The National Airspace System (NAS) comprises a complex network of aircraft, airports, airlines, and air traffic controllers. Central to NAS operations are Traffic Flow Management (TFM) and Air Traffic Control (ATC), which strategically and tactically balance traffic demand with airspace sector capacity constraints. A key initiative, the Ground Delay Program (GDP), pre-assigns departure delays during periods of high arrival demand, enhancing safety and reducing fuel costs. Despite its intended functionality, the program often faces operational inefficiencies due to weather uncertainties and deviations in flight planning. This study quantifies the performance shortfalls of current GDP operations by focusing on metrics such as "excess delay" and "wasted slots." We developed a flight delay identification algorithm and a deterministic queueing model to measure these metrics across over 1200 GDPs across multiple airports in 2019. Additionally, we employed regularized regression models and counterfactual factor contribution to further examine GDP performance and identify key factors that impact GDP-induced delays. Our analysis reveals significant opportunities for improving GDP efficiency through more precise GDP planning and better flight time compliance, potentially aided by advanced technologies like Trajectory Based Operations (TBO) and Time-Based Flow Management (TBFM).

The airport access use case is a promising early-stage application for Urban Air Mobility (UAM). Understanding the operational paradigm of UAM at airports is crucial for making equitable and effective regulatory and management decisions. A central open question is whether UAM will be integrated into the airport transportation network as a conventional scheduled transit service, such as subways and rail, or as a Transportation Network Company (TNC) characterized by dynamic supply-demand matching. In this paper, we propose a two-stage framework for conducting an economic feasibility analysis of UAM networks. In the first stage, we introduce a joint-supply-demand variable pricing problem to evaluate the impact of dynamic pricing on UAM operations. This model uses a binary logit formulation to capture the trade-off between travel time advantages and fare levels. In the second stage, the determined demand is used as input for the Electric Urban Air Mobility Vehicle Routing Problem with Non-linear Charging Time (eUAMVRP-NL), which optimizes fleet scheduling and charging decisions to derive operating revenue and cost estimates. We apply this framework to a case study of the Los Angeles International Airport (LAX) access market with an eight-spoke vertiport network. Our results indicate that UAM operations benefit significantly from TNC-like management; a variable pricing policy can increase operating profits by more than 100% compared to fixed-pricing schemes. Furthermore, we identify economies of stage length in longer UAM flights.

This paper develops models to quantify the dynamics of the impact of domestic air travel on the spread of the COVID-19 pandemic in the United States, using a wide range of datasets covering the period from March to December 2020-when international travel was restricted and become a minor source of new COVID-19 cases across country borders. With the help of flight operation data, we first develop a novel approach to estimate the county-level daily air passenger traffic, which combines passenger load factor estimates and information about the air traffic distribution. Crosssectional models using aggregated county-level variables are estimated. While this study focuses on air travel variables, we also control for potential spatial autocorrelation and other relevant covariates, including vehicle miles traveled (VMT), road network connectivity, demographic characteristics, and climate. The model results indicate that air travel has a strong and positive impact on the initial pandemic growth rate for both case-based and fatality-based aggregate models, but this impact attenuated after the first few months of the pandemic.

This paper introduces a novel joint-supply-demand optimization framework for Urban Air Mobility (UAM) operations, designed to maximize operating profit by simultaneously managing fleet scheduling and pricing. While existing research treats pricing as an exogenous factor, our model recognizes the UAM operator's dual role as both service provider and market maker. By integrating a discrete choice model into a spatial-temporal optimization framework, we capture the heterogeneity of passenger demand, including variability in travel time advantages and the value of time across different Origin-Destination (OD) markets. We apply the proposed model to a case study of the airport access market at John F. Kennedy International Airport (JFK). Our results demonstrate that the integrated approach can achieve a 60% increase in profit compared to benchmark pricing schemes that only consider temporal or spatial variability. Furthermore, sensitivity analysis reveals that the joint model effectively mitigates the "Wild Goose Chase" phenomenon by aligning pricing policy with aircraft availability. This paper provide a tool with which UAM operators can identify economically viable markets. It also allows policy makers to evaluate the integration of UAM into urban transportation networks.

