Airport Network Research Articles

Distributed decision making for unmanned aerial vehicle inspection with limited energy constraint

The unsatisfactory energy density of the state-of-art batteries imposes constraints on the practical application of unmanned aerial vehicles (UAVs). Establishing a UAV airport network that integrates energy supply and information exchange functionalities represents an ideal solution for enabling synergistic UAV operations. However, devising efficient distribution protocols for these airports remains a challenge. By leveraging modeling and analysis of the energy density of existing UAV batteries, we can forecast the flight range and distances achievable by UAVs. Here, we propose a distribution protocol for UAV airport platforms aimed at enhancing distribution accuracy by the use of AI principles. Furthermore, considering the possibility of emergency UAV stop, we introduce an emergency stop system in conjunction with standard stopping procedures to optimize distribution efficiency and enhance UAV inspection safety. Moreover, existing UAV airports usually provide energy to UAVs without harnessing UAVs to facilitate interconnection and interoperability among different airports. This inefficiency leads to significant resource wastage in energy distribution. To address this, we introduce a shared energy network that allows different companies to operate according to energy distribution needs. This network not only supplies energy to UAVs but also employs UAVs for energy collection and transportation, facilitating energy trading, business collaboration, and data transmission among diverse organizations. By enabling ubiquitous energy trading, this study provides us an ideal strategy for the future construction of energy network with interconnection and interoperability, which can be extended to other applications calling for energy distribution.

Scenario-based robust reachability analysis for networked airport delay dynamics

This work introduces a novel scheme to represent delay dynamics in airport networks. A key feature of the proposed scheme is that uncertainty affecting network operation is accounted for via a robust reachability analysis approach. In particular, the dynamical system is adopted to describe the propagation of outbound and inbound delays between airport nodes. A probabilistic reach set for this networked system is formulated as a chance-constrained optimization program. Then, we resort to a data-driven randomized computation method called scenariooptimization , where the chance constraint is replaced by a set of scenario-based deterministic constraints. Besides, the robustness of the reach set is explicitly derived through a posterior algorithm. Moreover, we conduct the extensive experiments on a dataset of real-world airport delays in China and provide some interesting results. Additionally, the case study at a refined scenario level shows the potential of the proposed approach in application.

Mining Delay Propagation Causality within an Airport Network from Historical Data

Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlation-based multivariate conditional independence (PCMCI). This method comprehensively considers all airports and causality mining in two stages. The first stage uses conditional independence tests to obtain the parent node set of the target airport, which includes both true and false causal relationships. The second stage employs instantaneous conditional independence tests to eliminate false causal relationships and obtain test statistics representing the strength of causality. Based on historical delay data from US airports over a year, the experimental results show that multiple factors cause delay propagation in airport networks rather than a single causal relationship. The scope of delay propagation is limited, mainly affecting a few airports closely connected to it. Delays at airports with small flight volumes are more likely to propagate. Few airport pairs in the network mutually propagate delays and, often, delays at airports affected by a particular airport’s delay also exhibit causal relationships with each other. This method provides a new perspective for deepening the understanding of delay propagation mechanisms in airport networks.

Infrastructure Investment and Industrial Competitiveness in Uttarakhand

In this research paper, I have provided a comprehensive analysis of “Infrastructure Investment and Industrial Competitiveness in Uttarakhand.” In Uttarakhand, the relationship between infrastructure spending and industrial competitiveness symbolizes a transforming story. By strategically investing in digital infrastructure, electricity production, and transportation networks, this northern Indian state has changed the face of its economy. Strong network of roads, railroads, and airports have eased logistical restrictions, promoting the expansion of a variety of enterprises. The state of Uttarakhand has become a global pioneer in sustainable energy because to investments in clean energy projects, notably hydroelectric electricity and solar energy, which has increased industrial production. Effective e-governance and online education have been made possible by digital connection, spurring industrial innovation. Uttarakhand is a developing industrial powerhouse because to its aggressive policies, competent people, and favorable business climate. Manufacturing, pharmaceuticals, textiles, information technology, and agriculture have all seen rapid growth as a consequence, greatly enhancing the state's economic competitiveness. This seamless fusion of economic prowess and infrastructural investment highlights Uttarakhand's amazing trajectory toward becoming a model of sustainable development and international acclaim.

Investigating the impact of spatial dependence and heterogeneity on airport relationships: Empirical evidence from China

This research focused on the relationships among 34 major airports in China, analyzing how these relationships are influenced by spatial dependence and heterogeneity. Using a spatial econometric model, it found that passenger traffic in China’s airports displays a mutually driven aggregation effect. Furthermore, the study revealed that the airport relationship changes from complementarity to competition as the geographic distance between them decreases. The study also classified the main airports in China into three hierarchical levels and found significant complementarity and competition among those within the hub-and-spoke network structure. Specifically, this study identified a complementary relationship among regional trunk and local branch airports, as-well-as significant competition among hub airports. Crucially, the study suggests that the absence of a hub airport weakens the collaborative interactions between different types of airports, emphasizing the critical importance of hub airports for the connectivity and operational efficiency of China’s airport network.

