Urban Air Mobility Operations Research Articles

Analysis and Prediction of Airspace Availability for Urban Air Mobility Operations in the Sao Paulo Metropolitan Region

Urban Air Mobility (UAM) is an emerging form of transportation that is expected to introduce novel flight networks into already busy and complex airspace surrounding major cities and metropolitan regions. This paper studies the dynamics of urban airspace use by conventional aircraft over the Sao Paulo metropolitan region in order to identify and predict which airspace volumes are least constrained and best accessible for future UAM flights. Using historical flight tracking data, clustering analysis is first performed to identify departure and arrival trajectory patterns flown by conventional traffic at the two major airports – Sao Paulo/Guarulhos International airport and Sao Paulo/Congonhas airport. We then create a probabilistic model of the spatiotemporal distribution of air traffic under known meteorological conditions, which enables the prediction of active procedures, their spatial confidence regions and the resulting airspace availability for UAM in response to dynamic operational factors. The data-based approach allowed for a high-fidelity characterization of the Sao Paulo urban airspace use patterns as well as for accurate predictions of the available airspace for UAM, bringing novel insights and capabilities in support of dynamic and efficient urban airspace management.

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.

Aviation Safety for Urban Air Mobility: Pilot Licensing and Fatigue Management

Urban Air Mobility (UAM) is an emerging air traffic system designed for passengers and cargo in and around urban environments. Both the Federal Aviation Administration of the United States and the European Union Aviation Safety Agency endorse a phased development approach for UAM, commencing with manned aviation and subsequently transitioning to remotely piloted and autonomous operations. This article focuses on legal considerations related to aviation safety, with a specific focus on pilot licensing and crew fatigue management. An analysis of existing aviation law provisions suggests that the International Civil Aviation Organization can work with local authorities to create regulations governing both on-board and remote pilots involved in UAM operations. Safety standards in air law can apply mutatis mutandis to on-board pilots until specific regulations are developed. In the longer term, there shall be domestic laws on both on-board and remote UAM pilots.

Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.

A demand forecasting model for urban air mobility in Chengdu, China

The successful application of new technologies such as remotely piloted aircraft systems, distributed electric propulsion systems, and automatic control systems on electric vertical take-off and landing(eVTOL) aircraft has prompted Urban Air Mobility (UAM) to be mentioned frequently. UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas, which is thought to share some of the traffic on the ground. One of the prerequisites for UAM to operate on a regular basis is that its demand can support the operating costs, so forecasting UAM demand is necessary. We conduct UAM demand forecasting based on the four-step method, focusing on improving the third-step modal split, and propose a demand forecasting model based on the logit model. The model combines a nested logit (NL) model with a multinomial logit (MNL) model to solve the problem of non-existent UAM sharing rates. We use Chengdu, China as an example, and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method. The results show that UAM is suitable for shared operation during the early stages. With a fully shared operation, the UAM share rate increases by 0.73% for every kilometer increase in distance. Moreover, UAM is more competitive than other modes for delivery distances exceeding 15 ​km. Finally, using the distributions of the share rate and traffic flow pattern from the simulation, we propose the routes that can be prioritized for UAM operations in Chengdu.

Flying in the Strike Zone: Urban Air Mobility and Wildlife Strike Prevention

The number and rate of wildlife strikes continue to increase worldwide. More than 30 years of wildlife strike data indicate that approximately 92% of wildlife strikes for commercial aircraft occur at or below 3500 ft above ground level. Since urban air mobility (UAM) operations are envisioned to carry cargo and passengers up to an altitude of 4000 ft above ground level, UAM operations will face increased exposure to wildlife strike risks throughout their operations. Moreover, the comparatively small size of UAM aircraft, proposed cruising speeds of up to 170 kt, and quieter electric engines are expected to reduce opportunities for threat perception and evasive maneuvers by aircraft operators and wildlife. This paper presents how well-established wildlife strike prevention measures can be adapted to meet the needs of UAM operations and how, with new technology, original equipment manufacturers (OEMs), regulatory agencies, aircraft operators, communities, airspace managers, and vertiport operators can work together to enhance safety.

Deep learning based prediction of urban air mobility noise propagation in urban environment.

