Airport Size Research Articles

Drivers of short-term essential air service operations: Load factor, pricing, and subsidy policies in China's domestic air market

This study analyzes the impact of the temporary flight subsidy policy on the operational performance of China's domestic air transport market. Using panel data from 42 days between May 7 and June 17, 2022, and applying a dynamic panel model with time-invariant variables, the study explores the factors influencing average load factor and average ticket price under short-term basic air service conditions. The results reveal that, during the pandemic, load factors exhibited weak dependence on historical data, with airlines adjusting to real-time demand fluctuations. Additionally, the flight subsidy policy failed to meet its intended goals, leading to a reduction in overall load factors, particularly for China Southern, China Eastern, and Hainan Airlines, while Air China demonstrated more flexibility in adapting to the policy. The study also identifies the significant influence of route distance and airport size on both load factors and ticket prices, with longer routes and major hub airports commanding higher prices. These findings suggest that future subsidy policies should be more flexible and tailored to specific market conditions to effectively support the recovery and long-term sustainability of the aviation industry.

Sustainable Governance for Promotional Market Competitiveness in Chinese Airports after COVID-19

Like many countries, China’s airports faced severe operational challenges after the COVID-19 outbreak. However, this predicament has been better mitigated by the Chinese government’s strong support for the aviation industry. However, whether this supportive policy can ensure the sustainable development of the industry needs to be further evaluated. In order to better understand the role of government promotional policies, this paper attempts to explore the driving mechanisms behind the sustainable development performance of Chinese airports. Using the bootstrap Data Envelopment Analysis (DEA) methodology, this study evaluates the operational efficiency of 43 major airports over the period from 2015 to 2022. The efficiency scores are compared with the regional distributions of supply and demand factors, as well as the different sizes based on their annual passenger throughput. The overall operational efficiency showed a fluctuating trend of an initial increase followed by a decline due to COVID-19, with significant regional disparities in airport efficiency. Regional differences are evident, with the eastern region having the highest efficiency value of 0.719, while the central region has the lowest efficiency of 0.594. The findings show that airports in the eastern region perform the best due to the proactive market support with rapidly increasing logistics demand, while airports in the west and northeast regions supported by the strong promotional policies follow closely in terms of operational efficiency. In contrast, airports in the central region are much less efficient due to the lack of market or policy support. To analyze more precisely this conflict on the governance, further analyses show that airport size also has a significant impact on the operational efficiency. Larger airports are typically more efficient, while smaller airports are less efficient, implying the market demand is slightly better to promote the airport operational efficiency than the promotional policies. These findings help us to gain a deeper understanding of the mechanism in the sustainable governance behind the operational efficiency of airports and suggest that more market-oriented appropriate policy direction is required to enhance the competitiveness of the aviation industry. These findings offer valuable insights for policymakers and stakeholders of the developing countries as well, underlining the necessity not for supply-driven policies, but for the demand–pull efforts to optimize resource allocation and adapt to evolving market dynamics.

Analysis of the interactive effects of scavenge parameters on spark ignition two-stroke diesel engines

The scavenging efficiency of a two-stroke diesel engine affects the formation of the mixture in the engine cylinder, which in turn affects the engine’s performance. A one-dimensional simulation model of a two-stroke diesel engine is established using GT-POWER, and the model with original experimental data is calibrated as boundary conditions. Firstly, the influence of crankcase compression ratio on engine performance of the two-stroke engine was investigated to obtain an appropriate crankcase compression ratio; Then, based on the optimization index of scavenging efficiency, the optimal combination of engine air port structure and timing is obtained through orthogonal optimization calculation method. To achieve higher scavenging efficiency, when the phase of the air port remains unchanged, a scavenging port width ratio of 1.0 and an exhaust port width ratio of 0.8 can achieve a scavenging efficiency of 97.2%; When the air port size remains unchanged, the engine scavenging efficiency reaches 96.44% when the scavenging port timing is 123° CA and the exhaust port timing is 89° CA.

