Airport Network Research Articles

Measuring Onward Connectivity with a Prescriptive Approach

Although connectivity has been the focus of several topics in the research field, onward connectivity has received less attention, despite that it can shed light on various issues in the context of a mature air transport market and an increasingly dense airport network. We propose several ways to measure onward connectivity, some of which are based on the connectivity literature whilst others are suggested in the light of a qualitative study on the needs and wants of a particular customer segment in the case study of Castellon airport, Spain. We apply our measures to this case to determine the best hub to connect to for this segment and provide the connection that maximizes onward connectivity. We find that “demand catered for,” an onward connectivity measure that balances the number of destinations with the interest of the potential customers, is a good tool to choose the best hub. We also propose the adjustment of a measure based on connectivity literature (the global connectivity index) which delivers a more precise approach to choosing the best time of the day to connect to the hub. Our work opens new venues for studying connectivity from a perspective based on market demand as well as using onward connectivity for prescriptive applications and is potentially useful for almost one in two airports (particularly new and small ones), as well as for other actors in the commercial aviation industry.

Global tourism, climate change and energy sustainability: assessing carbon reduction mitigating measures from the aviation industry.

As many business activities—especially those associated with the energy-intensive industries—continue to be major sources of greenhouse gas emissions, and hence significantly contributing to global warming, there is a perceived need to identify ways to make business activities eventually carbon neutral. This paper explores the implications of a changing climate for the global tourism business and its intertwining global aviation industry that operates in a self-regulatory environment. Adopting a bibliometric analysis of the literature in the domain of global tourism and climate change (772 articles), the paper reveals the underlying sustainability issues that entail unsustainable energy consumption. The aviation industry as a significant source of carbon emission within the sector is then examined by analyzing the top 20 largest commercial airlines in the world with respect to its ongoing mitigating measures in meeting the Paris Agreement targets. While self-regulatory initiatives are taken to adopt Sustainable Aviation Fuels (SAF) as alternative fuel production and consumption for drastically reducing carbon emission, voluntary alignment and commitment to long-term targets remain inconsistent. A concerted strategic approach to building up complementary sustainable infrastructures among the global network of airports based in various international tourist destination cities to enable a measurable reduction in carbon emission is necessary to achieve a transformational adaptation of a business sector that is of essence to the recovery of the global economy while attempting to tackle climate change in a post-COVID-19 era.

Sequential motifs in observed walks

Abstract The structure of complex networks can be characterized by counting and analysing network motifs. Motifs are small graph structures that occur repeatedly in a network, such as triangles or chains. Recent work has generalized motifs to temporal and dynamic network data. However, existing techniques do not generalize to sequential or trajectory data, which represent entities moving through the nodes of a network, such as passengers moving through transportation networks. The unit of observation in these data is fundamentally different since we analyse observations of trajectories (e.g. a trip from airport A to airport C through airport B), rather than independent observations of edges or snapshots of graphs over time. In this work, we define sequential motifs in trajectory data, which are small, directed and sequence-ordered graphs corresponding to patterns in observed sequences. We draw a connection between the counting and analysis of sequential motifs and Higher-Order Network (HON) models. We show that by mapping edges of a HON, specifically a $k$th-order DeBruijn graph, to sequential motifs, we can count and evaluate their importance in observed data. We test our methodology with two datasets: (1) passengers navigating an airport network and (2) people navigating the Wikipedia article network. We find that the most prevalent and important sequential motifs correspond to intuitive patterns of traversal in the real systems and show empirically that the heterogeneity of edge weights in an observed higher-order DeBruijn graph has implications for the distributions of sequential motifs we expect to see across our null models.

Research on slot allocation for airport network in the presence of uncertainty

In this paper, a slot allocation model for airport network in the presence of uncertainty is proposed. The airport resource planning problem is modeled as a process from an aggregate level and an individual level for multiple days' slot scheduling in order to consider capacity uncertainty at airports characterized by meteorological scenarios. The aggregate level uses a two-stage model to minimize the sum of displacement cost and delay cost in the worst-case scenario using Benders’ Decomposition-based algorithm. At the individual level, an integer linear programming model is developed based on the results from the aggregate level to provide specific departure slot and arrival slot for each flight. An airport network composed of 15 coordinated airports in China is studied and shows that by experimental analysis the overall optimization method is capable of providing robust and efficient solutions for slot allocation and benefits from high computational efficiency. The outperformance of the two-stage optimization method from the aggregate level is also illustrated in comparison with the one-stage optimization method. Finally, recommendations for the slot allocation policy in practice are provided.

