Air Passenger Flows Research Articles

Decoding air passenger flows: Identifying the role of network autocorrelation in air travel

With the rapid expansion of the global aviation industry, especially in the Eurasian continent, understanding the factors driving interregional air passenger flow is of increasing importance. While most existing studies emphasize node-level and edge-level influencing factors such as economic scale, population size, and geographic distance, they often neglect the pivotal variable of network autocorrelation. This research is the first to introduce network autocorrelation within the Eurasian context and systematically analyze it using the Eigenvector Spatial Filtering Negative Binomial Gravity Model. Our findings highlight: (1) A significant network autocorrelation in the Eurasian continental aviation network. The eigenvector spatial filtering negative binomial regression model effectively captures this autocorrelation, considerably reducing model estimation bias. Specifically, the leading 3.28% of eigenvectors capture a high degree of this network autocorrelation. (2) The presence of network autocorrelation introduces estimation biases in related variables, resulting in underestimations of economic size, population size, visa restriction, and international trade, while overestimating cultural and institutional differences, geographical distance, colonial relationship. (3) Various factors affect the Eurasian continental sub-region's air passenger flows differently, indicating regional variations. This study takes a step towards improving our understanding of network autocorrelation in air passenger flows research.

What shapes international air transportation in Southern Africa? − The evidence considering the impact of Covid-19

The lack of intra-continental air connectivity in Africa has long been criticized. This paper aims to understand the factors determining intra-regional air transportation in the Southern Africa region and the impact of the COVID-19 pandemic. Firstly, we analyze the geospatial characteristics of the air transport network, airport connectivity, and market structure before and after the pandemic. We also examine the progress of air transport liberalization in Africa and its impact on market structure and airline privatization. We find that the liberalization facilitated market competition and the progress of privatization, but its influence didn’t sustain long. Secondly, based on monthly data at the city level from April 2012 to December 2021, using monthly Total Night-time Light Intensity (TNLI) as a proxy of gross regional product (GRP), we develop a gravity model to empirically investigate the determinants of international air passenger flows within Southern Africa. The analysis shows that the factors promoting air traffic include GRP, air transport liberalization, the presence of low-cost carriers, and airline privatization, while high market concentration and diplomatic disagreements hinder air passenger flows. The pandemic and associated government restriction measures have severely impacted intra-regional air transport connectivity, while the vaccination program has significantly contributed to the recovery of air transportation.

Forecasting Post-Epidemic Air Passenger Flow Among Hub Cities in China Based on PLS and GA-SVR Model

For stakeholders and companies involved in civil aviation to plan and make wise decisions, accurate estimates of air passenger flow are necessary. The conclusions drawn from this model serve as an invaluable resource for pertinent choices. The top hub cities in mainland China's air travel network are predicted using a variable weight combination model that combines PLS and GA-SVR. According to the test results, the model developed in this study improves prediction accuracy. This demonstrates how well detailed information about social and economic development can be gleaned from linear development patterns and nonlinear fluctuation rules. Predictions can be made with more accuracy and a better fit as a result. Over the next five years, it is predicted that over 300 million passengers will fly between the top hub cities in mainland China, an increase of 6.51% per year. The growth in passenger traffic varies significantly between different routes. The routes from Beijing to Shanghai and Shanghai to Shenzhen saw the most travellers, while the Beijing-Chengdu route saw the fastest growth in traveller numbers. The study's findings provide useful advice for civil aviation businesses and people involved in their decision-making, fostering growth in the sector during the post-pandemic period.

Air passenger flow forecasting using nonadditive forecast combination with grey prediction

Air passenger flow forecasting plays a critical role in managing air transportation. It is important to develop accurate methods for forecasting the flow of passengers to formulate relevant operational plans and the schedule of airports. Even though empirical evidence has shown that forecast combination can improve the prediction accuracy of single-model forecasts, no study in the literature on transportation to date has examined the performance of such combinations to predict the demand for air transport. In light of the usefulness of grey prediction, which does not require applying any statistical test to examine the data at hand, and relationships among forecasts to be reckoned with, this study proposes using the nonadditive Choquet fuzzy integral to nonlinearly synthesize forecasts from four commonly used univariate grey prediction models into composite forecasts. The results of experiments confirmed that the risk arising from the incorrect choice of forecasting models can be eliminated by using forecast combinations. The proposed combination method based on grey prediction significantly outperformed the other competitors considered.