Modeling airport networks for theoretical policy analyses

More than in almost any other transport industry, air transport is globally connected. This paper provides an overview over theoretical studies considering network structures involving two or more airports. Beyond conveying study results, the goal is to offer guidance on how to \emph{theoretically} cover and analyze specific issues in the most effective way. Effectiveness is measured in terms of the complexity needed to address the specific issues. The selected issues discussed in this paper include ownership structures and privatization, congestion, beggar-thy-neighbor, and competition. We demonstrate that the analysis of these issues requires networks of different sizes and structures. We measure the airport size by the number of airports involved. We determine the network structure by (i) the number of regions and by distinguishing between regions which are just passively present or actively involved in policy making, and (ii) the involvement of local and non-local passengers.

Seasonality of flights in China: Spatial heterogeneity and its determinants

Seasonality is an essential issue for service industries but lacks the attention of most transport scholars. To close this gap, this study explored the spatial heterogeneity and determinants of flight seasonality from a supply-side perspective, using the monthly flights of 222 airports in China during 2018 as a sample. The following conclusions were drawn. First, domestic flights in China face seasonality due to the country's vast territory and diverse natural environment. Second, from an airport perspective, seasonality is high in small airports serving remote places and in cities that are tourism destinations. Third, from a route perspective, feeder routes in the air transport network of China face higher seasonality when compared to trunk routes. Finally, airport size and a mix of natural landscape factors shape domestic flight seasonality at the national level. At the local level, most factors (e.g., airport size and temperature) are more evident in the northwest region.

Comparison of selected airports in terms of sustainability

The paper explores sustainable development in the context of European airports, analyzing economic, ecological, and social dimensions. Definitions of sustainable development evolve, with the emphasis on meeting current needs without compromising future generations. Four international European airports – Schiphol, Munich, Prague, and Bratislava – are compared and investigated to find out whether airport size affects sustainability. The paper reveals an interconnected relationship between these dimensions: social factors influence economic outcomes, impacting environmental resources for conservation. Ecologically, carbon emissions and noise pollution are examined, with Schiphol emerging as a leader in carbon neutrality due to its scale and resources. The paper highlights the significance of employees’ well-being for social sustainability.

Airline market exit after a shock event: Insights from the COVID-19 pandemic

During the COVID-19 pandemic, passenger demand for air transportation declined drastically. In the Unites States (U.S.), the Coronavirus Aid, Relief, and Economic Security (CARES) Act provided financial assistance. In return, commercial passenger airlines were given minimum service obligations, which allowed airlines to remove markets (flights between origin and destination airport pairs) from their networks as long as they continued operating in all cities that they serviced pre-pandemic. A binary logit methodology is used to model airline-market level decisions to continue operating in a market or to exit it. Two time periods are modeled: during normal operating conditions (before the pandemic) and after a major shock event (after the beginning of the pandemic). Results show that after the pandemic, 8.4 times more airline markets are exited as compared to before. Interestingly, the probability of exit is found to vary widely across markets, airports, and airlines. Some market characteristics have a high probability of exit both before and after the pandemic, including low passenger revenue per available seat mile, low flight frequencies, and flights to/from multi-airport cities. In contrast, other market characteristics impact airlines’ market exit decisions in only one time period rather than both. For example, during normal operating conditions, airport size does not impact market exit. However, after the pandemic, the probability of exit is 1.8 to 2.2 times higher for the larger hub airports as compared to the smallest airports (non-hubs), a result that is explained within the context of the CARES Act minimum service obligations.

Exploratory and confirmatory analysis to identify airports and airlines characteristics as factors influencing domestic passenger traffic in Nigeria

Passenger traffic at airports is characterised by fluctuations resulting from the influence of several factors. The influence of each factor is different, leading to unpredictable passenger traffic patterns that make planning difficult. Previous studies used geographic, demographic, and economic variables as exploratory factors to examine air travel demand. The study explores several variables to confirm airports and airlines’ characteristics as demand factors for domestic air travel in Nigeria. Data for the study were collected by administering a questionnaire to respondents at major domestic airports in Nigeria. The variables were presented in the 5-point Likert Scale for respondents to rank in order of significance. Exploratory and confirmatory factor analyses (EFA and CFA) were employed to identify the significant factors affecting passenger traffic at domestic airports in Nigeria. EFA reduced fifteen variables to four orthogonal factors influencing passenger traffic at domestic airports. CFA validates airport and airline services, demographics, economic factors, and airport size and facilities as significant factors affecting passenger traffic at domestic airports in Nigeria. The model fit test shows CMIN/DF = 2.263; CFI = 0.940; GFI = 0.929; NFI = 0.901; and RMSEA = 0.078. The result identifies airport and airline characteristics as factors influencing passenger traffic at the domestic airport in any country. It implies that airport and airline characteristics significantly influence domestic air traffic and needs to be included in modelling. Identifying airport and airline characteristics as air travel determinants make this study unique for policy decisions to forecast domestic passenger traffic in a country.