Precise recognition of spatio-temporal patterns in air traffic trajectory data plays an important role in ensuring the safety and efficiency of air traffic management. Clustering is a widely used method for identifying spatio-temporal patterns in traffic trajectories. However, the high dimensionality of trajectory data often complicates the analysis, especially in terms of both temporal and spatial aspects. Here, we introduce an efficient deep clustering model using a generative approach for trajectory clustering, called Air Traffic Trajectory analysis via Latent feature Clustering (ATTLC). It features distinctive characteristics: ATTLC combines a variational autoencoder (VAE) model with a clustering algorithm to effectively learn latent features, accurately represent air traffic trajectory data, and cluster objects within the latent space. ATTLC optimizes both clustering and network loss. We demonstrate the effectiveness of ATTLC in clustering tasks on large-scale air traffic trajectory data covering China. The experiments demonstrate that ATTLC can address high-dimensional trajectory data. Most importantly, ATTLC can help analyze spatio-temporal patterns in large-scale air traffic trajectory data, potentially revealing the evolution of the trajectory networks and identifying core hub nodes.

This paper introduces a vehicle-routing formulation of the Urban Air Mobility (UAM) fleet scheduling problem. The proposed model, electric urban air mobility vehicle routing problem with non-linear battery charging time (eUAMVRP-NL), studies the optimal flight scheduling and charging policy for a given fleet of eVToL vehicles to maximize the operating revenue with the assumption of a non-linear charging model for the aircraft batteries. We design a Cluster-First Route-Second (CFRS) solution heuristic that first decomposes the k-vehicle problem into k number of 1-vehicle problems before optimizing the flight scheduling and charging decisions for each aircraft in the fleet. We validate our CFRS algorithm on a small, two-vertiport instance in which an optimal solution can be obtained. We found a loss of optimality less than 2\%. Finally, we demonstrate the use of eUAMVRP-NL in obtaining meaningful metrics for the estimation of capital cost and operating cost by considering the airport access use case of UAM at Los Angeles International Airport (LAX)

This study provides a method to quantify the benefits of shifting passenger traffic from air to high-speed rail from the perspective of flight-delay cost reduction. We first estimate the number of flight reductions for airport origin-destination pairs based on the high-speed rail ridership forecasts provided in the California High-Speed Rail 2020 Business Plan, and then distribute these flight reductions to quarter-hour intervals. Lasso models are applied to estimate the impact of reduced queuing delays at SFO, LAX, and SAN on arrival delays at the national Core 29 airports. These delay reductions are then monetized using aircraft operating costs and the value of passenger time. We evaluate alternative airport-capacity and flight-schedule scenarios, as well as multiple percentiles of probabilistic high-speed rail ridership forecasts. The resulting estimates indicate flight-delay cost savings of 51-88 million in 2018 dollars in 2029 and235-392 million in 2018 dollars in 2033.

In an era of volatile jet fuel prices and strict decarbonization mandates, reducing aircraft weight by minimizing discretionary fuel is a critical, zero-capital efficiency strategy. Despite advanced flight planning algorithms, human dispatchers frequently over-fuel to buffer against operational uncertainties, costing the industry billions annually and generating preventable emissions. This paper investigates the behavioral drivers of these decisions by integrating operational data from 175,000 flights with dispatcher psychometric survey responses. Using a newsvendor framework and structural equation modeling, we quantify a staggering systematic conservatism: on average, dispatchers value the risk of dipping into reserve fuel 1,200 times more than the cost of carrying excess fuel. We find that dispatchers with "detail-oriented" and "conservationist" personality traits load significantly less discretionary fuel, suggesting that airlines should prioritize these characteristics during hiring and recurrent training. We conclude that technological upgrades must be paired with behavioral policies to achieve optimal fuel efficiency. To modernize human-in-the-loop decision-making, we propose a suite of actionable management interventions, including choice architecture (behavioral nudging) within flight planning software, individualized post-flight feedback loops, explainable AI training to build algorithmic trust, and the recalibration of asymmetric institutional risks.