Modeling Delay Propagation in Airport Networks via Causal Biased Random Walk

Modeling Delay Propagation in Airport Networks via Causal Biased Random Walk

Spatiotemporal characteristics and influencing factors of airport service quality in China

Airport service quality (ASQ) is essential for determining the quality of ground civil aviation services. In this study, ASQ was assessed using the monthly airport aviation complaint data from 2015 to 2019 of 196 airports in mainland China (except for airports in Hong Kong, Macao, and Taiwan, which are not included in the statistics). First, we constructed a seasonal index of aviation complaints to evaluate and compare the overall temporal characteristics of ASQ in China. Second, the spatial distribution pattern of ASQ in China was determined using the aviation complaint concentration index and hot spot analysis model. Finally, the major influencing factors and categories of ASQ in China were analyzed considering spatiotemporal dimensions using the correspondence analysis method. The results revealed that there were clear seasonal differences among ASQ in China, with a high–low–low–high distribution during all four seasons. The regional agglomeration trend of airport aviation complaints was obvious, and the spatial difference in ASQ was large. Northern and western China performed better than southern China. Spatiotemporal and influencing factors of ASQ were the most strongly correlated factors in each quarter and region. The predominant source of aviation complaints across all types of airports is related to fundamental services, with check-in services identified as the most impactful category affecting ASQ. This study, based on 60 months of statistical data, offers a comprehensive evaluation of ASQ throughout the entire airport network in mainland China, from the perspective of aviation complaints. Additionally, a systematic ASQ evaluation method and research system encompassing time, space, and elements were established. This framework not only stimulates the improvement and enhancement of ASQ but also provides a theoretical foundation for differentially enhancing ASQ in regional airports. Overall, our results contribute to breaking through the qualitative research thinking system in ASQ research from a theoretical perspective, paving the way for exploring broader research in enhancing the quality of ground civil aviation services.

Feasibility of wastewater-based detection of emergent pandemics through a global network of airports.

Wastewater-based surveillance has been put into practice during the pandemic. Persistence of SARS-CoV-2 in faeces of infected individuals, and high volume of passengers travelling by air, make it possible to detect virus from aircraft wastewater, lending itself to the potential identification of a novel pathogen prior to clinical diagnosis. In this study, we estimated the likelihood of detecting the virus through aircraft wastewater from the probabilities of air travel, viral shedding, defecation, testing sensitivity, and sampling. We considered various hypothetical scenarios, with diverse sampling proportions of inbound flights, surveillance airports, and sources of outbreaks. Our calculations showed that the probability of detecting SARS-CoV-2 would increase exponentially against time in the early phase of the pandemic, and would be much higher if the 20 major airports in Asia, Europe, and North America cooperated to perform aircraft wastewater surveillance. We also found other contributors to early detection, including high sampling proportion of inbound flight at destination airports, small population size of the epicentre relative to the travel volume, and large volume of outbound travelers to major airports around the globe. We concluded that routine aircraft wastewater monitoring could be a feasible approach for early identification and tracking of an emerging pathogen with high faecal shedding rates, particularly when implemented through a global surveillance network of major airports.

FAST-CA: Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention for airport network delay propagation prediction

The issue of delay propagation prediction in airport networks has garnered increasing global attention, particularly due to its profound impact on operational efficiency and passenger satisfaction in modern air transportation systems. Despite research advancements in this domain, existing methodologies often fall short of comprehensively addressing the challenges associated with predicting delay propagation in airport networks, especially in terms of handling complex spatial–temporal dependencies and sequence couplings. In response to the complex challenge of predicting delay propagation in airport networks, we introduce the Fusion-based Adaptive Spatial–Temporal Learning with Coupled Attention (FAST-CA) framework. FAST-CA is an innovative model that integrates dynamic and adaptive graph learning, coupled attention mechanisms, periodicity feature extraction, and multifaceted information fusion modules. This holistic approach enables a thorough analysis of the interplay between flight departure and arrival delays and the spatial–temporal correlations within airport networks. Rigorously evaluated on two extensive real-world datasets, our model consistently outperforms current state-of-the-art baseline models, showcasing superior predictive performance and the effective learning capabilities of its intricately designed modules. Our research highlights the criticality of analyzing spatial–temporal relationships and the dynamics of flight coupling, offering significant theoretical and practical contributions to the advancement and management of air transportation systems.