A Deep Learning Approach for Flight Delay Prediction Through Time-Evolving Graphs

Flight delay prediction has recently gained growing popularity due to the significant role it plays in efficient airline and airport operation. Most of the previous prediction works consider the single-airport scenario, which overlooks the time-varying spatial interactions hidden in airport networks. In this paper, the flight delay prediction problem is investigated from a network perspective (i.e., multi-airport scenario). To model the time-evolving and periodic graph-structured information in the airport network, a flight delay prediction approach based on the graph convolutional neural network (GCN) is developed in this paper. More specifically, regarding that GCN cannot take both delay time-series and time-evolving graph structures as inputs, a temporal convolutional block based on the Markov property is employed to mine the time-varying patterns of flight delays through a sequence of graph snapshots. Moreover, considering that unknown occasional air routes under emergency may result in incomplete graph-structured inputs for GCN, an adaptive graph convolutional block is embedded into the proposed method to expose spatial interactions hidden in airport networks. Through extensive experiments, it has been shown that the proposed approach outperforms benchmark methods with a satisfying accuracy improvement at the cost of acceptable execution time. The obtained results reveal that deep learning approach based on graph-structured inputs have great potentials in the flight delay prediction problem.

Optimal Supply Chain Design for Renewable Aviation Fuel Production in México Considering the Full Use of Nonedible Biomass

Renewable energy resources have received attention due to concerns about oil depletion and climate change consequences. In the aviation sector, the development of renewable aviation fuel is identified as a promising alternative to replace fossil jet fuel in the current infrastructure. Thus, obtaining the alternative optimal designs to satisfy the upcoming demand has become a crucial factor. Therefore, a mathematical model of biojet fuel production supply, by hydroprocessing inedible oils (such as Jatropha curcas, camelina, palm stearin, and Ricinus communis), was proposed along with renewable hydrogen production (produced from residual biomass in oil extraction). Considering the Mexican airport network, it was possible to identify the best economic scenario with a profit of 16,893,146 USD/y and 27.6% CO2 emission reduction, in comparison with fossil jet fuel use. Additionally, the best solutions show that it is possible to satisfy the national jet fuel demand (4,157.5 ktonne/y) with the produced renewable fuel jet and even increase the profit using surplus sales (315 ktonne/y). This is possible with the amount of biomass harvested in 26 states in Mexico, and only 11 biojet fuel production facilities are installed.

Mapping ex ante risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data.

A rapid response to global infectious disease outbreaks is crucial to protect public health. Ex ante information on the spatial probability distribution of early infections can guide governments to better target protection efforts. We propose a two‐stage statistical approach to spatially map the ex ante importation risk of COVID‐19 and its uncertainty across Indonesia based on a minimal set of routinely available input data related to the Indonesian flight network, traffic and population data, and geographical information. In a first step, we use a generalised additive model to predict the ex ante COVID‐19 risk for 78 domestic Indonesian airports based on data from a global model on the disease spread and covariates associated with Indonesian airport network flight data prior to the global COVID‐19 outbreak. In a second step, we apply a Bayesian geostatistical model to propagate the estimated COVID‐19 risk from the airports to all of Indonesia using freely available spatial covariates including traffic density, population and two spatial distance metrics. The results of our analysis are illustrated using exceedance probability surface maps, which provide policy‐relevant information accounting for the uncertainty of the estimates on the location of areas at risk and those that might require further data collection.

The time cost of saving money: detouring and connecting time losses in the virtually interlined European airport network

Virtual interlining, the use of actively marketed self-connecting flight itineraries, is often assumed to be a money-saving air travel strategy. Earlier research on this topic broadly confirmed the money-saving character of virtual interlining, but to date non-monetary costs associated with this price advantage have not yet been systematically examined. In this paper, we address this lacuna by juxtaposing the price advantage of virtual interlining with the potential time costs for the case of indirect flight itineraries in the European airport network. Focusing on those markets where the cheapest virtually interlined itinerary renders a price advantage over its indirect traditional counterpart, we analyse the time cost from two complementary perspectives: (1) connecting time and (2) detour factor. To this end, we query Kiwi.com’s Tequila platform to obtain data on all available flight itineraries in the first week of August, October and December 2019. Based on a series of sign tests, we reveal the time costs of saving money: while virtually interlined itineraries render a price advantage compared to their indirect traditional counterparts, they come with a significantly larger connecting time and detour factor. We reflect on possible explanations, and highlight a number of avenues for future research.