We examine how aircraft seat configuration interacts with daily operation in Regional Air Mobility by applying a joint supply-demand optimization framework that simultaneously determines market share, fare, and flight schedule. The framework integrates a binary logit discrete choice model into a task assignment formulation, capturing passengers' mode choice between Regional Air Mobility and driving across spatiotemporal origin-destination pairs. We evaluate three U.S. college town corridors under 4-, 6-, and 8-seat configurations across cost scales from 0.4 to 1.0 and fleet sizes from 12 to 30 aircraft. Profitability and throughput serve as primary performance metrics, and we analyze pricing power, operating cost, and revenue to explain performance variation across markets. We find that larger aircraft configurations and fleet sizes do not improve profitability universally. Larger aircraft are preferred where economies of scale are favorable and demand is sufficient and directionally balanced. The best configuration in these case studies is the 4-seat in imbalanced markets and the 6-seat in balanced or dense markets.

Most flight delays are caused by imbalances between traffic demand and capacity in the airport or in airspace. In the pre-tactical phase, air traffic flow and capacity management (ATFCM) is employed to align traffic demand with air traffic control (ATC) capacity, thereby enabling airlines to conduct more efficient flight operations. This paper proposes an approach that integrates flight schedule optimization (demand management) and airspace fix (i.e., waypoint) capacity setting (capacity management) in the pre-tactical phase, aiming to reduce flight delays and enhance flow stability at the target airport in a multiple-airport system (MAS). The approach is developed within a distributional reinforcement learning framework, where particle swarm optimization (PSO) replaces the traditional epsilon greedy strategy to improve training efficiency. The framework estimates reward quantiles over discrete actions to facilitate effective policy learning (optimizing flight schedules and waypoint capacity settings). Our distributional reinforcement learning framework incorporates two centralized agents, namely a flight agent and a waypoint agent, which collaborate via a shared reward function. A case study based on the MAS in the Greater Bay Area in Guangdong-Hong Kong-Macao demonstrates that limited adjustments to flight schedules, together with optimal waypoint capacity settings, can significantly reduce flight delays and ensure safe and steady traffic flow at target airport. The results also demonstrate that the proposed method achieves better performance in terms of reward acquisition, characterized by reduced flight delays and more stable target airport traffic flow, compared to traditional reinforcement learning and heuristic approaches.

The market of low-altitude economy has the po-tential to reach trillion dollars in 10 years globally.In China,it serves as a hallmark of national strategic emerging industries,and represents new quality pro-ductive forces.Exploring innovative engineering and technologies for low-altitude economy infrastructure is expected to promote sustainable growth in this sector.

Urban Air Mobility (UAM) presents a transformative vision for metropolitan transportation, but its practical implementation is hindered by substantial infrastructure costs and operational complexities. We address these challenges by modeling a UAM network that leverages existing regional airports and operates with an optimized, heterogeneous fleet of aircraft. We introduce LPSim, a Large-Scale Parallel Simulation framework that utilizes multi-GPU computing to co-optimize UAM demand, fleet operations, and ground transportation interactions simultaneously. Our equilibrium search algorithm is extended to accurately forecast demand and determine the most efficient fleet composition. Applied to a case study of the San Francisco Bay Area, our results demonstrate that this UAM model can yield over 20 minutes' travel time savings for 230,000 selected trips. However, the analysis also reveals that system-wide success is critically dependent on seamless integration with ground access and dynamic scheduling.

Disruptions in the National Airspace System (NAS) lead to significant losses to air traffic system participants and raise public concerns. We apply two methods, cluster analysis and anomaly detection models, to identify operational disruptions with geographical patterns in the NAS since 2010. We identify four types and twelve categories of days of operations, distinguished according to air traffic system operational performance and geographical patterns of disruptions. Two clusters–NAS Disruption and East Super Disruption, accounting for 0.8 of operations in U.S. air traffic system. Another 16.5 severe but still significant disruptions focused on certain regions of the NAS, while on the remaining 81.5 Anomaly detection results show good agreement with cluster results and further distinguish days in the same cluster by severity of disruptions. Results show an increasing trend in frequency of disruptions especially post-COVID. Additionally, disruptions happen most frequently in the summer and winter.

