Critical Airports Research Articles

Resilience analysis of cities' air accessibility under disruptions

Air transportation stands as an indispensable pillar of a city's economy. An effective and reliable air transport service plays an important role for the prosperity of a city. Moreover, in many cases, a city has multiple airports within its catchment area and the collaborative relationships under disruptions between these airport services have often been overlooked in prior studies. To bridge this gap, this paper firstly introduces the concept of “instant air accessibility” for a city and develops a resilience metric aimed at quantifying the impact of airport disruptions on a city's air accessibility, taking into account the perspective of a multi-airport system. We apply the metrics to a network with 48 cities at or above the second-tier level in China. Results of the air accessibility analysis show that some relatively small cities have high accessibility that are comparable to megacities in China, but some provincial capital cities have low accessibilities, although they maintain superior political and economic status among the cities. Most of the cities' accessibility are vulnerable to targeted disruptions. Additionally, we identify critical airports that wield significant influence over the overall accessibility performance of the entire network. The findings from this study offer valuable insights for the management of air transport resources and the enhancement of the resilience of cities' air accessibility.

Does the Airport Servicescape Affect the Relationship between Airport Outcome Quality and Passenger Satisfaction?

Airports promote passenger satisfaction with superior service. Airport outcome quality (AOQ) is a part of Airport Service Quality (ASQ), which impacts daily service performance. In this study, the airport servicescape influenced the link between airport outcome quality and passenger satisfaction. The model was developed using the Hierarchical Service Quality Model (HSQM) and Stimulus-Organism-Response (SOR) theory, incorporating two direct and moderated paths. The study utilized a convenience sampling technique and conducted a cross-sectional survey of international travellers at critical airports in Tanzania. 389 respondents were evaluated using the PLS-SEM technique. Significant enhancements have been made in the direct and moderated relationships between airport outcome quality and passenger satisfaction. The airport environment was more effectively managed than the direct route. The environment of an airport improves the satisfaction of international airport passengers. Moreover, these findings have implications for theory, methodology, management, and policy. The study enhances the partial least squares research methodologies, specifically for higher-order structures. The study shows airport architects and officials that the airport servicescape is crucial for improving customer satisfaction at airport terminals.

Climate Change Impacts on Frost and Thaw Considerations: Case Study of Airport Pavement Design in Canada

Rising temperatures due to climate change can significantly impact the freeze–thaw condition of airport pavements in cold regions. This case study investigates the implications of warming temperatures on the freeze–thaw penetration and frost heave of pavements in critical airports across Canada. To this end, different methods were used in the quantification process through climate change simulations considering emission scenario RCP8.5 in 20 and 40 year time horizons. The results show that climate change would have different design implications for airport pavements, depending on their location. The predictions suggest a shallower frost penetration depth, and possibly less frost heave, for the airports not underlain by permafrost, while airports over permafrost areas might experience an increase in thickness of the active layer, ranging from 41 to 57 percent, by 2061. Among the different methods used in this study, it was observed that some methods performed better in predicting the frost depth of fine soils, while others worked better in the frost depth prediction of coarse soils. The results indicate the need for more mechanistic models to provide a more realistic prediction of freeze–thaw penetration, as compared to existing empirical models.

Cross-sectoral and multiscalar exposure assessment to advance climate adaptation policy: The case of future coastal flooding of California’s airports

Climate adaptation is inevitable to managing the climate risk of infrastructure systems and has become an emerging topic in the past decade. Despite the growing need for collaborative and multi-agency efforts in climate change adaptation, however, current airport and transportation governance structures reinforce siloed approaches to manage climate risk. Here we combine a novel coastal flooding exposure assessment of California’s airports and a policy review to address the importance of collaborative climate adaptation by viewing airport infrastructures as interconnected systems across spatial scales and sectors. Our exposure assessment innovates by investigating the airport perimeter, its interconnected infrastructure (road access, ground-based navigation and communications systems), and multimodal interregional transportation corridors. At the local scale, we find that substantial airport assets are exposed as early as 2020–2040, suggesting the urgency for near-term adaptation actions. Regarding the interconnected infrastructures, 23 unaccounted airports are identified at risk of disruption when compared with existing studies, including global hubs such as the Los Angeles International Airport. At the regional scale, we investigate how exposed airports transfer risk within the interregional multimodal transportation corridors, and we identify the critical airports within these corridors to be prioritized for adaptation. Finally, based on a policy review of more than 100 state legislature and planning documents, implications of our results are discussed at the national and state levels. This study presents a new generation of infrastructure exposure assessment to climate-induced hazards, by addressing cross-sectoral and multiscalar dependencies that are currently overlooked by adaptation policies. We also argue that new modes of collaborative efforts are needed to achieve effective climate adaptation for interconnected infrastructures.