Topology and Robustness of Weighted Air Transport Networks in Multi-Airport Region

Topological analyses of multi-airport regional air transport networks are the basis for the sustainable development of multi-airport systems. In this study, we modeled the Yangtze River Delta (YRD) region’s airport infrastructure as a network and presented a weighted approach by which to analyze the network structure and robustness from the perspective of complex network theory. The analysis of the Yangtze River Delta Airport Network (YRDAN) indicates that it is a small-world network, and its cumulative degree has a power-law distribution, suggesting that it has scale-free properties. As its weighted clustering coefficient was found to be much smaller than the non-weighted counterpart, this demonstrates that most of the network traffic is focused on a hub-and-spoke pattern. Furthermore, the over-centrality of the YRDAN suggests weak accessibility of small cities and high dependence of air transport on the hub-and-spoke pattern. The assessment of the robustness of the YRDAN in the face of intentional attacks found that domestic networks are more robust than foreign aviation networks. However, the isolation of a small fraction of selected nodes can cause serious problems in the functioning of the YRDAN.

Contribution of general aviation to ensuring transport accessibility to the Arctic regions: the challenges and areas of focus

The article defines the dominant position of general aviation to ensure passenger transportation, to render the sanitary aviation and the Ministry of Emergencies services, taking into consideration the natural and climatic conditions of the Russian Federation Eastern Arctic. It analyzes the current state of the transport system of this territory, which is characterized by the railway communication lack, available roads for only seasonal purpose – winter roads, and the key role of water transport in the organization of freight transport based on the complex long-term (more than one year) multimodal delivery schemes, extremely high cost of regional and local air transportation. The purpose of the study is to analyze the state of the general aviation system in the Eastern Arctic of Russia and identify the factors, constraining its development, and to formulate proposals for their elimination. The article examines the state of the operating airport network, route network and operators’ fleet of aircraft – in service which render services to these territories. The limited airport network does not provide aviation accessibility to each settlement of the Arctic zone and causes transport discrimination. The lack of scheduled flights in local communication leads to increased waiting time for transportation and reduces demand. The outdated general aviation fleet with low economic efficiency is outcompeted by the helicopter service. The cost of a helicopter flight hour in the Arctic is 500–650 thousand rubles. The article considers the legal restrictions, that are associated with overstated requirements for companies and aviation enterprises serving the general aviation activities and hinder the development of local air transportation in the Arctic zone. Based on the analysis of foreign experience, the areas of focus to improve the Russian legislation, regulating the general aviation activities, which will allow us to reduce the costs to maintain the airport network, thereby stimulating its expansion for the purpose of increasing the level of transport accessibility to remote settlements of the Arctic zone, are proposed. The proposed measures to improve the air transport system in the Arctic zone are aimed to achieve the formulated targets.

Timescales of delay propagation in airport networks

Flight delays persist and spread in airport networks due to high interconnectivity in the air transportation infrastructure. How quickly delay propagates between two airports is determined by factors such as the number of flights between airports, the duration of the flight, presence of disruptions, and schedule buffers. Accurate estimation of the time for delay propagation can improve system predictability and reliability. However, noisy airport delay data, along with a lack of visibility into airline scheduling and disruption management strategies, result in a challenging estimation problem for such propagation timescales. We present an algorithm to estimate statistically significant time lags between airport delays from noisy, aggregate operational data. The algorithm uses sliding correlation windows to extract the airport pairs with stable delay lags. We apply our method to identify different timescales of interactions for US airport delays in 2017. Our analysis yields two main results: (1) The most stable lags between airport delays involve the Northeast airports; (2) The stable lags between two airports are negatively correlated to the scheduled flight times between the same two airports. These results regarding delay propagation speeds have potential implications for delay prediction models and airline schedule design.

