Urban Air Mobility Operations Research Articles

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.

Drawing the Highways in the Sky for Urban Air Mobility Operations

Urban air mobility (UAM) is a new mode of intracity transportation that promises to free commuters of ground congestion by providing air-taxi services within dense metropolitan environments. The introduction of UAM operations into the airspace, however, brings challenges related to the efficient and equitable management of UAM air traffic, particularly with regard to trajectory assignment and optimization. Near-term UAM missions are anticipated to operate within structured airspace corridors, leading to the interesting problem of corridor formation. In this paper, we describe a minimum-cost, network flow optimization framework that provides global optimal corridors for a given set of origin-destination pairs, subject to airspace metrics such as airspace density, complexity, and flexibility. The framework is applied to both small-scale scenarios that demonstrate the behavior of the framework and a full-scale scenario representative of the Atlanta metropolitan area. This work contributes to understanding how to effectively integrate UAM into the airspace.

Complexity optimal air traffic assignment in multi-layer transport network for Urban Air Mobility operations

Large numbers of Urban Air Mobility (UAM) vehicles are expected to operate in urban airspace in the near future, exceeding the capacities of current airspace and Air Traffic Management (ATM) systems. This paper presents an air traffic assignment framework for 3D air transport networks in urban airspace to enable UAM operations at future demand levels. The individual vehicle dynamics are aggregated to describe the overall flow feature in this macroscopic model. Firstly, UAM operations are modeled as flows and structured in a three-dimensional two-way air transport network. Then, a complexity optimal air traffic assignment in urban airspace is formulated as an optimization problem. Based upon the Linear Dynamical System (LDS), a novel complexity metric is defined as objective function, which takes into account dynamic flow structure, congestion, and operational efficiency. A two-phase approach combining Simulated Annealing (SA) and Dafermos’ Algorithm (DA) is introduced to efficiently solve this problem. To validate the proposed model, a case study of an air transport network in Singapore’s urban airspace with two different demands is conducted. Comparative studies are carried out between the proposed algorithm and other widely used traffic assignment algorithms. The results show that the proposed approach is capable of assigning flows in an efficient and effective manner, reducing the complexity of the air transport network significantly. The results also show that optimizing the flow pattern reduces total complexity by 90.44%±0.53% and 92.12%±0.35% with 95% confidence interval, respectively in two scenarios. The framework may be useful for Air Navigation Service Providers (ANSP) in strategic planning for UAM operations and urban airspace design.

Risk-bounded and fairness-aware path planning for urban air mobility operations under uncertainty

Collision avoidance is an important issue in the field of the autonomous operations of aerial vehicles in dynamic and uncertain urban environments. This paper introduces a risk-bounded and fairness-aware path planning algorithm for multiple electrical vertical take-off and landing (eVTOL) aircraft operating in such environments. This algorithm advances the sampling-based path planning with the risk domain formulation to generate stochastically safety assured collision-free paths that are robust to both vehicle and environmental obstacle uncertainties for multiple eVTOL aircraft. To address the concern of flight fairness between different eVTOLs in the free flight airspace, the strategy of permit assignment is introduced. By incorporating the risk domain formulation into the sampling-based path planning with the strategy of permit assignment, the algorithm proposed in this paper not only inherits the computational advantage of the sampling-based methods, but also guarantees a stochastically feasible flying zone at every time step. Simulation studies demonstrate the promising performance of the proposed algorithm.

A Trajectory Evaluation Platform for Urban Air Mobility (UAM)

Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios.

Impact of Airspace Restrictions on Urban Air Mobility Airport Shuttle Service Route Feasibility

Initial urban air mobility (UAM) operations are anticipated to include airport shuttle services between airport terminals and city centers. UAM airport shuttle operations have the potential to improve the customer experience and provide airports with new revenue streams but risk interfering with current airport operations. In this paper, we develop a discrete, heuristics-based optimization framework to explore the impact of airspace constraints and the placement of vertiports in the airport terminal area on the feasibility of UAM shuttle operations. The framework is applied to the terminal areas of Hartsfield-Jackson Atlanta International Airport (ATL), Dallas Fort Worth International Airport (DFW), and Los Angeles International Airport (LAX). Small-scale UAM operations are found to be feasible at all three airports with current terminal-area procedures, particularly if UAM aircraft are classified as helicopters. Scaled-up operations are found to be most feasible at ATL because of the possibility of segregating UAM aircraft from commercial traffic, whereas scaled-up operations at LAX and DFW are expected to require the adoption of new technologies, procedures, and regulations.