Critical airports of the world air sector network based on the centrality and entropy theory

In this paper, an air sector network (ASN) is built based on the obtained data describing the airspace of world. In the constructed network, nodes and edges represents the airports and airlines respectively. Based on the complex network and the entropy, we find that the airport rank obtained through the centrality and entropy theory is better than that derived through the traditional means. The improved entropy weight algorithm is used to reflect the location information of the nodes in the network, synthesize the centrality of the previous complex network, construct the importance evaluation matrix and finally calculate the ranking results of the importance of the nodes. Finally, through calculation, we found that the ranking result after comprehensive consideration is better than that being determined only through centrality, which illustrates the effectiveness of the method.

Investigating and Identifying Critical Airports for Controlling Infectious Diseases Outbreaks

The enormous economic impact of air transportation networks on a local, national, and international level has created an interest for further investments and increased the complexity of the global air transport network. The increased number of worldwide airport passengers and of the aircraft movements and also of the international airport cargo shipments is evident. Therefore, the functionality of this complex air transport network is very important and requires to be investigated and evaluated for identifying the airports that appear to be critical to this network. However, besides the economic benefits that air transport network is offering in local, national and international level through their services (shipments of goods and transport of passengers) play an ever-increasing role in safeguarding global health security. This paper investigated the global air transport network for identifying the airports that may constitute a public health event of international concern from infectious diseases (e.g. COVID-19). In detail, critical airports, in terms of centrality measures (e.g. Closeness, Degree, Betweenness, and Page Rank Centrality) were identified and addressed by pointing them to global authorities for suggesting and implementing routine prevention and control measures for possible future disease outbreaks.

Integrators' global networks: A topology analysis with insights into the effect of the COVID-19 pandemic

In this paper we propose, to the best of our knowledge, the first analysis of the global networks of integrators FedEx, UPS, and DHL using network science. While noticing that all three networks rely on a “hub-and-spoke” structure, the network configuration of DHL leans towards a multi-“hub-and-spoke” structure that reflects the different business strategy of the integrator. We also analyzed the robustness of the networks, identified the most critical airports per integrator, and assessed that the network of DHL is the most robust according to our definition of robustness. Finally, given the unprecedented historical time that the airline industry is facing at the moment of writing, we provided some insights into how the COVID-19 pandemic affected the global capacity of integrators and other cargo airlines. Our results suggest that full-cargo airlines and, much more dramatically, combination airlines were impacted by the pandemic. On the other hand, apart from fluctuations in offered capacity due to travel bans that were quickly recovered thanks to the resilience of their networks, integrators seem to have escaped the early months of the pandemic unscathed.

Identification of critical airports for controlling global infectious disease outbreaks: Stress-tests focusing in Europe

As the global population increases and transportation connectivity improves in quality and prices, the demand for mobility increases, especially in long-haul services. According to the 2017 report of the European Commission in Mobility and Transport, the performance of all modes for passenger transport (roadways and airways) are reaching record highs. Although the benefits of the increased demand for mobility are substantial and welcome, an effort should be paid such as to ameliorate possible threatening side-effects that may also arise. As World Health Organization (WHO) denotes and as has been evident from the global COVID-19 epidemic outbreak, infectious diseases can be spread directly or indirectly from one person to another under common exposure circumstances such as air transportation (especially long-haul airline connections) that may act as the medium for transmitting and spreading infectious diseases. In this paper, analytical and realistic models have been integrated, for providing evidence on the spread dynamics of infectious diseases that may face Europe through the airlines system. In particular, a detailed epidemiological model has been integrated with the airlines’ and land transport network, able to simulate the epidemic spread of infectious diseases originated from distant locations. Additionally, a wide set of experiments and simulations have been conducted, providing results from detailed stress-tests covering both mild as well as aggressive cases of epidemic spreading scenarios. The results provide convincing evidence on the effectiveness that the European airports' system offer in controlling the emergence of epidemics, but also on the time and extent that controlling measures should be taken in order to break the chain of infections in realistic cases.

Assessing Interdependencies and Congestion Delays in the Aviation Network

Concerning air traffic delays, air transport networks appear to have variable performance and stochastic nature. A delay incident in one airport may affect the operational efficiency of others and generate various side effects to the whole aviation network. Flight delays are a widespread phenomenon nowadays, costing billions to the air transportation economy and degrading passenger’s quality of service. Dependency graphs have been proposed in the past to understand the delay propagation phenomenon and analyze such cascading events by using dependency chains. In this work, we propose a risk-based method to analyze interdependencies and congestions in the aviation network. The methodology and the developed tool can assess delay incidents in airports and produce weighted risk dependency graphs, presenting how a delay that occurred in one airport may affect other interconnected airports. Based on data collected from the US Bureau of Transportation Statistics, we analyze how flight delay risk propagates inside the aviation network. In addition, using historic flight performance data we provide predictions for flight chains, which are prone to delays. We implement a tool that can detect the most critical airports and congested connections based on their delay contribution in dependency chains. It also proposes the n-order dependency chains, which should be avoided by airline flight planners, to reduce delay impacts in the aviation network.

