Research and Applications of Low-Altitude Urban Traffic System

1. Introduction

Urban low-altitude traffic systems are an emerging and transformative urban transportation system, promising to revolutionize how people and goods move within and between cities. This involves the use of drones, air taxis, and other aerial vehicles operating at low altitudes, typically below 500 feet, in increasingly congested urban areas where traditional ground transportation systems struggle to keep pace, leading to delays, pollution, and inefficiencies. Urban low-altitude traffic offers a promising solution to these challenges by utilizing the airspace above cities for fast, efficient, and sustainable transport.

Integrating these aerial vehicles into urban environments poses significant challenges. Developing appropriate regulatory frameworks is crucial to ensure safety, manage airspace effectively, and protect privacy. Robust communication systems are needed for the real-time coordination and control of UAVs (Unmanned Aerial Vehicles). Infrastructure development, such as vertiports and charging stations, is essential to support the operation and maintenance of these vehicles. Safety and public acceptance are also critical. Addressing concerns related to noise pollution, visual impact, and equitable access is vital for gaining public support and ensuring that the benefits of urban low-altitude traffic are widely shared.

This Special Issue explores various dimensions of urban low-altitude traffic, presenting cutting-edge research and insights across transportation, communication, safety, and other relevant areas, providing a comprehensive overview of this rapidly evolving field. The topics include the following:

• Latest innovations in drones and air taxis, including propulsion systems, battery advancements, and autonomous navigation technologies;
• Systems and strategies for managing urban airspace, including unmanned traffic management (UTM) systems and their integration with traditional air traffic control;
• Advanced communication networks enabling urban aerial vehicles’ real-time data exchange, control, and coordination;
• Technologies and protocols designed to prevent collisions and ensure the safe operation of aerial vehicles in urban environments;
• Design, location, and implementation of vertiports, landing pads, and electric charging stations essential for supporting urban aerial traffic;
• Infrastructure for the maintenance and support of aerial vehicles, ensuring their reliability and longevity;
• Environmental benefits of electric propulsion systems, optimizing flight operations, and reducing the carbon footprint of urban aerial traffic;
• Addressing the social implications of urban low-altitude traffic, including noise pollution, visual impact, and strategies for gaining public acceptance;
• Exploring how urban aerial traffic can be designed to be accessible and beneficial to all segments of society, including underserved communities.

This Special Issue aims to provide a holistic view of urban low-altitude traffic, showcasing interdisciplinary research and the collaboration necessary to realize its full potential. By addressing the technical, regulatory, infrastructural, environmental, and societal dimensions, we hope to contribute to the advancement and integration of urban low-altitude traffic solutions, making our cities smarter, cleaner, and more connected.

2. Short Presentation of the Papers

Cao et al. [1] introduced a three-tier hierarchical dynamic separation framework adapting minima across strategic (30–80 m category-specific baselines), pre-tactical (0.7–1.8× encounter-dependent scaling), and tactical (real-time 3D decomposition) timescales. Monte Carlo simulation across 100,000+ flight hours demonstrates 47% collision rate reduction versus fixed 30 m separation (0.008 vs. 0.015 per 1000 h, p < 0.001), 50% airspace utilization increase (18.4 vs. 12.3 UAVs/km³), 44% flight time penalty decrease (8.5% vs. 15.2%), and 99.97% ICAO-compliant TLS achievement (≤10⁻⁷ per flight hour) with real-time performance (78.5 ms for 20 UAVs). The framework provides an immediately deployable foundation for heterogeneous UAV traffic management.

Xie et al. [2] developed a methodology for determining safe distances and assessing the throughput capacity of transport systems. The work is based on a multi-criteria assessment that takes into account four significant indicators. The application of the Pareto optimization principle made it possible to identify the most effective compromise solutions. A collision probability model with random UAV headings was proposed to determine safety separations, and a grid capacity simulation model with saturation judgment and convergence verification was established. The optimal grid granularity was identified as 20 m. Safety separations for DJI M300RTK, Mavic 3Pro, and Air 3S were 104 m, 86 m, and 47 m, respectively. Saturated capacity stabilized within 106–116 s, with stable values of 1.022, 0.961, and 1.023 drones/min for the three UAV models. The results of the study contain key conclusions about traffic capacity and suggest ways to optimize it. Conclusions: This study provides a theoretical framework for airspace resource optimization and UAV path planning, offering quantifiable benchmarks to evaluate and manage urban low-altitude airspace.