A deep learning based method is proposed to predict the urban air mobility (UAM) noise propagation in the urban environment. This method aims to efficiently estimate the noise impact of UAM flights on the complex urban area. The noise hemisphere was created via the comprehensive multirotor noise assessment framework to determine the noise level of UAM. The noise propagation to a randomly generated three-dimensional (3D) urban area was then calculated using the ray tracing method, including atmospheric attenuation and multiple reflections. 45 000 two-dimensional noise maps were used to train and evaluate the modified convolutional neural network. The results demonstrated high accuracy, with a root mean square error of only 2.56 dB compared to the ray tracing method, while reducing computation time by more than 1800 times. This model was applied to analyze the noise impact of various UAM flight conditions and landing scenarios at a vertiport. This deep learning approach is a fast method with adequate accuracy for predicting UAM noise impact in 3D urban environments. Also, it can inform the development of noise based strategies for UAM operations.

Probabilistic modeling and reasoning of conflict detection effectiveness by tracking systems towards safe urban air mobility operations

This study targets the scenario where centralized tracking systems provide tactical conflict detection for urban air mobility (UAM) flights. In this scenario, the interaction between airspace design and tracking system performances, and its impact on the effectiveness of conflict detection have not been addressed in the literature. To overcome this gap, this study aims at achieving probabilistic modeling and reasoning analysis, to provide references for stakeholders in the design of urban airspace and the deployment of flight tracking systems. A framework integrating multiple probabilistic methods is established. We formulate the event tree of pair-wise aircraft encounters with conflict detection provided by the tracking system. Then Monte Carlo simulation is performed by using an agent-based tool that we develop, to quantify the probability of event occurrences. Finally, Bayesian Networks models are built to infer the dependencies of conflict detection effectiveness on the airspace design and tracking system performances. The outcomes of this study demonstrate improvement in conflict detection that better tracking system performances can provide, and the impact of airspace design under different tracking configurations. The results can be used in the UAM traffic network planning, and systems standardization for UAM flight tracking, towards safe urban air traffic.

Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)

To meet the growing mobility needs in intra-city transportation, the concept of urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">corridors</i> , that link the aerodromes, thus avoiding the use of complex routing strategies such as those of modern-day helicopters and alleviating the burden on the ground transportation system. For safety, a UAM aircraft must use air-to-ground communications to report flight plan, off-nominal events, and real-time movement to ground base stations (GBSs). A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace and create a fast, efficient, and safe transportation system. In this paper, to characterize the wireless connectivity performance for UAM, a suitable spatial model is proposed. For the considered setup, assuming that any given aircraft communicates with the closest GBS, the distribution of the distance between an arbitrarily selected GBS and its associated aircraft and the Laplace transform of the interference experienced by the GBS are derived. Using these results, the signal-to-interference ratio (SIR)-based connectivity probability is determined to capture the connectivity performance of the UAM aircraft-to-ground communication network. Then, leveraging these connectivity results, a wireless-enabled asynchronous federated learning (AFL) framework that uses a Fourier neural network is proposed to tackle the challenging problem of turbulence prediction during UAM operations. For this AFL scheme, a staleness-aware global aggregation scheme is introduced to expedite the convergence to the optimal turbulence prediction model used by UAM aircraft. Simulation results validate the theoretical derivations for the UAM wireless connectivity. The results also demonstrate that the proposed AFL framework converges to the optimal turbulence prediction model faster than the synchronous federated learning baselines and a staleness-free AFL approach. Furthermore, the results characterize the performance of wireless connectivity and convergence of the aircraft’s turbulence model under different parameter settings, offering useful UAM design guidelines.

Large Eddy Simulation for Empirical Modeling of the Wake of Three Urban Air Mobility Vehicles

Recent advances in urban air mobility have driven the development of many new vertical take-off and landing (VTOL) concepts. These vehicles often feature original designs departing from the conventional helicopter configuration. Due to their novelty, the characteristics of the supervortices forming in the wake of such aircraft are unknown. However, these vortices may endanger any other vehicle evolving in their close proximity, owing to potentially large induced velocities. Therefore, improved knowledge about the wakes of VTOL vehicles is needed to guarantee safe urban air mobility operations. In this work, we study the wake of three VTOL aircraft in cruise by means of large eddy simulation. We present a two-stage numerical procedure that enables the simulation of long wake ages at a limited computational cost. Our simulations reveal that the wakes of rotary vehicles (quadcopter and side-by-side helicopter) feature larger wake vortex cores than an isolated wing. Their decay is also accelerated due to self-induced turbulence generated during the wake roll-up. A tilt-wing wake, on the other hand, is moderately turbulent and has smaller vortex cores than the wing. Finally, we introduce an empirical model of the vortex circulation distribution that enables fast prediction of wake-induced velocities, within a 2% error of the simulation results on average.

