Manouk van der ZwanGuelcin ErmisAlexei Sharpanskykh
102855.1-102855.23页
查看更多>>摘要：We aim to contribute to the automation of ground handling tasks using autonomous ground support equipment (GSE) at airports. Automation of airside operations has recently become critical for the airports to achieve higher levels of safety and efficiency under growing traffic demand and requires solving a complex scheduling and path planning problem. To address this problem, we present a multi-agent task allocation and path planning model for handling airside operations on the apron. In the problem, the ground handling tasks are to be allocated to the equipment, the trips of vehicles should be scheduled within specific time windows considering the flight schedules, and the collisions of vehicles on the apron and service roads should be avoided. We present a centralized multi-agent task allocation and routing model which aims to optimize the allocation and routing of various types of ground handling tasks over a heterogeneous set of GSE vehicles. We convert the allocation and routing problem into vehicle routing problem with time windows, pick-ups, deliveries and solve the problem using a warm start mixed integer linear programming (MILP) model. We also introduce a nonlinear objective function which converts the MILP model into a mixed integer nonlinear programming (MINLP) model, to minimize the time service locations at the stands are occupied. Then, we solve the corresponding path finding problem to find collision free paths for the GSE, by the multi-agent path finding model. The proposed model outperforms the decentralized approach in previous research regarding the allocation rate of assigning tasks to vehicles and the performance indicators of finding conflict free paths, and in CPU time. The mean deviations from shortest paths were considerably small in path planning which means that the solution quality was high. Furthermore, the CPU time of allocating tasks has been reduced by 48% compared to the CPU time of decentralized allocation.
查看更多>>摘要：Wind shear (WS) refers to an abrupt change in wind speed and/or direction, whether in a vertical or horizontal direction. In particular, low-level wind shear (hereafter LLWS) is a type of WS that occurs at or below an altitude of approximately 1600 ft (500 m) and thus affects aircraft operations during landing or take-off phases. Jeju International Airport (CJU) is well-known for experiencing frequent LLWS and consequent occurrence of go-around (GA) operations (also referred to as missed approach). LLWS is known to be elusive and thus difficult to predict. Most previous studies are concerned with elucidating LLWS from a meteorological angle, without considering its potential effects on flight operations. In this study, we investigate the weather conditions that lead to LLWS at CJU airport and then seek the linkage between LLWS and go-around operations. General weather information and flight records containing aircraft speed, altitude, and specific weather observations during GA at CJU airport are collected. We empirically categorize five wind patterns that contribute to severe LLWS and necessitate go-around operations. In this paper, we drive a probability table that summarizes the chances of go-around operations according to the wind direction and speed. We also discuss limitations and areas for future research.
查看更多>>摘要：This study explores the route expansion process and network performance of four major LCCs in China since their inception, analyzing their tactical actions toward HSR and the underlying drivers of these trends. The findings suggest that the Chinese LCCs' routes had undergone an expansion from east to west, with a gradual westward shifting trend. Compared with FSAs, the LCCs prefer to establish new services in Southern China, which has a higher level of economic development and population density. Of course, the LCCs do not blindly pursue new routes between regions, instead, they continuously improve the internal route network to gain a stronger market position. This strategy is manifested in the fluctuating increase in the flying distance of newly opened LCC routes (Calzada and Fageda, 2019). This might be in response to the competition from FSAs and HSR. LCCs gradually abandon short-haul routes while concentrating capacity on routes exceeding 800 km, which is consistent with Wang et al. (2017)'s finding. Meanwhile, the LCCs improved the performance of their networks, with different change patterns. Furthermore, the LCCs all experienced a significant surge in the number of new routes but reduced the routes within 800 km. There are various potential explanations, one of which should be that the LCCs seek to launch new routes not served by HSR, as well as longer routes HSR cannot effectively support.
查看更多>>摘要：Airport operational resilience is a crucial metric reflecting an airport's capacity to adapt to external shocks, essential for maintaining safety and operational efficiency. While there has been research on airport resilience under various severe weather conditions, the specific contributing factors and their impacts remain inadequately explored. This study develops a comprehensive index system that integrates airport performance and meteorological data, using a random forest algorithm to quantify the influence of various factors on airport resilience across five types of severe weather. Furthermore, a PatchTST(Patch time series Transformer)-based time series model improved by the Cauchy loss function is proposed to accurately predict airport operational resilience. Focusing on severe weather events during the period from January 2023 to July 2024 at Dallas-Fort Worth International Airport in the United States. To mitigate multicollinearity, variables with high Pearson correlation and variance inflation factor (VIF) values were removed prior to analysis. Feature importance results reveal that hourly flight movements (HFM) consistently hold the highest importance across weather types, while temperature (TEMP), relative humidity (RHUM) and air pressure (PRES) exhibit relatively higher meteorological influence despite limited overall impact. The optimal Cauchy-PatchTST model, with a look-back window of L = 36 and a forecast length of T = 1, outperforms the traditional PatchTST model with MSE loss, three Transformer-based models and other optimized machine learning algorithms, achieving a 15.49% to 94.10% reduction in MAE on the test set. This study provides critical indicator analysis for airports across various severe weather conditions and offers reliable resilience data to support future operations management.