Twenty years of airport efficiency and productivity studies: A machine learning bibliometric analysis

Airport efficiency and productivity analysis has attracted increasing attention over the last two decades. During this period, the number of such studies has grown significantly, with more than 100 articles related to the topic having been published in peer-reviewed journals in broad areas including transportation, economics, management, operations research and mathematics. Our study complements the previous literature surveys by adopting bibliometric analysis to improve the understanding of airport efficiency and productivity studies. Our analysis identifies publication patterns (e.g., the most relevant authors, institutions and countries; collaborative networks; key journals; and the most cited articles) and popular research topics (e.g., keyword analysis). The annual growth rate of publications is found to be 10.96%, with an increasing trend during 1999–2019. In terms of geographical distribution, almost half of the studies evaluate the performance of the airport sector in Europe, while 21.88% focus on Asian airports. Airport ownership form and size, hub status and economic regulations are popular variables used to explain variation in airport efficiency.

An Improved Framework for Airport Detection Under the Complex and Wide Background

As an important transportation facility, airports substantially affect the economic lives of people. However, the full extraction of airports located in a vast area is concerning. The size of an airport in a previous wide area detection framework is relatively large and has a strong saliency in remote sensing images, whereas the contradiction between a complex geographical background and a small airport size has yet to be resolved. In this study, we propose a set of automatic detection frameworks to realize efficient detection for various airports in nine Indian states/union territories under the condition that only runway samples are labeled. A preliminary extraction of runway features is performed with a high F1 and recall rate, and teacher nodes judge and guide the results. Next, the output is connected to classification and segmentation for outlier elimination and pixel extraction to locate the runways. For the study area, the proposed framework airport retention rate (RR) is 92.7%, with the false alarm reduction rate(FARR) reduced by a maximum of 95.3%. A total of 192 airports are discovered, and the effective airport growth rate(GR) is 47.4%. Compared to previous work, RR, GR, FARR, and run efficiency increased by 2.2%, 16.0%, 4.5%, and 432.5%, respectively, with more small- and medium-sized airports detected. Furthermore, the framework is tested in Japan, and 155 airports are detected. Thus, the proposed framework effectively improves detection capability for small- and medium-sized airports in large-scale areas and updates the airport database.

Detecting delay propagation in regional air transport systems using convergent cross mapping and complex network theory

It has been widely recognized that delay propagation is a widespread phenomenon in air transport systems. Regional air transport systems are vulnerable to severe systemic delay propagation because of connected resources and shared airspace. This study presents a two-stage analytical framework to detect delay propagation in regional air transport systems, which is critical to support industrial applications in air transport operations. In the first stage, we propose a delay causality without the strong and independent causality (SIC) assumption to represent the delay propagation effect. A convergent cross mapping method is employed to obtain the delay causality. In the second stage, we build a delay causality graph based on the none SIC assumption (DCG-non-SIC). A complex network analysis of DCG-non-SIC is presented to enhance the understanding of delay propagation patterns. This study carries out a real-world case study and provides some interesting results. Robustness checks are conducted, which verifies that the results derived in the analytical framework are solid. The results show that there is extensive delay causality between airport pairs, and a transitive trait of delay causality is observed in regional air transport systems. In addition, the importance of airports in delay propagation is not exactly in line with the hub class or the airport size.