Carramiñana et al. [3] proposed an authorization method which is based on a deferred authorization decision with multiple-priority classes that are gate-kept by a series of scarce flight tokens. In it, operators can guide the aerial traffic deconfliction process by indicating the criticality of each operation (i.e., selected priority class) based on their business logic and the available flight tokens. Scarce token distribution is performed by a centralized service following a fairness- or congestion-management policy defined by authorities. Also, geographical and temporal incentives can be considered using a 4D-dependent temporal airspace cost to compute the required number of tokens per flight. Results based on several simulation scenarios demonstrate the validity of the approach and its capability in prioritizing different operators’ behaviors (fairness management) or avoiding flight hotspots (congestion management). Overall, it is concluded that the proposed method is an efficient, fair, simple and scalable novel authorization process that can be integrated into the U-space ecosystem.

Guo et al. [4] established a median location model to minimize vertiport construction cost, passenger commuting cost, and penalty cost. For the nonlinear term in the objective function, the Big-M method is employed. Based on the reformulated model, we improve the branch-and-bound algorithm (LVBB) to solve it, where the Lagrange relaxation method is used to decompose the large-scale problem into parallel subproblems and compute the lower bound, and the variable neighborhood search algorithm is used to obtain the upper bound. Numerical experiments are performed in the 11 administrative districts of Nanjing, China. The results demonstrate that the proposed location scheme effectively balances vertiport construction cost and passenger commuting cost while satisfying capacity limitations. It also significantly reduces commuting time to improve passenger satisfaction. This scheme can offer strategic guidance for infrastructure planning in UAM.

Pothana et al. [5] presented an analysis of temporal statistical patterns in flight traffic, the predictive modeling of future traffic trends using machine learning, and the identification of optimal time windows for UAV operations within airports. The framework was developed using historical Automatic Dependent Surveillance–Broadcast (ADS-B) data obtained from the OpenSky Network. Historical flight data from Class B, C, and D airports in California are processed, and statistical analysis is carried out to identify temporal variations in flight traffic, including daily, weekly, and seasonal trends. A recurrent neural network (RNN) model incorporating Long Short-Term Memory (LSTM) architecture is developed to forecast future flight counts based on historical patterns, achieving mean absolute error (MAE) values of 4.52, 2.13, and 0.87 for Class B, C, and D airports, respectively. The statistical analysis findings highlight distinct traffic patterns across airport classes, emphasizing the practicality of utilizing ADS-B data for UAV flight scheduling to minimize conflicts with manned aircraft. Additionally, the study explores the influence of external factors, including weather conditions and dataset limitations on prediction accuracy. By integrating machine learning with real-time ADS-B data, this research provides a framework for optimizing UAV operations, supporting airspace management and improving regulatory compliance for safe UAV integration into controlled airspace.

Liu et al. [6] examined the effects of axial and lateral rotor separation on the rotor’s thrust performance through wind tunnel experiments. The tests simulate horizontal, vertical, and hovering states by generating relative airflow in the wind tunnel, focusing primarily on the thrust coefficient changes of the bottom rotor at various separations. The results are compared with a single rotor operating under the same conditions without wake interference. Additionally, computational fluid dynamics simulations were conducted to investigate the effect of wake interactions by analyzing the velocity flow field between the two rotors in different separations. Both the experimental and simulation results demonstrate that rotor aerodynamic performance is notably influenced by wake interactions. Under hovering and vertical states, substantial aerodynamic interference occurs in the region directly beneath the top rotor, within 1D ≤ Z ≤ 3D. This interference gradually diminishes as the rotor separation increases. Additionally, the thrust coefficient of the bottom rotor decreases with increasing flight speed due to the wake, and at higher flight speeds, the wake tends to contract. When the lateral separation is X = 0D, the mid-sectional flow field of the two rotors exhibits symmetry; however, with lateral separation, the symmetry of the bottom rotor’s wake velocity field is disrupted. During the horizontal flight, the rotor wake tilts backward due to the relative airflow, and the extent of this influence is governed by both rotor rotational speed and flight velocity. Therefore, when UAVs operate in formation, it is crucial to account for factors affecting aerodynamic performance, and rotor separation must be optimized to enhance flight safety and efficiency.