Exploring General Aviation Pilot Perspectives on Proximate Urban Air Mobility Operations

Urban air mobility is a vision for new aviation operations that leverage advances in flight automation, electrical propulsion, and vertical flight technologies to address passenger and cargo needs for faster on-demand, short-range air transport. Enabling urban air mobility operations in the National Airspace System will require significant coordination across the aviation and transportation communities, including the general aviation community. The urban air mobility operating vision will introduce new and complex interactions to these pilots, driving a need for coordinated operating procedures that align with the needs of each community. This paper summarizes an exploratory human-in-the-loop experiment performed at The MITRE Corporation to explore general aviation pilot perspectives on proximate urban air mobility operations. Pilot participants used a certified flight deck simulator to fly a route in an environment with various urban air mobility traffic configurations. Objective and subjective measures were collected to understand general aviation pilot perspectives. Results indicate pilot preference for a limited number of corridors within a segment of airspace even at higher urban air mobility traffic rates. Acceptability of other urban air mobility operating concepts explored in this research study decreased with an increase in urban air mobility traffic rates.

Ground Control System for UAS Safe Landing Area Determination (SLAD) in Urban Air Mobility Operations.

The use of the Unmanned Aerial Vehicles (UAV) and Unmanned Aircraft System (UAS) for civil, scientific, and military operations, is constantly increasing, particularly in environments very dangerous or impossible for human actions. Many tasks are currently carried out in metropolitan areas, such as urban traffic monitoring, pollution and land monitoring, security surveillance, delivery of small packages, etc. Estimation of features around the flight path and surveillance of crowded areas, where there is a high number of vehicles and/or obstacles, are of extreme importance for typical UAS missions. Ensuring safety and efficiency during air traffic operations in a metropolitan area is one of the conditions for Urban Air Mobility (UAM) operations. This paper focuses on the development of a ground control system capable of monitoring crowded areas or impervious sites, identifying the UAV position and a safety area for vertical landing or take-off maneuvers (VTOL), ensuring a high level of accuracy and robustness, even without using GNSS-derived navigation information, and with on-board terrain hazard detection and avoidance (DAA) capabilities, in particular during operations conducted in BVLOS (Beyond Visual Line Of Sight). The system is composed by a mechanically rotating real-time LiDAR (Light Detection and Ranging) sensor, linked to a Raspberry Pi 3 as SBC (Session Board Controller), and interfaced to a GCS (Ground Control Station) by wireless connection for data management and 3-D information transfer.

Vertiport Operations Modeling, Agent-Based Simulation and Parameter Value Specification

Urban air mobility (UAM) is the idea of creating a future mobility market through the introduction of a new mode of aerial transport with substantial travel time advantages. A key factor diminishing travel time savings is vertiport processes. So far, vertiport throughput capacity has only been studied in a static manner using analytical methods, which has been found to be insufficient. This paper wants to increase the level of understanding of operational dynamics on vertiport airfields by being the first to apply agent-based simulation. For this purpose, an existing vertiport model consisting of pads, gates and stands was refined through two means. First, a sensitivity study with over 100 simulations was executed shedding light on the driving processes on a vertiport airfield. Second, an expert interview series with 17 participants was conducted, letting the experts evaluate the model and specify relevant parameter values. Three main results should find mention here: (1) Pad operations were identified to be most impactful on passenger delays. (2) Pad and gate processes have a threshold capacity beyond which delays increase exponentially. (3) A refined vertiport model is presented, including the 27 most relevant parameters and their value specification. In conclusion, this paper finds that optimized vertiport airfield design is crucial to UAM operations, and dynamic passenger and vehicle interactions cannot be neglected.