Evaluation of Airport Size and Delay Causal Factor Effects on Delay Propagation Dissipation

The International Civil Aviation Organization identifies departure and arrival punctuality as on-time key performance indicators. However, these metrics assume a flight’s delay is a result of either the origin or destination airport, providing limited information on where delay should be mitigated in the U.S. National Airspace System (NAS). This study evaluates the relationship between delay propagation magnitude, delay causal factor, airport size, and charged facility (airport or Air Route Traffic Control Center), to examine if certain delays take longer to dissipate. First, using flights from July 2018, results show that most delay propagation chains originate at large-hub airports. However, these delays were the quickest to recover. Second, this study presents a regression model, predicting total propagated delay using fixed effects based on the weather region where the original delay occurred. Each additional flight affected by downstream delay adds 18.7 min on average to total arrival delay in a propagation chain. Additionally, if weather was the original causal factor, total propagated delay increased by 11.6 min compared with non-weather delays. Lastly, this study compares delay propagation in July 2018 and July 2020. Results show uneven impacts of the coronavirus disease 2019 (COVID-19) across the large-hub airports. Some of the investigated airports did not witness large improvements in average delay per delayed flight, warranting further research in the future. While delay and delay propagation have not been completely eradicated in the NAS during the COVID-19 pandemic, findings suggest that both have significantly declined on average.

An Approach to Measure the Performance and the Efficiency of Future Airport Infrastructure

The aim of this paper is to design an approach to evaluate the expected efficiency and performance of future airport infrastructure. First, an airport sampling method to select similar airports is developed based on socioeconomic and operational airport variables that are summarized in a proxy variable; second, the ARIMA-GARCH-Bootstrap method is applied to forecast the selected outputs (PAX and ATMS) whilst the selected inputs (Cities, Gates, Runaways, Airport Size, Pax carriers, and Num. of employees) remain constant; and third, the VRS-OO and the CRS-OO DEA models are implemented to evaluate the efficiency and performance of the airports in the current and future years. The proposed approach is used to evaluate the future airport infrastructure of the new Mexico City Airport against 19 representative worldwide airport hubs. The proposed approach is applied to analyze the Mexico City Airport multi-airport system infrastructure as a case study. The results show that this multi-airport system requires more airside infrastructure that must be added by the new Mexico City Airport, airlines should operate aircrafts with more capacity to serve more PAX per ATM, and airlines must open new connections at the new Mexico City Airport to increase the expected efficiency and performance of this multi-airport system.

Evaluating financial performance of commercial service airports in the United States

Studies on airport performance have focused primarily on productivity and operational efficiency. There are very few studies on airports’ financial viability and strength, especially those in the United States. Most of the U.S. commercial service airports are government-owned entities and operated for the service of the community rather than for profit, but expected to be financially self-sufficient and raise capital funds mainly through the bond markets. However, past studies of the financial performance of U.S. airports have relied on traditional profitability measures and financial efficiency measures that are more appropriate for airports owned and operated by for-profit entities. Based on the literature for non-profit organizations and the practices of credit rating agencies and government oversight bodies, this paper adopts six pertinent financial performance metrics to measure and compare the operational financial performance, leverage, and liquidity of 60 large and medium commercial service airports in the United States during the 2010–2017 period. The paper further examines factors that may affect airport financial performance. The results reveal that large hub airports have better liquidity while medium airports have better leverage during the study period. These results suggest that the effect of airport size on financial performance is inconclusive. Regression analysis shows that airports with high productive efficiency and those without a dominant carrier tend to have more surplus revenues for meeting their operational financial needs and capital spending and have better liquidity. The paper highlights the importance of using appropriate metrics to evaluate the financial performance of public sector entities and provides relevant information to bond investors. The devastating impacts of the COVID-19 pandemic on airports accentuate the significance of leverage and liquidity. Therefore, the financial metrics discussed in this paper would help support public policy debates and allocate public funds to the airports.

Ready for digital transformation? The effect of organisational readiness, innovation, airport size and ownership on digital change at airports

This study investigates the effect of organisational readiness, innovation and airport size and ownership on digital change at airports. Data is collected from a survey of managers at 94 airports worldwide and analysed using partial least squares structural equation modelling. Organisational readiness is found to have a direct effect on digital change. Organisational readiness also has a direct effect on innovation, which subsequently affects digital change. Airport size has a direct effect on digital change while the effect of ownership is not significant. The findings show that successful development of organisational readiness can be used to speed up the rate of innovation needed for digital change at airports.

Factors affecting environmental practice adoption at small European airports: An investigation

The majority of the world’s 3759 commercial airports handle under 5 million passengers a year and these small airports rarely employ practices to address their environmental externalities. The aim of this research is to investigate the range of environmental practices (EPs) that are employed at small European airports and identify the factors which affect their adoption. The findings of an online survey of 413 small airports in the European Common Aviation Area reveal that the EPs most commonly adopted concern waste management and noise reduction. Privately owned airports were generally more engaged with EPs than publicly owned ones. Consumer pressure, regulatory intervention, and airport size positively affected the adoption of environmental practices whereas complexity, perceived relative advantage and human resource constraints acted as barriers to adoption. The paper concludes with recommendations for policy and practice to support EP engagement and reduce the environmental impact of small airport operations worldwide.