Challenges to the Operational Safety and Security of eVTOL Aircraft in Metropolitan Regions: A Literature Review

The start of urban air mobility operations using helicopters began in 1940s in Los Angeles to transport passengers and mail between various locations. However, the concept of urban air mobility (UAM) only emerged in the 1960s. Its implementation used helicopters to avoid congestion by land, typical of populated metropolitan regions. However, there was a severe limitation in using these aircraft because of accidents, huge operating costs, and high noise generation levels. In 2010, a new UAM concept conceived electric vertical take-off and landing (eVTOL) vehicles. These vehicles, in principle, would be safer, with lower operating costs and less noisy than helicopters. Several aircraft manufacturers have developed dozens of eVTOL models, focusing on the feasibility of using them for UAM in some metropolitan regions. However, we must consider several challenges to materialize this new concept, including those related to operational safety and security. Among these challenges is these vehicle operations integration to the airspace structure and the air traffic control system, the new embedded and ground systems development, the ground infrastructure implementation to support the operations, the reliability of air navigation systems, the meteorological information provision in real-time and with complete coverage of metropolitan airspaces to be used by eVTOL, and others. Despite some similarities, this new UAM concept will not be identical for all urban regions due to differences related to topographies, weather, urban construction patterns and air traffic control systems. Therefore, local studies are essential to support gradual and safe implementations of operations with these vehicles. This paper presents a literature review, identifying and discussing the utmost safety and security-related challenges researchers observe.

Comparing Urban Air Mobility Network Airspaces: Experiments and Insights

Urban air mobility (UAM) systems include a network of (un)structured airspaces. The geometry and operations on these networks affect system performance across several goals including safety, efficiency, and externalities. The primary goal of this work is to find and illustrate the safety, efficiency, and externality trade-offs between different styles of network architecture. To do so, this paper uses a microscopic traffic simulator for UAM aircraft to experiment with different network architectures. Key performance measures reflecting the varied system goals are considered. Comparisons of network performance at varying demand levels illustrate the different behavior of traffic and congestion for each network architecture. The results indicate that there is no one-size-fits-all solution for network designs, rather there are trade-offs between designs. Fewer network restrictions and organization allow for routing efficiencies at the cost of a higher conflict rate and greater congestion at high demand levels. Greater network restrictions and organization can reduce the conflict rate and effectively manage high levels of demand but may suffer from locally concentrated conflicts and trajectories in addition to routing inefficiency. The insights will interest airspace researchers, regulators, and UAM operators as they consider appropriate future designs of airspace to accommodate UAM operations.

Repurposing urban air mobility infrastructure for sustainable transportation in metropolitan cities: A case study of vertiports in São Paulo, Brazil

Urban Air Mobility (UAM) is an emerging transportation solution for large cities, utilizing electric aircraft with vertical take-off and landing (eVTOLs) capabilities, which operate within a network of vertiports and offer advantages such as reduced noise and emissions compared to traditional modes of transport. The objective of this study is to examine the feasibility of utilizing existing aerodrome and heliport infrastructure for UAM operations. The focus is on determining whether these locations can be repurposed to accommodate the requirements of UAM services. To achieve this, spatial analysis methods have been employed using Geographic Information Systems to investigate the São Paulo metropolitan region in Brazil, with a specific emphasis on access from São Paulo - Congonhas Airport. The results demonstrate that the existing infrastructure holds significant potential for improving urban mobility in the city. This research contributes valuable insights into the operational possibilities of utilizing existing infrastructure for UAM, paving the way for the implementation of sustainable and efficient urban transportation systems.