Airport productivity and network centrality in the pandemic outbreak: Lessons from the Turkish airports

The adverse effects of the Covid-19 outbreak on airport performance have been noted in many studies, but the determinants of airport productivity in this period still need to be adequately discussed. Identifying performance drivers during the pandemic may offer insights into the efficient management of airports. In this study, the factors affecting airport productivity during the pandemic were revealed, and the performance sources were explained with attention to network centrality (indegree, outdegree, clustering coefficient, betweenness, and eigenvector), both in the domestic and international context. To do that, the Fixed Proportion Ratio was applied to measure the performances of Turkish airports from 2017 to 2020. Next, Malmquist Productivity Index was used to calculate the technical and technological changes in productivity. Finally, Gradient Boosting Modeling was employed to test the impacts of network centrality measures and other contextual variables on total factor productivity. Results revealed that international networks lost their importance, while domestic networks were the major drivers of airport productivity during the pandemic outbreak. Accessible airports with robust domestic connections (represented by high clustering coefficient and indegree centrality) were less vulnerable in this period.

A Heuristic Cutting Plane Algorithm For Budget Allocation of Large-scale Domestic Airport Network Protection

Abstract It is well known that airport security is an important component of homeland security, since airports are highly vulnerable to terrorist attacks. In order to improve the overall security of the domestic airport network, some work studied the budget allocation of domestic airport network protection. They established a minimax optimization model and designed an exact cutting plane algorithm to solve the problem. However, the exact algorithm can not solve large-scale problems in an acceptable time. Hence, this paper designs a heuristic cutting plane algorithm for solving budget allocation of large-scale domestic airport network protection. Finally, numerical experiments are carried out to demonstrate the feasibility and effectiveness of the new algorithm.

Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks

Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks

Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction

Accurate and interpretable delay predictions are vital for decision-making in the aviation industry. However, effectively incorporating spatiotemporal dependencies and external factors related to delay propagation remains a challenge. To address this challenge, we propose the SpatioTemporal Propagation Network (STPN), a novel space-time separable graph convolutional network that models delay propagation by considering both spatial and temporal factors. STPN uses a multi-graph convolution model that considers both geographic proximity and airline schedules from a spatial perspective, while employing a multi-head self-attention mechanism that can be learned end-to-end and explicitly accounts for various types of temporal dependencies in delay time series from a temporal perspective. Experiments on two real-world delay datasets show that STPN outperforms state-of-the-art methods for multi-step ahead arrival and departure delay prediction in large-scale airport networks. Additionally, the counterfactuals generated by STPN provide evidence of its ability to learn explainable delay propagation patterns. Comprehensive experiments also demonstrate that STPN sets a robust benchmark for general spatiotemporal forecasting. The code for STPN is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Kaimaoge/STPN</uri> .

Long-Term Airport Network Performance Forecasting With Linear Diffusion Graph Networks

Long-Term Airport Network Performance Forecasting With Linear Diffusion Graph Networks

Impact of COVID-19 and Recovery of Airports in the Field of Human Resources Management

The aviation industry is one of the sectors most affected by the COVID-19 pandemic. The aim of this article is to assess the impact of the COVID-19 pandemic on the area of airport human resource management and on the international airport network. COVID-19 had an unprecedented impact not only on the capacity of international seats for passengers and the gross operating income of airlines from passengers but also on airport companies, which, due to the measures taken and in order to maintain their existence, had to lay off employees. In the beginning of this paper, we discuss the impact of the pandemic on the aviation industry, caused by travel restrictions and overall economic development. In the second part, we will assess the impact of the COVID-19 pandemic on the management of human resources of the Austrian airport company and the resulting complications for airport companies in recovering from the consequences of the pandemic. Based on the obtained information and data evaluation, we came to the conclusion that the use of outsourcing services is an advantageous solution in the field of recruiting missing human resources. The added value on the part of the airport company is high-quality, trained personnel ready to perform the work, without the need for additional financial and time investments, limiting the airport operation itself and personnel who are still struggling with a lack of time and manpower. The goal of the companies is to fulfill the contracts, maintain the flow of money, and have a good name both for passengers and for the general public and all interested parties. When determining a suitable strategy using legislative conditions, it is possible to achieve cost savings in the area of human resources and the main business activities of the company.