Spatial characteristics of aircraft CO2 emissions at different airports: Some evidence from China

This paper analyzes aircraft CO2 emissions (in both quantity and intensity per passenger) during landing and take-off cycles at nine different airports in Jiangsu province (China) over a ten-year time span (2007–2016). Our database is unique and very detailed in that we combine flight schedules, with aircraft type (engines) used, and landing-and-take-off cycles. We are particularly interested in how the spatial characteristics impact emission levels. To this end we estimate a CO2 emission model taking the airport characteristics into account, and apply a spatial classification and autocorrelation model to distinguish between different types of airports and systems. Our analysis shows that: (1) there are strong spatial distribution differences between airports due to the patterns of economic development, airport size and aircraft used; (2) most airports have a high reduction potential of CO2 emission, without a loss of economic performance; (3) significant spatial aggregation effects exist and are persistent during most observational years, which indicates a strong Matthew effect of CO2 emission within Jiangsu province; and (4) airport size, linkage to the local economy, and airport location are closely related to aircraft CO2 emissions. We also provide a number of recommendations to improve airport CO2 emissions and add to sustainable development.

Examining airline ground operation incidents by airport size

Evaluating ground operations incidents is a critical step for airlines from both a management and safety perspective. Analysis of incidents is useful in both safety management and in planning for training and recurrent training of airline employees. De-identified data was provided by a major U.S. airline for all reported ground operations incidents from 2010-2017. This ex post facto research examined the rate of ground operations incidents by airport size for this airline, using both the National Plan of Integrated Airport Systems (NPIAS) classification and the airline's classification of airport size, based on its operations at each airport. NPIAS recognizes all commercial, reliever, and public airports in the national airport system, and the airports in this study were all classified as small/non-hub, or large/medium hub. The airline's size classification was based on its operations at a given airport and assigned airports as small, medium, large, or mega stations. The analysis of ground operations showed that the rate of incidents per operations did not differ with airport size for either the NPIAS size categories or the airline's size classification.

Abiotic determinants of airport performance: Insights from a global survey

This paper explores airport business in association with its operational, economic, geographical and regulatory environment. The two-stages methodology engages a post-positivist approach and data triangulation. In the first stage, airport performance (AP) is measured with the tailor-made Airport Business Excellence Model in 137 airports spread over 90 countries. Subsequently, the AP scores are examined vis-à-vis an extensive set of abiotic/externally determined factors, such as ownership, regulation, size, seasonality, hub status, location, economic development, traffic mix and performance management system. The data is first processed with the Kruskall-Wallis non-parametric test to detect statistical differences in AP levels among airport subgroups and the relevant contribution of the abiotic determinants is then investigated by multiple regression analysis. The results indicate that the effectiveness of the regulatory mix is in close connection with second-order charging schemes. Furthermore, low seasonality and an extra-large airport size are positively related to AP, whereas non-hub status, the absence of formal performance management systems and the blending of public and private sectors in mixed ownership/management schemes act in the opposite direction.

Estimating potential air quality impact of airports on children attending the surrounding schools

To estimate the pollution impact of seven sample airports in California on the surrounding schools, routine high-quality observations of 1-min average wind direction and speed at the airports were used to calculate the time each school within a 10-km radius was downwind of each airport. We demonstrate 0.87 correlation between pollution levels and downwind time at Los Angeles International Airport (LAX). Depending on the airport size, between 10 and 15 years of data was collected and downwind times calculated for the school hours throughout the school-months of August to June. By comparing this data with the 2015–2016 student enrollment size and demographics of the neighboring schools, we have demonstrated that an estimated 65,409 (8.7%) students spend 1–6 h per day at school throughout the academic year with exposure to airport-related air pollution. However, a higher proportion of economically disadvantaged students (10.6%) experience aforementioned levels of exposure. The level of exposure does not appear to be affected by the proposed 1-h delay in the school starting time that is intended to reduce student fatigue.