Sustainable operations have become essential for mitigating the environmental impact of air transport and ensuring the long-term viability of the industry. Accordingly, achieving existing sustainability goals is crucial for reducing carbon emissions within the sector. This study presents an evaluation of the International Air Transport Association's (IATA) ambitious sustainability goals for 2050, focusing on the potential challenges and barriers that may impede their successful realization. Through a comprehensive survey circulated among carefully selected air transport sustainability researchers attending the Air Transport Research Society (ATRS) World Conference 2025, our study gathers independent academic expert perspectives on feasibility of IATA's commitments. The survey, structured around six categories, namely estimation of goal completion, economic barriers, technological challenges, policy issues, industry resistance, and public factors - elicits expert opinions on the likelihood of achieving the stated sustainability targets. The survey reveals significant skepticism about achieving net-zero carbon emissions by 2050, with economic barriers, technological challenges, and regulatory issues being major hurdles. High costs, slow tech adoption, and lack of global regulatory frameworks are other concerns. By leveraging the expertise of air transport researchers, this study offers a unique and authoritative perspective on the challenges facing the aviation industry's sustainability efforts.

Urban air mobility (UAM) introduces new challenges for infrastructure planning, requiring data driven approaches for sustainable site selection. This study proposes USE-LFA (Urban Site Evaluation using Latent Factor Analysis), a framework designed to support equitable and environmentally conscious siting of urban ports. Applying latent factor analysis to 25 urban attributes in Seoul, the framework identifies six latent factors, grouped into two dimensions: Suitability and Attractiveness. These dimensions are combined through a tunable prioritization metric, enabling alignment with local strategic goals. The analysis uncovers spatial typologies and clustered siting patterns, highlighting regional disparities in site potential. Sensitivity analysis demonstrates that small adjustments in the Suitability Attractiveness weighting substantially affect viable site candidates, emphasizing the need for calibrated decision-making. USE-LFA facilitates interpretable and transferable analysis across different urban contexts and datasets, offering a scalable approach to integrating UAM and other emerging mobility systems into urban environments, while advancing sustainable and inclusive transport infrastructure development.

Adapting the existing power grid to support large-scale urban air mobility (UAM) operations using electric vertical take-off and landing (eVTOL) aircraft presents a critical infrastructural challenge that needs to be tackled. To this end, this paper presents a framework for estimating the potential of smart charging to improve power system welfare when integrating large-scale UAM into the power grid. We first estimate passenger travel demand for UAM from location-based service (LBS) data. Then we obtain the feasible charging window of aircraft by solving a fleet dispatching problem to maximize the fulfillment of scheduled UAM travel demand. With consideration of power grid dynamics, we optimize the timing and rate of charging in each feasible charging window to maximize power system welfare without disturbing optimal fleet dispatch. Finally, uncertainties in arrival times and charging demands are managed with chance-constrained optimization. This ensures the failure probability for meeting charging demand stays below a set confidence level. The efficacy of the proposed approach was demonstrated by case studies using a 9-bus transmission system.

The long waiting time at airport security has become an emergent issue as demand for air travel continues to grow. Not only does queuing at security cause passengers to miss their flights, but also reduce the amount of time passengers spend at the airport post-security, potentially leading to less revenue for the airport operator. One of the key issues to address to reduce waiting time is the management of arrival priority. As passengers on later flights can arrive before passengers on earlier flights, the security system does not always process passengers in the order of the degree of urgency. In this paper, we propose a chance-constrained optimization model that decides in which time slot passengers should be recommended to arrive. We use chance constraints to obtain solutions that take the uncertainty in passenger non-compliance into account. The experimental results, based on a sample day of flight schedules at the Barcelona airport, show a reduction of 85 waiting time. Compared to the deterministic case, in which passengers are assumed to fully comply with the recommendations, we see a 30 reduction of the total waiting time. This highlights the importance of considering variation in passenger compliance in the management of airport security queues.

Airport security queues often suffer from inefficiencies that result in long wait times and decreased throughput, especially at peak departure time, affecting both passengers and airlines. This work addresses the problem of reassigning passengers to specific time slots for crossing security, aiming to mitigate these inefficiencies. We frame this problem as a Minimum Cost Network Flow (MCNF) problem, enabling us to solve it exactly in polynomial time due to its linear programming structure. Our approach redistributes passenger demand across different time intervals. By optimizing the reassignment of passengers to sigma-minute time slots, we achieve significant improvements in throughput and reductions in waiting time. Preliminary results demonstrate the effectiveness of our method in enhancing operational efficiency and passenger satisfaction. The MCNF formulation offers a scalable and adaptable solution, providing long-term benefits for airport security management.