An iterative graph expansion approach for the scheduling and routing of airplanes

A tourism company that offers fly-in safaris is faced with the challenge to route and schedule its fleet of airplanes in an optimal way. Over the course of a given time horizon several groups of tourists have to be picked up at airports and flown to their destinations within a certain time-window. Furthermore, the number of available seats, the consumption of fuel, the maximal takeoff weight, and restrictions on the detour of the individual groups have to be taken into account. The task of optimally scheduling the airplanes and tour groups belongs to the class of vehicle routing problems with pickup and delivery and time-windows. A flow-over-flow formulation on the time expanded graph of the airports was used in the literature in order to model this problem as a mixed integer linear program. Most of the benchmark problems, however, could not be solved within a time limit of three hours, which was overcome by what they call the time-free approach (TFA). In the TFA they formulate the problem for a simplified (time-free) graph and use an incumbent callback to check for feasibility in the original graph. While this approach led to very good results for instances, where few time-free solutions were infeasible for the original problem, some instances remained unsolved. In order to overcome this problem, we derive two new exact formulations that include time as variables. Although these formulations by themselves are not always better than the approach from the literature, they allow for an effective construction of graphs which can be interpreted as intermediate graphs between the graph of airports and the expanded graph with vertices for each visit. Using similar relaxation techniques to the TFA and constructing these graphs based on solutions of the relaxations guarantees that only critical airports are expanded. A computational study was performed in order to compare the new formulations to the methods from the literature. Within a time limit of 3 hours, the new approach was able to find proven optimal solutions for all previously unsolved benchmark instances. Furthermore, the average computation time of all benchmark instances was reduced by 90 percent.

Measuring the resilience of an airport network

Resilience is the ability of a system to withstand and stay operational in the face of an unexpected disturbance or unpredicted changes. Recent studies on air transport system resilience focus on topology characteristics after the disturbance and measure the robustness of the network with respect to connectivity. The dynamic processes occurring at the node and link levels are often ignored. Here we analyze airport network resilience by considering both structural and dynamical aspects. We develop a simulation model to study the operational performance of the air transport system when airports operate at degraded capacity rather than completely shutting down. Our analyses show that the system deteriorates soon after disruptive events occur but returns to an acceptable level after a period of time. Static resilience of the airport network is captured by a phase transition in which a small change to airport capacity will result in a sharp change in system punctuality. After the phase transition point, decreasing airport capacity has little impact on system performance. Critical airports which have significant influence on the performance of whole system are identified, and we find that some of these cannot be detected based on the analysis of network structural indicators alone. Our work shows that air transport system’s resilience can be well understood by combining network science and operational dynamics.

Efficiency and robustness of weighted air transport networks

This paper proposes a novel efficiency metric for weighted air transport networks (ATNs), where the weight represents the connection strength between two airports concerning the number of routes, the frequency of flights, or the number of passenger seats. This metric is further used to assess and compare the weighted efficiency and robustness of eight domestic ATNs, with the data of routes being operated by all the airlines. Furthermore, the critical airports in these ATNs are identified. It is found that the link weight has a significant impact on the assessment of efficiency and robustness and ranking of critical airports.

Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports

This paper analyzes the vulnerability of the European air transport network to major airport closures from the perspective of the delays imposed to disrupted airline passengers. Using an MIDT dataset on passenger itineraries flown during February 2013, full-day individual closures of the 25 busiest European airports are simulated and disrupted passengers then relocated to minimum-delay itineraries. Aggregate delays are used to rank the criticality of each airport to the network, with the possibility of disaggregating the impact across geographical markets. The results provide useful reference values for the development of policies aimed at improving the resilience of air transport networks.

Empirical analysis of airport network and critical airports

Air transport network, or airport network, is a complex network involving numerous airports. Effective management of the air transport system requires an in-depth understanding of the roles of airports in the network. Whereas knowledge on air transport network properties has been improved greatly, methods to find critical airports in the network are still lacking. In this paper, we present methods to investigate network properties and to identify critical airports in the network. A novel network model is proposed with airports as nodes and the correlations between traffic flow of airports as edges. Spectral clustering algorithm is developed to classify airports. Spatial distribution characteristics and intraclass correlation of different categories of airports are carefully analyzed. The analyses based on the fluctuation trend of distance-correlation and power spectrum of time series are performed to examine the self-organized criticality of the network. The results indicate that there is one category of airports which dominates the self-organized critical state of the network. Six airports in this category are found to be the most important ones in the Chinese air transport network. The flights delay occurred in these six airports can propagate to the other airports, having huge impact on the operation characteristics of the entire network. The methods proposed here taking traffic dynamics into account are capable of identifying critical airports in the whole air transport network.

Robustness of the air transport network

This paper presents a methodology for the detection of critical airports (those whose isolation would cause the largest losses in network connectivity) in the global air transport network (ATN), based on simulating an attack on selected ATN airports using different adaptive selection criteria. The performances of several node selection criteria are compared, together with a new criterion based on Bonacich power centrality. The results show that most critical airports can be detected with an adaptive strategy based on betweenness centrality. The detection of such airports may help the development of contingency plans to develop an appropriate response to any airport closure.