Re-examining the Rank-Size Distribution, Characteristics, and Evolutionary Trends of Air Transport Flows in China

Understanding of air transport geography is essential since air transport activity is highly related to regional economic development. A geographical issue arises: Does Zipf’s law strike again? Zipf’s law can be an efficient tool to detect the geographical distribution of air passenger (or freight) flows. Using provincial passenger and freight traffic data from 1980 to 2019, this study adopts Zipf’s law to analyze rank-size distribution characteristics of the air transport industry in China and its evolution. We then build a Geo-Detector model to explore the driving forces of geographical distributions (of air traffic). The findings are as follows. First, the geographical distribution of aviation flows in China complies with Zipf’s law, and the freight flow is more significant than the passenger flow. From a spatial to spatial-temporal perspective, the geographical distribution of different aviation flows (i.e., air passenger and freight flows) can be more diverse. Second, critical factors like international import trade, the number of foreign business travelers, and foreign direct investment combine to shape the geographical distribution of air traffic.

FORECASTING OF AIRLINE PASSENGERS BASED ON MACHINE LEARNING

The management of airlines depends on the forecasting of air passenger flow, but standard forecasting techniques cannot guarantee the accuracy of the forecast. When they encounter large-scale, multidimensional, nonlinear, and non-normal distributing time series data, they have the ability to generalize. In this paper the SVM regression is implemented to help with forecasting air passenger flow. We discover that the SVM regression algorithm's outcome exhibits the least inaccuracy when compared to the other two forecasting techniques by carefully choosing the parameters and kernel function. Concerns about demand splits among service providers have been given to every sector by the nearperfect competition scenario. This is especially important in the airline business, where high service standards are the norm. Demand is driven by the player who achieves the greatest mappings between every one of his the airline's offerings and the set of consumer preferences. The airline business has grown exponentially as a result of the economic reforms of 1991, which were promptly followed by the privatization of Indian skies, creating nearly ideal competition. More specifically, cross-border activities have started in the international sectors, where previously only domestic carriers operated. Keywords – Airline Passengers, Support Vector Machine, Forecasting, Machine Learning

Analysis and Forecasting of International Airport Traffic Volume

Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of continuous data, the fuzzy data with the smallest errors can be subjected to SVR to find the optimal hyperplane model with the minimum distance to the appropriate support vector sample points. The performance of the proposed model was compared with those of five other models. Of the compared models, the FSVR model exhibited the lowest mean absolute percentage error (MAPE), mean absolute error, and root mean square error for all types of traffic at all of the airports analyzed; all of the MAPE values were below 2.5. The FSVR model can predict future growth trends in air traffic, air passenger flows, aircraft flows, and logistics. An airport authority can use this model to analyze the existing operational facilities and service capacity, find bottlenecks in airport operations, and create a blueprint for future development. The findings revealed that implementing a hybrid modeling approach, specifically the FSVR model, can significantly enhance the performance of the SVR model. The FSVR model allows airlines to predict traffic growth patterns, identify viable new destinations, optimize their schedules or fleet, make accurate marketing decisions, and plan traffic effectively. The FSVR model can guide the timely construction of appropriate airport facilities with accurate predictions. Rapid, cost-effective, efficient, and balanced transportation planning enables the provision of fast, cost-effective, comfortable, safe, and convenient passenger and cargo services while ensuring the proper planning of the airport’s capacity for land-side transportation connections.

Analyzing multiple COVID-19 outbreak impacts: A case study based on Chinese national air passenger flow

The spread of COVID-19 results in a significant drop in traffic levels worldwide. Quantifying the impact of multiple COVID-19 outbreaks on traffic systems is critical to developing differentiated policies in the future. This paper proposes a novel COVID-19 multiple outbreak analysis method (NCMOA), dividing the impact scope and degree under multiple COVID-19 disturbances, and using the recovery rate and accumulated loss to quantify the impacts on air passenger flow. A case study based on Chinese national air traffic flow is executed, and the recovery patterns and the differentiated disturbances are analyzed. Results show that air passenger flow recovers with a similar pattern after the first outbreak, and subsequent outbreaks cause local effects and cannot affect the overall recovery pattern. Further, the heterogeneous influence factors and trends on the epi-centers (EC) and the nation are analyzed. In addition, the methods and results of this paper quantify the impact of COVID-19 on air passenger flow at a more detailed level under multiple disturbances. They could provide a basis for differentiated policy formulation of airlines and government in the future.

A Multi-Period Model for Optimal Changi Airport Check-In Counter Operations

Growth in air passenger flow has caused severe congestion at the airport check-in counter, posing a significant problem for airport management. Particularly during the check-in process, the necessary authorities must coordinate sufficient facilities with adequate staffing levels. The airport check-in counter problem (ACCAP) is a field concerned with establishing the optimal number of check-in counters to balance operating expenses and passenger wait times in order to reduce airport congestion. Expanding the number of counters and staff to a minimum operating cost is able to prevent the congestion problem from escalating without incurring further operating expenses. This paper focused on proposing optimal scheduling of airport check-in counters operations, including staffing. A dynamic model with multi-period principles is adapted to address the aforementioned problem by balancing the trade-off between service performance and operational cost. As a case study, data from Singapore Changi International Airport was utilized. The findings are also discussed in terms of the flow of passengers throughout the airport check-in procedure and operations. As a result, the number of activated counters is minimized throughout all shifts by applying the dynamic model at the average service time. At the same time, there are fewer passengers in the queue.