Do Airports Affect Regional Economic Growth? Evidence From China

This paper investigates the impacts of airports on regional economic growth using county-level data in China from 2000 to 2019. Firstly, this paper constructs the air accessibility index, which measures airport activities and improves the previous measurements by adding flight frequency as the weight. Secondly, to address the endogeneity problem in determining airport location, this paper uses the instrument variable and adopts the two-stage least squares method. This paper uses historical air accessibility estimated based on the airport network in 1957 as the instrumental variable. The paper finds the following. (1) Air accessibility positively affects economic growth in China, and the impacts are differentiated by regional heterogeneity and industrial heterogeneity. (2) The construction of new airports shortens the travel time by road transport to the airport, thus reducing the total travel time from origin to destination. The effects of airport construction are significant for local economic growth. (3) This paper takes a closer look at the spatial pattern of airport economic effects. In addition, the results show that local economic effects are differentiated by their distance from airports. This study shows clear and consistent evidence that airport construction positively affects regional economies, and it is beneficial for policy-makers in formulating policies to promote local economic growth.

Transport causality knowledge-guided GCN for propagated delay prediction in airport delay propagation networks

Flight delays pose a worldwide challenge that significantly affect the safety and efficiency of air transportation systems. However, propagated delay prediction, as well as its causality among airport delay propagation networks, has not considered some crucial issues regarding spatiotemporal dependence and propagation relationships. Thus, this study proposes a transport causality knowledge-guided extended graph convolutional network (GCN) framework to tackle these issues. In particular, a causality knowledge-guided airport delay propagation network (ADPN) is developed using the second modified transfer entropy (SMTE) principle. Furthermore, a causality-embedded adjacency matrix is utilized by an extended GCN for propagated delay prediction. Comprehensive validations and results indicate that the proposed method benefits significantly from the causality knowledge, and increases the prediction performances up to 15.51%. Thus, transport causality is significant and efficient for understanding propagated delay features and airport delay propagation network characteristics.

Towards analyzing the robustness of the Integrated Global Transportation Network Abstraction (IGTNA)

The well-functioning of our transportation systems is essential for ensuring the mobility of people and goods. According to the Sustainable Developmental Goals (SDG) adopted by the United Nations General Assembly in 2015, it is of utmost importance to build and maintain a resilient infrastructure. In terms of transportation systems, a resilient infrastructure guarantees seamless connections and robustness against failures and targeted attacks. Existing studies on small-scale road, subway, or air transportation networks have revealed interesting patterns and phenomena for different parts of the world. These studies, however, are usually either spatially or modal-wise constrained. Most notably this holds for the worldwide airport network, which has frequently been analyzed in the literature. Transportation interactions on the ground are usually considered through aggregation of nodes into either cities or multiple airport regions. This conceptual simplification leaves room for the analysis of an Integrated Global Transportation Network Abstraction (IGTNA), covering aviation and ground transportation on our planet. We believe that it is crucial for planners and analysts to include all infrastructure, assets, networks, modes, and users as a single system. IGTNA enables to model all aspects of interconnectivity at a high-level of details, encoding points of interests as nodes and potential transitions between points of interests as links. Our aggregated network representation of the global transportation system consists of 5.3 million nodes and more than 8.6 million links. We propose scalable data extraction and network dismantling techniques. This study is, to the best of our knowledge, the first to report on the robustness of the IGTNA. We believe that our study enables decision-makers to achieve effective decision-making on future transportation investment through a holistic understanding of the impacts from negative shocks of disruptive events.

Airport Cluster Delay Prediction Based on TS-BiLSTM-Attention

To conduct an accurate and reliable airport delay prediction will provide an important basis for the macro control of an airspace delay situation and the dynamic allocation of airspace system capacity balance. Accordingly, a method of delay prediction for target airports based on the spatio-temporal delay variables of adjacent airports is proposed in this paper. First, by combining the complex network theory, we first extract the topology of the airport network and create airport clusters with comparable network properties. Second, we develop the TS-BiLSTM-Attention mode to predict the delay per hour for airports in the cluster. As the spatio-temporal feature variables, the arrival delay of airport cluster-associated airports and the delay time series of landing airports are utilized to reach the conclusion. The experimental results indicate that the delay prediction predicated on clusters is superior to that based on data from a single airport. This demonstrates that the delay propagation law derived from cluster data based on spatio-temporal feature extraction can generalize the delay propagation characteristics of airports within clusters.

Modeling and Invulnerability Analysis of Multilayer Air Traffic Network considering Altitude Layer

In this paper, a network coupling mechanism is studied to couple the air sector network, airport network, and air route network into a multilayer air network model. Then, the altitude layers are divided into three: high, medium, and low, and the arrival and departure flight procedures are also considered. By defining the association between the altitude layers and the waypoints, a multilayer air network model considering the altitude layer is constructed. Then, the line graph theory is used to redefine the nodes and edges, and the network is reconstructed to obtain a new single-layer one which is easy to calculate. Finally, a case study is carried out in Chengdu control area. The results show that the proposed model is closer to the reality, and the invulnerability performance is more referential. Besides, it also reflects the impact of altitude layers on the efficiency of airspace. The results are helpful for air traffic controllers to manage airspace better and have significance for promoting air traffic safety and stability.