Urban Air Mobility Review (2020–Present) Highlights Advances in Communication, Management, and Sustainability

Urban Air Mobility, the concept of using air travel to alleviate city congestion and improve access, is rapidly evolving into a viable transportation solution. Zhitong He, Zijing Wang from the American Bureau of Shipping, and Lingxi Li lead a comprehensive review of the significant advances made in this field since 2020. Their work highlights progress in three interconnected areas: reliable communication systems for air-to-ground data exchange, innovative air traffic management concepts for autonomous urban airspace, and sustainable practices encompassing energy-efficient propulsion and environmental assessment. This research demonstrates how coordinated development across these domains is essential for establishing a scalable and environmentally responsible urban air mobility ecosystem, paving the way for a future of efficient and accessible city travel.

Sustainable Urban Air Mobility Challenges and Technologies

This research explores the complex challenges and exciting opportunities surrounding the development of Urban Air Mobility (UAM), emphasizing the crucial role of advanced technologies in achieving safe and sustainable operations. Key areas of focus include advanced communication networks, essential for reliable, low-latency data exchange for air traffic management, vehicle-to-vehicle communication, and remote monitoring. Efficient and sustainable power solutions are also vital, with research concentrating on the design and optimization of power systems for electric vertical take-off and landing (eVTOL) vehicles, incorporating hybrid energy storage systems to maximize efficiency and range, and optimizing flight paths to minimize energy consumption. The research acknowledges that UAM will not operate in isolation, requiring integration with existing ground transportation networks through careful traffic demand analysis, multi-modal transportation planning, and consideration of safety when interacting with other vehicles and pedestrians. Data analytics and Artificial Intelligence (AI) play a critical role in enhancing UAM safety and efficiency, enabling risk analysis, anomaly detection, predictive maintenance, and trustworthy AI-driven decision-making. The team utilized simulation, data mining, network analysis, and visualization tools to evaluate UAM systems, extract insights from large datasets, and analyze research trends, ultimately presenting a comprehensive overview of the technological and logistical considerations for realizing a sustainable and safe UAM ecosystem.

Compact Antenna System for Urban Air Mobility

Researchers are pioneering advanced communication and management systems to enable safe and efficient Urban Air Mobility (UAM). To optimize communication performance, scientists developed a compact leaky-wave antenna (LWA) array operating between 52. 33 and 68. 7GHz, incorporating a spoof-surface-plasmon-polariton structure to achieve high gain and wide azimuth coverage, ensuring sufficient ground clearance for seamless integration with other electronic components. Experimental work focused on mitigating signal interference caused by airframe structures and propeller rotations, identifying specific frequency bands that enhance transmission quality and informing optimized antenna placement strategies.

To improve situational awareness, the study pioneered a geometric sequence decomposition (GSD) approach for enhancing range and velocity estimation in radar systems, significantly improving target detection accuracy even under challenging signal conditions. Complementing this, scientists developed a compressive sensing (CS)-based obstacle detection framework utilizing a multiple-input multiple-output (MIMO) radar setup, achieving superior imaging performance compared to traditional methods while reducing data requirements, as validated against LiDAR data. Studies analyzing commuter preferences in the San Francisco Bay Area revealed that up to 45% of commuters would prefer UAM during peak congestion, even with longer transfer times to vertiports, highlighting the strategic importance of vertiport placement. Researchers developed scalable scheduling approaches, including an Integer Linear Programming (ILP) model complemented by incremental ILP solving and a look-ahead search strategy, both incorporating vehicle relocation to improve service coverage. Furthermore, a multi-agent deep reinforcement learning (MADRL) framework was introduced to enhance collaborative management and dynamic scheduling adaptations, accounting for operational constraints and commuter demands.

Radar, Compressive Sensing, and Antenna Advances

Recent work demonstrates substantial progress towards realizing Urban Air Mobility (UAM), focusing on communication, UAM management, and sustainability. Researchers have achieved significant breakthroughs in optimizing communication systems for aerial vehicles, with a novel geometric sequence decomposition (GSD) approach for radar systems significantly improving target detection accuracy even under challenging signal-to-noise ratios. Furthermore, a compressive sensing (CS)-based obstacle detection framework reduced data requirements while maintaining high accuracy when compared to LiDAR data, offering a robust navigation tool for cluttered urban environments. In antenna design, a leaky-wave antenna (LWA) array, utilizing a spoof-surface-plasmon-polariton structure, achieved high gain and wide azimuth coverage in the 52.

33-68. 7GHz frequency range, ensuring sufficient ground clearance for seamless integration with other electronic components. A multi-agent deep reinforcement learning (MADRL) framework, employing a centralized training and distributed execution approach, enabled cooperation among multiple vehicles, ensuring equitable service distribution in simulations based on real-world vertiport maps. To enhance safety, a decentralized and hierarchical air traffic management (ATM) model was proposed, organizing air traffic control through “vertihubs” and “vertiports” to manage local airspace effectively. The study identifies advancements across three interconnected areas: communication systems enabling reliable air-ground data exchange, innovative air traffic management concepts for dense urban airspace, and sustainability initiatives focused on energy-efficient propulsion and environmental impact reduction. Through detailed analysis of existing research, the team classifies diverse technological efforts and highlights key innovations shaping the future of UAM. The findings demonstrate significant progress towards realizing scalable and sustainable urban air mobility ecosystems.

However, the authors acknowledge that challenges remain, particularly regarding the integration of these technologies into existing urban infrastructure and regulatory frameworks. Future work, they suggest, should focus on addressing these integration hurdles and refining the technologies to ensure safe, efficient, and environmentally responsible UAM operations. This research establishes a valuable foundation for ongoing development and provides a forward-looking perspective on the opportunities and challenges that lie ahead in this rapidly evolving field.