American Airlines (United States)

Airspace traffic complexity is closely related to the safety and operational efficiency of civil aviation. To enhance the accuracy of complexity assessment, this paper proposes an adaptive metric learning based airspace traffic complexity assessment method(ATCA-AML). First, a multi-resolution air traffic image dataset is constructed to capture the variation of airspace operational states across different spatiotemporal scales. On this basis, an adaptive metric learning model is designed, which takes multi-resolution images as input and employs a dynamic multi-proxy generation method to optimize the distribution of samples in the embedding space. This enables effective classification of airspace traffic complexity into multiple levels. Experiments conducted on real data from the Central-Southern China airspace demonstrate that the spatiotemporal resolution of traffic images has a significant impact on assessment performance. Moreover, compared to existing approaches, the proposed method achieves superior accuracy and discriminative capability, offering a more precise reflection of the dynamic complexity of airspace operations.

Abstract To address the limitations of weak interpretability and poor generalization in existing taxi-out time prediction models, this study proposes a novel prediction model for departing flights based on Stacking ensemble learning and Shapley additive explanations. Firstly, decomposing taxi-out time into unimpeded taxi-out time and dynamic taxi-out time, followed by separate correlation analysis with influencing factors. Then, constructing a Stacking-based prediction model with comparative evaluation between holistic and phased prediction approaches. Finally, implementing SHAP analysis to quantify feature importance, and validate the rationality of the model using actual operating data from Shenzhen Bao'an international airport of China. The results indicate that: (1) Unimpeded taxi-out time is mainly influenced by the configuration of the airport, while the dynamic taxi-out time is mainly influenced by surface traffic flow; (2) Phased prediction shows enhanced interpretability despite marginally inferior performance (MAPE:10.6%, MAE:99.7s, RMSE:140.5s) compared to holistic prediction; (3) The Stacking model achieves superior accuracy (± 60s/±180s/±300s prediction rates: 41.0%/86.3%/96.5%) and generalization capability over existing methods; (4) The dual feature selection mechanism based on Shapley analysis and correlation analysis can ensure high prediction accuracy of the model while effectively reducing feature dimensions. (5) SHAP analysis was employed to quantify feature impacts on taxi-out time and decode feature interactions, thereby demystifying the model's black-box nature and offering actionable insights for air traffic controllers' decision-making.

This paper contributes to the finance literature by investigating the risk contagion and bailout effects in the global financial system. We use the bilateral exposure matrix to examine the impact of credit shocks and liquidity shocks on the global financial system when a Global Systemically Important Bank (G-SIB) is supposed to be in distress. The findings indicate that the global financial network is dominated by large and medium-sized financial institutions. Credit shocks does not play a significant role in risk contagion nor do they trigger the systemic risk in the global financial system. However, the global financial network is more vulnerable to liquidity shocks than to credit shocks. If a credit shock and liquidity shock overlaps with each other, the hybrid shock can exacerbate the risk contagion effects and lead to rapid propagation throughout the global financial network. If a regulator bails out the distressed institutions, the bailout efficiency increases with the number of rescued institutions. However, the improvement in of bailout efficiency decreases with the number of rescued institutions. If a regulator can promote financial institutions to raise the rollover ratio, the financial system will be more robust to shocks. This paper provides a ground view of systemic risks contagion and sheds light on directions for future research on systemic risk contagion in the global financial system. • The credit shock does not play a significant role in risk contagion in the global financial system. However, the liquidity shock has shown more substantial effects than that of the credit shock. • If the credit shock and liquidity shock overlaps with each other, the contagion effects are amplified. • If the regulator implements the bailout, the total rescue effects in the second round is better than that of other rounds. • If the financial institutions can raise the rollover ratio, the impact of systemic risk could be reduced significantly.

Purpose: This study investigates the relationship between the choice attributes of the passengers of a Korean airline and relationship quality during the COVID-19 pandemic in South Korea. Design/Methodology/Approach: This study used a quantitative approach and conducted an online survey using Google Forms through the convenience sampling method on users with experience in domestic and international airlines within the past month. It received 300 valid responses. The data were analyzed using Statistical Package for the Social Sciences version 21. Findings: This study hypothesized that a significant relationship exists between airline selection attributes and the quality of relationships. The results demonstrate that satisfaction significantly influences trust. Conclusion: This study demonstrated that choice attributes as perceived by passengers exert a significant influence on determining the quality of relationships between airlines and customers. Research Limitations and Implications: The data were collected using an online questionnaire due to the COVID-19 situation. This aspect is a limitation of the study because it is highly dependent on the honesty and willingness of the respondents. Practical Implications: This study found that airlines can improve the quality of relationships with customers by overcoming crises if they can increase satisfaction by grasping the selection attributes pursued by customers. This study suggests appropriate marketing strategies for airline managers in Korea.

<div class="section abstract"><div class="htmlview paragraph">The aerospace ecosystem is a complex system of systems comprising of many stakeholders in exchanging technical, design, development, certification, operational, and maintenance data across the different lifecycle stages of an aircraft from concept, engineering, manufacturing, operations, and maintenance to its disposal. Many standards have been developed to standardize and improve the effectiveness, efficiency, and security of the data transfer processes in the aerospace ecosystem. There are still challenges in data transfer due to the lack of standards in certain areas and lack of awareness and implementation of some standards. G-31 standards committee of SAE International has conducted a study on the available digital data standards in aircraft asset life cycle to understand the current and future landscapes of the needed digital data standards and identify gaps. This technical paper presents the study conducted by the G-31 technical committee. This paper reviews the data being exchanged between various stakeholders in the aerospace asset lifecycle and the availability of standards for the data transfer within the aerospace ecosystem. It identifies gaps based on the list of currently available data standards, and then creates a future landscape to address the needed digital data standards. This paper focuses on aircraft operations, maintenance, transfer, disposal processes, and post-build stage, and does not address the detailed interactions during the aircraft design, development and manufacturing phases. Its scope is also limited to key stakeholder interactions throughout the different stages of the aircraft operations, maintenance, and retirement.</div></div>

We consider a solar microgrid design and dispatch problem using an adaptive stochastic optimization framework. First, we propose a two-stage mixed-integer model for optimal placement and planning of distributed generation (DG) units and energy storage system units. We incorporate time series modeling into stochastic optimization approach to characterize the solar irradiance uncertainty. In the first stage, design decisions (e.g., location and sizing of DGs) are made and in the second stage, dispatch decisions (e.g., how much to generate, how much to store) are made such that electricity demand is met in a reliable and cost effective way. Chance constraints are employed to control the real load shedding within a predefined probability level. Then, we propose a combined sample average approximation (SAA) and linearization technique to solve this problem more efficiently. The advantage of this approach is that no additional binary variables are introduced while reformulating the chance constraints. Computational time, quality of solution, and load shedding percentage are compared with the traditional SAA. Moreover, we carry out a comprehensive out-of-sample simulation on a real-world case study in the state of Arizona assessing the effectiveness of our approach. Numerical experiments demonstrate that the chance constraints are effective tools for control of load shedding in distributed generation and the proposed approach outperforms traditional methods both in terms of true load shedding percentage and computational time.

SummarySelective maintenance is often applied in many industrial environments where maintenance is performed between sequence missions. When the mission time is stochastic and there are multiple maintenance workers with different capacities, the system reliability of the next work mission can be maximized by using a stochastic model under the constraint of the limit maintenance time. The optimal maintenance strategy is obtained with an optimization algorithm. A simulation was performed to verify the validity and feasibility of the proposed model.