查看更多>>摘要：Following a challenging start to the 21st century, airlines rebounded to achieve record profits in the aftermath of the Great Recession (2007-2009). While profitability refers to the absolute financial gains of a firm, profit efficiency is a measure of how effectively a firm converts its resources into maximum potential profit, given its operating environment and input prices. These distinct economic concepts raise key questions about airline strategies: Can airlines maximize their profits? Which business models achieve higher profit efficiency? What factors influence their profit efficiency? This study addresses these questions using a stochastic profit efficiency model based on data from U.S. airlines spanning from 2009 to 2019. Our findings reveal that the U.S. airline industry exhibits an average profit efficiency of 93.2 %. Low-Cost Carriers (LCCs) have a higher mean efficiency score of 98.7 % while Full-Service Airlines (FSAs) follow them with 95.3 %. Ultra-Low-Cost Carriers (ULCCs) have the lowest profit efficiency at 86.1 %. Finally, LCCs have demonstrated more stable profit efficiency over the years. In addition, ancillary revenues positively impact the profit efficiency, indicating higher markup resulting from add-on pricing. The strategies implemented after the Great Recession—such as capacity discipline and mergers—have significantly increased profit efficiency while the airport network expansion result in lower profit inefficiency. Overall, this study highlights the extent of profit efficiency for the U.S. airline industry and identifies the key factors influencing it.
查看更多>>摘要：Efficient air traffic management (ATM) relies on accurately understanding and predicting air traffic patterns and delays. While deep learning methods have shown promise in prediction tasks, they often lack interpretabil-ity and require large volumes of data. This paper presents a novel, data-driven framework to model and predict near-terminal traffic flow and flight delays by identifying the underlying partial differential equations (PDEs) that govern air traffic dynamics. Our approach leverages aircraft trajectory patterns and density distributions to estimate probability density functions (PDFs) of travel times. Using sparse regression for system identification, we learn the governing equations that capture the temporal evolution of density and travel time distributions. These equations are then embedded into a Physics-Informed Neural Network (PINN) for integrated prediction. Experiments with real-world data validate the framework's effectiveness in accurately identifying governing PDEs and forecasting flight delays. By combining physical modeling with deep learning, the proposed method improves both the interpretability and generalizability of AI applications in ATM, offering practical value in enhancing airport efficiency and operational decision-making.
查看更多>>摘要：Artificial Intelligence (AI) is reshaping the aviation industry, driving efficiency, automation, and innovation across multiple operational domains. This study examines commercial airline passengers' perceptions of AI's role in addressing key industry challenges, including air traffic management, predictive maintenance, passenger experience, and sustainability. Using a quantitative approach, a survey was conducted among 320 airline passengers in Spain to assess their attitudes toward AI-driven applications in aviation. The findings reveal strong support for AI in optimizing flight operations, reducing delays, and enhancing security procedures. However, significant skepticism remains regarding AI's autonomy in decision-making, particularly in pilot replacement and automated flight rerouting. Statistical analyses indicate that younger and frequent travelers exhibit higher confidence in AI's potential, whereas older passengers demonstrate greater reluctance toward Al-driven automation. Additionally, AI is perceived as a crucial enabler of environmental sustainability, with respondents acknowledging its role in reducing fuel consumption and emissions. These insights provide valuable implications for policymakers, airlines, and technology developers seeking to align AI adoption with passenger expectations while ensuring safety, efficiency, and regulatory compliance. The study highlights the need for a balanced approach that integrates AI's technological advancements with human oversight to foster trust and acceptance in the future of AI-powered aviation.
Jianhua PiXingjian LiuWill W. QiangChris Webster...
102875.1-102875.11页
查看更多>>摘要：China's implementation of Airport Economic Zones (AEZs) seeks to capitalize on aviation infrastructure for local development. While existing studies have assessed airport-related urban development in China, the specific impacts of AEZ policies on local economies remain underexamined. To this end, our study evaluates the impacts of AEZs on local economies, utilizing a panel dataset of 62 prefecture-level cities in China spanning 2000-2019. We employ a heterogeneous timing difference-in-differences method to assess localized economic impacts of AEZs, considering three specific treatment timings. The results show that AEZs have positive but limited impacts on localized economic growth, particularly evident in increased economic activities around airports. Local economic impacts of more recently announced national airport economic demonstration zones are insignificant in the analysis. Meanwhile, the establishment of other kinds of development zones around airports fosters nearby economic activity and employment in airport-related sectors, oftentimes with higher levels of statistical significance. These findings add empirical evidence for airport region development's impact on economy and underscore the importance of institutional support for maximizing AEZs' contributions to urban development.
查看更多>>摘要：This study examines how capacity purchase agreements (CPAs) between major U.S. network carriers and then-regional airline partners affect schedule padding practices. Using a large dataset of over 7 million flight-level observations from 2023 and distinguishing between flights operated directly by mainline carriers and those operated by regional partners under branded CPA arrangements, we assess whether CPAs incentivize airlines to pad schedules more aggressively. Results consistently show that CPA-operated flights have significantly more schedule padding than mainline-operated flights, with the effect being most pronounced in long-haul markets. These findings amplify the dual-edged nature of such partnerships: while CPAs support network connectivity and operational reliability, the systematically longer scheduled block times may reduce aircraft utilization.
查看更多>>摘要：This study examines the interplay between air passenger transport and international tourism through the role of GDP (Gross Domestic Product) and trade openness in 11 key markets of Singapore. Co-integration tests indicate that although air passenger transport and international tourism may not be individually stable in some markets, their fluctuations are strongly and durably related. The estimation results using a combined SARIMA (Seasonal Autoregressive Integrated Moving Average), X (exogenous factors), and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model show that there is a strong interrelationship between air passenger transport and the number of international tourists to Singapore, but air passenger transport is less sensitive to fluctuations in the number of international tourists. GDP and trade openness also have significant impacts on the demand for both sectors, but at different levels across markets. The cyclical, seasonal, and external shock effects found in air passenger transport and international tourism indicate the diversity in behavior and characteristics of each market. The research results provide a basis for managers and policymakers to forecast and formulate development policies for tourism and air transport.
NETL
NSTL
Elsevier