Developing virtual acoustic terrain for Urban Air Mobility trajectory planning

Urban Air Mobility (UAM) is a transformative concept that must operate harmoniously within the constraints imposed by societal impacts. Noise-aware flight trajectory planning can address UAM’s community noise concerns. However, the traditional trajectory optimization paradigm requires repetitive computations of a flight’s noise footprints in complex urban environments and is computationally expensive. In this work, we propose virtual acoustic terrain, a novel concept to enable an efficient trajectory optimization paradigm. By applying acoustic ray tracing and the principle of reciprocity in a complex urban environment, we convert different noise constraints into 3D exclusion zones which UAM operations should avoid to maintain limited noise impact. It combines with the physical urban terrain to define an acceptable fly zone for non-repetitive noise-aware trajectory optimization. This framework provides a new angle to future urban area airspace management and can also accommodate other forms of societal constraints.

Data-Driven Approach Toward Airspace Design for Regional Air Mobility Operations in Korea

As the Korean government agencies have recently announced the first official road map for urban air mobility (UAM) with the aim of introducing a new aviation transportation system, many companies in South Korea have initiated projects to design airspace infrastructure for UAM operations at the early stage of development. Given that the agencies tentatively plan to expand UAM to regional air mobility (RAM) operations at the mature stage of development, this research specifically focuses on establishing airspace infrastructure for upcoming RAM operations in South Korea. The proposed methodology leverages three different algorithms: 1) a partitioning-based clustering algorithm for placing vertiport locations, 2) a density-based clustering algorithm for predicting areas of convective weather, and 3) the Latin hypercube sampling-based probabilistic road map (LHS-based PRM) algorithm for generating an adaptive airspace network. The resulting airspace, constructed by the proposed methodology, takes into account airspace restriction areas such as prohibited areas or military operation areas. The main contribution of this research is to employ a data-driven approach using machine learning and LHS-based PRM algorithms to dynamically establish airspace infrastructure to be potentially used for upcoming RAM operations in South Korea.

Predeparture Flight Planning to Minimize Operating Cost for Urban Air Mobility

Urban air mobility (UAM) is envisioned to move to highly automated and high-density operations in low-altitude urban airspace in the future. Providers of services for UAM (PSU), rather than the legacy air traffic control, are anticipated to support operators with operational planning, aircraft deconfliction, conformance monitoring, and emergency information dissemination. Such services, for hundreds to thousands of simultaneous UAM operations in constrained airspace, can only be realized with automated systems. In this study, airspace and deconfliction models for generating predeparture conflict-free four-dimensional (4-D) flight trajectories are proposed, which can be further developed into an automated flight planning tool for PSU. A semistructured scalable airspace design for future UAM is proposed (i.e., a layered airspace topology with direct routes between vertiports, avoiding physical obstacles, such as buildings, obstructions, and restricted airspace) using the visibility graph method. Based on the proposed airspace design, deconfliction strategies (e.g., flight-level assignment and departure delay) are applied to obtain predeparture conflict-free 4-D trajectories of UAM operations by solving a mixed-integer programming problem, with the objective function to minimize the operating cost of UAM operations. Furthermore, sensitivity analysis is performed to investigate the impacts of three key cost parameters (electricity price, crew hourly rate, and maintenance hourly rate). The relationships of departure delay bound (maximum departure delay allowed) vs operating cost saving and departure delay bound vs delay cost to passengers are examined, as is the tradeoff between operating cost saving and passenger delay cost.

The impact of dispatching logic on the efficiency of Urban Air Mobility operations

The application of electrified propulsion systems is expected to facilitate the future operation of small aircraft in urban and suburban areas. However, in addition to the safe design of vehicles, efficient operation must also be achieved by matching passenger requests to unoccupied and suitable vehicles. In the agent-based transportation simulation MATSim (Multi-Agent Transport Simulation), a rule-based approach has been followed by assigning the nearest available vehicle to each passenger request. Sophisticated considerations such as pooling multiple passengers with identical trip requests in terms of time and location or the urgency of a request were only partially considered or not considered at all. To decrease operating costs, especially by reducing the number of passenger flights and avoiding relocation flights, appropriate dispatching is essential for efficient assignment of available vehicles to trip requests. For this purpose, the suitability of the Bipartite Matching Dispatcher (BMD), already proven for ground-based on-demand mobility concepts, was investigated for application to UAM systems by demonstrating its performance on an existing data set using a mock-up simulation and comparing it to the initial dispatcher in MATSim. Studies have not only shown the similarity between UAM and ground-based on-demand operations, but also the compatibility of BMD for high load traffic scenarios with undersized fleets. Insights on the impact of available information, for example, the prioritization of early-submitted requests over urgent requests, on the operator’s point of view can be concluded. For an efficient vehicle assignment, the consideration of passengers’ attitude towards possible comfort restrictions due to longer waiting times and incentives through price adjustments is of crucial importance.

