally, simulation-based studies and advanced computational models can be used to test and validate operational strategies under varying environmental and traffic conditions. The outcome will provide actionable insights for safer, more efficient, and scalable AAM operations, supporting the integration of autonomous and semi-autonomous aerial vehicles into urban and regional airspaces.

This research topic focuses on the analysis and optimisation of Advanced Air Mobility (AAM) operations, with an emphasis on improving safety, efficiency, and integration of Uncrewed Aerial Vehicles (UAVs) and eVTOL aircraft into complex airspace environments.

Contributions are invited that explore innovative solutions across the following themes:

- Trajectory optimisation and route planning under operational and environmental constraints

- Risk assessment and management strategies for AAM operations

- Integration of AAM into existing air traffic management and urban airspace systems

- Impact of meteorology and environmental factors on flight performance

- Simulation and modelling of multi-vehicle interactions and congestion

- Autonomous and semi-autonomous flight operations

- Energy efficiency, noise, and environmental impact assessments

- Data-driven approaches, including machine learning, for decision support

Types of Manuscripts: we welcome original research articles, technical notes, case studies, and comprehensive review papers that provide new insights, methodologies, or applications related to AAM operations. Interdisciplinary contributions combining aeronautics, robotics, computational modelling, and data analytics are particularly encouraged.

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Analysis and Optimisation of Advanced Air Mobility Operations