Pilots’ willingness to operate in urban air mobility integrated airspace: a moderated mediation analysis

Interest in advanced air mobility (AAM) and urban air mobility (UAM) operations for on-demand passenger and cargo transport continues to grow. There is ongoing research on market demand and forecast, community acceptance, privacy, and security. There is also ongoing research by National Aeronautics and Space Administration , Federal Aviation Administration, academia, and industry on airspace integration, regulatory, process, and procedural challenges. Safe integration of UAM and AAM will also require different stakeholder perspectives such as air traffic controllers, manned aircraft pilots, remote pilots, UAM operators, and the community. This research aimed to assess the willingness of manned aircraft pilots to operate in UAM integrated airspace based on airspace complexity and UAM automation level. In addition, a moderated mediation analysis was conducted using trust and perceived risk as mediators and operator type as a moderating variable. The results indicated that automation level influenced pilots’ willingness to operate an aircraft in integrated airspace. A moderating effect of operation type on automation level and willingness to pilot an aircraft was also observed: professional pilots were more amenable to UAM operations with a pilot on board compared with remotely piloted operations. Results from the study are expected to inform airspace integration challenges, processes, and procedures for UAM integrated operations.

Complexity Optimal Air Traffic Assignment for Multi-Layer Transport Network in Urban Air Mobility Operations

Complexity Optimal Air Traffic Assignment for Multi-Layer Transport Network in Urban Air Mobility Operations

Flight Guidance for Vision-Augmented Automatic Landing of Electric Vertical Take-off and Landing Vehicles

The rising interest in urban air mobility and electric vertical take-off and landing (eVTOL) vehicles brings both new possibilities and challenges to the aviation industry. To increase efficiency and reduce costs for urban air mobility operations, the aircraft will operate in a highly automated environment. In this paper, we present an experimental flight guidance system developed for conducting flight tests with a prototype eVTOL vehicle. The flight guidance logic compromises three hierarchical levels and processes input data from an external tactical collision avoidance system and a vision-based integrity monitor, which ensures that performance-based navigation requirements are satisfied. The trajectory selection module finds an energy-optimal path via pre-defined trajectories during flight. The presented system is developed according to an in-house requirements- and model-based process.

Sound Propagation Modelling for Manned and Unmanned Aircraft Noise Assessment and Mitigation: A Review

This paper addresses one of the recognized barriers to the unrestricted adoption of Unmanned Aircraft (UA) in mainstream urban use—noise—and reviews existing approaches for estimating and mitigating this problem. The aircraft noise problem is discussed upfront in general terms by introducing the sound emission, propagation, and psychoacoustic effects. The propagation of sound in the atmosphere, which is the focus of this paper, is then analysed in detail to isolate the environmental and operational factors that predominantly influence the perceived noise on the ground, especially looking at large-scale low-altitude UA operations, such as in the envisioned Urban Air Mobility (UAM) concepts. The physics of sound propagation are presented, considering all attenuation effects and the anomalies due to Doppler and atmospheric effects, such as wind, thermal inversion, and turbulence. The analysis allows to highlight the limitations of current mainstream aircraft noise modelling and certification approaches and, in particular, their inadequacy in addressing the noise of UA and, more generally, UAM vehicles. This finding is important considering that, although reducing noise at the source has remained a priority for manufacturers to enable the scaling up of UAM and drone delivery operations in the near future, the impact of poorly considered propagation and psychoacoustic effects on the actual perceived noise on the ground is equally important for the same objective. For instance, optimizing the flight paths as a function of local weather conditions can significantly contribute to minimizing the impact of noise on communities, thus paving the way for the introduction of full-scale UAM operations. A more reliable and accurate modelling of noise ground signatures for both manned and unmanned low-flying aircraft will aid in identifying the real-time data stream requirements from distributed sensors on the ground. New developments in surrogate sound propagation models, more pervasive real-time sensor data, and suitable computing resources are expected to both yield more reliable and effective estimates of noise reaching the ground listeners and support a dynamic planning of flight paths.