Strategic Conflict Management using Recurrent Multi-agent Reinforcement Learning for Urban Air Mobility Operations Considering Uncertainties

The rapidly evolving urban air mobility (UAM) develops the heavy demand for public air transport tasks and poses great challenges to safe and efficient operation in low-altitude urban airspace. In this paper, the operation conflict is managed in the strategic phase with multi-agent reinforcement learning (MARL) in dynamic environments. To enable efficient operation, the aircraft flight performance is integrated into the process of multi-resolution airspace design, trajectory generation, conflict management, and MARL learning. The demand and capacity balancing (DCB) issue, separation conflict, and block unavailability introduced by wind turbulence are resolved by the proposed the multi-agent asynchronous advantage actor-critic (MAA3C) framework, in which the recurrent actor-critic networks allow the automatic action selection between ground delay, speed adjustment, and flight cancellation. The learned parameters in MAA3C are replaced with random values to compare the performance of trained models. Simulated training and test experiments performed on a small urban prototype and various combined use cases suggest the superiority of the MAA3C solution in resolving conflicts with complicated wind fields. And the generalization, scalability, and stability of the model are also demonstrated while applying the model to complex environments.

Multivehicle Point-to-Point Network Problem Formulation for UAM Operation Management Used with Dynamic Scheduling

In this paper, we introduce a new formulation of the multivehicle point-to-point network problem to be utilised in urban air mobility (UAM) vertiport-to-vertiport network operations. Vehicle routing problems (VRPs) and their variants have previously been studied and applied in real-world situations, but these problems require additional depot locations, and not all the vehicles can travel to all the locations. In UAM operations, additional depot locations may not be required, and all vehicles can travel to all locations, meaning that existing routing problems are not suitable for application to the management of UAMs. Therefore, we propose a new formulation for UAM vertiport-to-vertiport operation by introducing new constraints. In addition, we integrate dynamic scheduling with the flight mission by controlling cruise speed and waiting in each UAM at each vertiport location to generate an arrival and departure schedule for different vertiports that can avoid collisions and increase the number of vehicles. A computational experiment is conducted using an MILP model, and the results show that although our formulation satisfies the problem definition, the computation time increases exponentially with an increase in the problem size. A case study is conducted in the Seoul area involving five vertiports, with 10- and 15-vehicle scenarios studied. This case study shows that the cruise speed variable is active only for the lower and upper bounds under dynamic scheduling, whereas the waiting time variable can be controlled between user-defined limits that can be applied to the management of vertiport-to-vertiport UAM operations.

Safety Assured Online Guidance With Airborne Separation for Urban Air Mobility Operations in Uncertain Environments

The concept of Urban Air Mobility (UAM) proposes to use revolutionary new electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient and on-demand air transportation service between places previously underserved by the current aviation market. A key challenge for the success of UAM is how to manage large-scale autonomous flight operations with safety guarantee in high-density, dynamic and uncertain airspace environments. In this paper, a safety assured decentralized online guidance algorithm with airborne self-separation capability is proposed and analyzed for multi-aircraft autonomous flight operations under uncertainties. The problem is formulated as a multi-agent Markov Decision Process with continuous action space and is solved by a customized decentralized online algorithm based on Monte Carlo Tree Search (MCTS). To guarantee the safety of real-time autonomous flight operations in uncertain environments, the formulation of loss of chance constrained separation is introduced and integrated with the proposed MCTS algorithm. In addition, Gaussian process regression along with Bayesian optimization is employed to discretize the continuous action space, which helps shorten the flight time. A comprehensive numerical study shows that the proposed algorithm can provide safe onboard guidance with guaranteed low near mid-air collision probability in uncertain and high-density airspace environments.