Spanish Airport Network Structure: Topological Characterization

Applied complex network theory has become an interesting research field in the last years. Many papers have appeared on this subject dealing with the topological description of real transport systems, from small networks like the Italian airport network to the worldwide air transportation network. A comprehensive topological description of those critical structures is relevant in order to understand their dynamics, capacities, and vulnerabilities. In this work, for the first time, we describe the Spanish airport network (SAN) as a complex network. Nodes are airports, and links are flight connections weighed by traffic flow. We study its topological features and traffic dynamics. Our analysis shows that SAN has complex dynamics similar to small-size air transportation networks of other developed economies. It shares properties of small-world and scale-free networks, and it is highly connected and efficient and has a disassortative pattern for high-degree nodes.

A sampling-guided unsupervised learning method to capture percolation in complex networks

The use of machine learning methods in classical and quantum systems has led to novel techniques to classify ordered and disordered phases, as well as uncover transition points in critical phenomena. Efforts to extend these methods to dynamical processes in complex networks is a field of active research. Network-percolation, a measure of resilience and robustness to structural failures, as well as a proxy for spreading processes, has numerous applications in social, technological, and infrastructural systems. A particular challenge is to identify the existence of a percolation cluster in a network in the face of noisy data. Here, we consider bond-percolation, and introduce a sampling approach that leverages the core-periphery structure of such networks at a microscopic scale, using onion decomposition, a refined version of the k-core. By selecting subsets of nodes in a particular layer of the onion spectrum that follow similar trajectories in the percolation process, percolating phases can be distinguished from non-percolating ones through an unsupervised clustering method. Accuracy in the initial step is essential for extracting samples with information-rich content, that are subsequently used to predict the critical transition point through the confusion scheme, a recently introduced learning method. The method circumvents the difficulty of missing data or noisy measurements, as it allows for sampling nodes from both the core and periphery, as well as intermediate layers. We validate the effectiveness of our sampling strategy on a spectrum of synthetic network topologies, as well as on two real-word case studies: the integration time of the US domestic airport network, and the identification of the epidemic cluster of COVID-19 outbreaks in three major US states. The method proposed here allows for identifying phase transitions in empirical time-varying networks.

Beyond zero: Activating triple zero airports

San Francisco International Airport (SFO) was the first airport to adopt a triple zero goal — zero energy, zero carbon and zero waste — in 2016. Five years in, this ambition is actively being implemented by subject matter expert ambassadors across campus. Airport staff and partners have co-created delivery plans, operationalised strategies and reported results on activating net zero and decarbonised buildings, doubling onsite renewables, procuring all-electric shuttle buses and light-duty vehicles, delivering sustainable aviation fuel to all aircraft, eliminating single-use plastics and so many more ‘zero’ gains. SFO staff have taken up the mantle by introducing new ideas and approaches, as well as seeking out a variety of thirdparty certifications to demonstrate our continued commitment to the health of our planet, passengers and employees. This is just the beginning. The path to zero is high-impact, highly collaborative work that requires the partnership of our design and construction teams, airlines, business partners, individuals and, most importantly, a global network of airports who have the aspiration AND passion for new and unexpected ways of incorporating sustainability into the way we work, how we develop our facilities and how we operate as our direct means to abate the climate crisis. Outlined herein is SFO’s path to zero, with the intention of defining a joint roadmap for airport partners to team up to ensure we each serve an active role in supporting the health of our communities, workforce and shared, but threatened, planet.

A budget allocation model for domestic airport network protection

In order to protect a domestic airport network from terrorist attacks, we investigate how to allocate a limited amount of budget into airports with different characteristics. We propose a minimax optimization model, where the Transportation Security Administration decides the budget allocation of each airport in the upper level, while the terrorist group chooses which flights to attack in the lower level. The objective of the terrorist group is to maximize the expected damage and the objective of the Transportation Security Administration is to minimize the corresponding maximum damage. We adopt a generalized cutting-plane algorithm to solve the proposed bilevel optimization problem. Moreover, we provide an illustrative example, and present some detailed sensitivity analysis and comparison results.