Formal Guarantees of Timely Progress for Distributed Knowledge Propagation

Autonomous air traffic management (ATM) operations for urban air mobility (UAM) will necessitate the use of distributed protocols for decentralized coordination between aircraft. As UAM operations are time-critical, it will be imperative to have formal guarantees of progress for the distributed protocols used in ATM. Under asynchronous settings, message transmission and processing delays are unbounded, making it impossible to provide deterministic bounds on the time required to make progress. We present an approach for formally guaranteeing timely progress in a Two-Phase Acknowledge distributed knowledge propagation protocol by probabilistically modeling the delays using theories of the Multicopy Two-Hop Relay protocol and the M/M/1 queue system. The guarantee states a probabilistic upper bound to the time for progress as a function of the probabilities of the total transmission and processing delays being less than two given values. We also showcase the development of a library of formal theories, that is tailored towards reasoning about timely progress in distributed protocols deployed in airborne networks, in the Athena proof assistant.

Urban Air Mobility: History, Ecosystem, Market Potential, and Challenges

Since the early 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century, inventors have conceptualized “plane cars” and other urban aerial transportation. Emerging innovations in electrification, automation, and other technologies are enabling new opportunities for on-demand air mobility, business models, and aircraft design. Urban air mobility (UAM) envisions a safe, sustainable, affordable, and accessible air transportation system for passenger mobility, goods delivery, and emergency services within or traversing metropolitan areas. This research employed a multi-method approach comprised of 106 interviews with thought leaders and two stakeholder workshops to construct the history, ecosystem, state of the industry, and potential evolution of UAM. The history, current developments, and anticipated milestones of UAM can be classified into six phases: 1) “flying car” concepts from the early 1910s to 1950s, 2) early UAM operations using scheduled helicopter services from the 1950s to 1980s, 3) re-emergence of on-demand services starting in the 2010s, 4) corridor services using vertical take-off and landing (VTOL) envisioned for the 2020s, 5) hub and spoke services, and 6) point-to-point services. In the future, UAM could face several barriers to growth and mainstreaming, such as the existing regulatory environment; community acceptance; and concerns about safety, noise, social equity, and environmental impacts. UAM also could be limited by infrastructure and airspace management needs, as well as business model constraints. The paper concludes with recommendations for future research on sustainability, social and economic impacts, airspace integration, and other topics.

Automated flight planning of high-density urban air mobility

Advanced Air Mobility (AAM) is an emerging concept for transporting people and cargo in urban, rural, regional, and interregional settings using revolutionary new aircraft. Urban Air Mobility (UAM), a part of AAM, focused on transport passengers in low-altitude urban airspace, has attracted extensive attention among industry, government, academia, and the public. Compared to existing commercial flights, AAM operations are expected to be at higher density; thus, an AAM system needs to be scalable, safe, and autonomous. Third-party service providers are recommended for offering air traffic management and information sharing services given the anticipated high density of UAM operations. To this end, we propose an Automated Flight Planning System (AFPS) that could be employed by such service providers. In the AFPS, a Low-Altitude Airspace Management System (LAMS) is proposed to automatically generate a route network with the capability of avoiding obstacles and obstructions in low-altitude airspace. The main components of this tool are the generation of a 3D map with LiDAR data, construction of a nodal network based on the map by using the visibility graph method, and initiation of 3D shortest paths. Given the requests of flight operations, i.e., origin, destination, departure time, the Low-Altitude Traffic Management System (LTMS) will design pre-departure conflict-free 4D trajectories and provide flexibilities of en-route maneuvering by taking system cost and equity among operators into consideration. In this study, a flight-level assignment strategy is proposed to solve the trajectory deconfliction problem in LTMS. In addition, a Nash Social Welfare Program (NSWP) is introduced to maintain fairness among different operators if the service is provided to multiple operators of UAM. A case study of the Tampa Bay area in Florida is used to demonstrate the operability of the proposed AFPS, and animations are built to visualize the optimized conflict-free operations of UAM in the study area.