The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia

The ongoing COVID-19 pandemic has posed a global threat to human health. In order to prevent the spread of this virus, many countries have imposed travel restrictions. This difficult situation has dramatically affected the airline industry by reducing the passenger volume, number of flights, airline flow patterns, and even has changed the entire airport network, especially in Northeast Asia (because it includes the original disease seed). However, although most scholars have used conventional statistical analysis to describe the changes in passenger volume before and during the COVID-19 outbreak, very few of them have applied statistical assessment or time series analysis, and have not even examined how the impact may be different from place to place. Therefore, the purpose of this study was to identify the impact of COVID-19 on the airline industry and affected areas (including the origin-destination flow and the airport network). First, a Clustering Large Applications (CLARA) algorithm was used to group numerous origin-destination (O-D) flow patterns based on their characteristics and to determine if these characteristics have changed the severity of the impact of each cluster during the COVID-19 outbreak. Second, two statistical tests (the paired t-test and the Wilcoxon signed-rank test) were utilized to determine if the entire airport network and the top 30 hub airports changed during COVID-19. Four centrality measurement indices (degree, closeness, eigenvector, and betweenness centrality) of the airports were used to assess the entire network and ranking of individual hub airports. The study data, provided by The Official Aviation Guide (OAG) from December 2019 to April 2020, indicated that during the COVID-19 outbreak, there was a decrease in passenger volume (60%–98.4%) as well as the number of flights (1.5%–82.6%). However, there were no such significant changes regarding the popularity ranking of most airports during the outbreak. Before this occurred (December 2019), most hub airports were in China (April 2020), and this trend remain similar during the COVID-19 outbreak. However, the values of the centrality measurement decreased significantly for most hub airports due to travel restrictions issued by the government.

Airport pricing versus (grandfathered) slots: A generalization

Many passengers can choose among various destinations for their flight trips (for instance, a leisure trip from Asia to Europe or the US). This study tests the sensitivity of the outcomes of equilibrium congestion policies with respect to changes in airport network and demand structures. A major feature is to incorporate substitute destination choices for origin–destination passengers into the analysis of equilibrium airport congestion policies involving slot or pricing policies. The analysis shows that the presence of substitute destination choices is a necessary condition for equilibrium slot quantities to reach the first-best outcome that maximizes the welfare of all airport regions whereas equilibrium pricing levels will always be too high relative to the first-best prices independent of the presence or absence of substitute destination choices. Examples based on specific functional forms and numerical simulations are used to illustrate the main results.

Delay propagation network in air transport systems based on refined nonlinear Granger causality

To probe deeply into the underlying mechanism of flight delay propagation, we construct the delay propagation networks among airports via refined nonlinear Granger causality, which is more fitting on nonlinear airport delay data and obtain more precise causal estimation. Specifically, the causal analysis method is used to determine the delay propagation relationship between airports. The delay propagation relationship of these airport pairs constitutes an airport delay propagation network, in which the nodes and edges of the network, respectively, represent the specific airport and the delay propagation relationship between airports. Complex network theory is then applied to analyse the global structure of delay propagation networks, indicating that small airports are inclined to cause or exacerbate delays. In addition, the airlines’ delay propagation networks display that the delay propagation by large airlines spread wider while the delay propagation by small airlines spread faster. These findings can be employed to design strategies for delay propagation dampening.

A model for cascading failures with the probability of failure described as a logistic function

In most cascading failure models in networks, overloaded nodes are assumed to fail and are removed from the network. However, this is not always the case due to network mitigation measures. Considering the effects of these mitigating measures, we propose a new cascading failure model that describes the probability that an overloaded node fails as a logistic function. By performing numerical simulations of cascading failures on Barabási and Albert (BA) scale-free networks and a real airport network, we compare the results of our model and the established model describing the probability of failure as a linear function. The simulation results show that the difference in the robustness of the two models depends on the initial load distribution and the redistribution of load. We further investigate the conditions of our new model under which the network exhibits the strongest robustness in terms of the load distribution and the network topology. We find the optimal value for the parameter of the load distribution and demonstrate that the robustness of the network improves as the average degree increases. The results regarding the optimal load distribution are verified by theoretical analysis. This work can be used to develop effective mitigation measures and design networks that are robust to cascading failure phenomena.

РАЗВИТИЕ АЭРОПОРТОВОЙ СЕТИ ЧУКОТСКОГО АВТОНОМНОГО ОКРУГА: ПОВЫШЕНИЯ ТРАНСПОРТНОЙ ДОСТУПНОСТИ РЕГИОНА

An analysis of the state of the existing transport system, airport network and landing sites of the Chukotka Autonomous Okrug was carried out in terms of meeting the needs of the region in year-round transport communication. The zones of the least transport accessibility and potential growth points of the region are determined. A scheme is proposed for the development of the re-gion's airfield network in order to replace the operation of helicopters with aircraft on local avia-tion routes, the cost of a flight hour of which is significantly lower.