Structural analysis of air passenger flow in the Republic of Croatia from the COVID–19 pandemic impact perspective

Air transport is a globally significant mode of transport that carries people and goods over relatively long distances in a relatively short time. In the case of the Republic of Croatia there are some relevant geographical factors that make it necessary to connect the country to domestic and international airlines, such as territorial division (Dubrovnik’s disconnection from the rest of the Croatian mainland), rugged shape and mountainous terrain. In addition, as a tourist destination, the country should offer good transport links to provide the best experience for tourists. The aim of this research is to conduct an in-depth structural analysis of the intensity and dynamics of passenger air traffic at the global, European, and national (i.e. Croatian) level, considering the specific crisis of the COVID-19 pandemic as a relevant factor in the development of passenger flow. To achieve the defined objective of this research, a structural analysis of the relevant indicators for the development of passenger flow at the airports of the Republic of Croatia was conducted. In addition, a comparative analysis of the intensity, structure and dynamics of passenger flow was carried out within the framework of a comparison of the relevant criteria and statistical data on the indicators of passenger flow at the international, regional, and national levels. The COVID-19 pandemic has significantly affected the state of passenger flow in air transport in terms of organisation and logistics, and especially in terms of the intensity and dynamics of passenger flows. Accordingly, uniform criteria for monitoring passenger flow, systematisation and structural analysis of relevant formation indicators are the basic requirements for continuous monitoring and planning of passenger flow in air transport.

Subsidence Monitoring and Mechanism Analysis of Anju Airport in Suining Based on InSAR and Numerical Simulation

The mountainous area of southwest China is characterized by significant topography and complex geological conditions, which pose great challenges to the airport’s site selection, construction, and safe operation. Suining Anju Airport, one of the key projects under construction in southwest China, is essential in alleviating and dredging the air passenger flow in Sichuan Province. Because the overlying quaternary strata’s physical and mechanical properties, thickness, and distribution range are fairly different in the longitudinal and transverse directions, the Anju Airport’s foundation in the hilly area has typical inhomogeneity. Large-scale excavation and filling pose a challenge to the ground stability of the airport. To comprehensively monitor Anju Airport’s uneven ground subsidence during the construction period, this paper selected SAR image data collected by the Sentinel-1A satellite from May 2018 to June 2021 to extract time-series ground subsidence measurements based on the SBAS-InSAR method. Furthermore, based on the simulation of roadbed filling in the airport’s parallel slide fill area, the dynamic evolution analysis of soil stress field and internal subsidence caused by roadbed filling activities was carried out to further reveal the occurrence mechanism of ground subsidence. The monitoring results show that the subsidence centers of Anju Airport are mainly distributed in the filling areas, and the average annual subsidence is −20~−75 mm/yr from May 2018 to June 2021. Comparative analysis with in situ data indicates that the RMSE of InSAR monitoring results was ±6.12 mm. The numerical simulation shows that the subsidence of the airport parallel slide is mainly caused by a load of subgrade filling body and the compression of its weight. The results of this study can provide reference methodology and data support for the construction and future safe operation of Suining Anju Airport.

Research on temporal and spatial distribution characteristics of passenger flow of Daxing airport line based on automatic fare collection data

AbstractThe airport line plays an important role in the airport landside traffic connection, which greatly relieves the urban road pressure. Studying the characteristics and laws of the time and space distribution of the airport line passenger flow will provide basic support for the airport line's capacity configuration, passenger flow control, and air‐track coordinated operation, and will also provide quantitative basis for the layout of urban terminal buildings. This paper proposes a method to analyse the temporal and spatial distribution characteristics of airport line passenger flow based on AFC card swiping data. By mining and extracting the travel behaviour data of subway airport line air passengers, the distribution characteristics of airport line passenger flow are analysed and visualized from two aspects of passenger travel time and space. Finally, a case study is carried out based on the actual data of Daxing airport express. The results show that: (1) the travel time of departure and return passengers is concentrated in 60–90 min; (2) The departure and arrival points of passengers are concentrated within the Fourth Ring Road, and a large number of air passenger flows are gathered and evacuated at hubs such as Beijing South Railway Station and Beijing West Railway Station.

The determinants of air cargo flows and the role of multinational agreements: An empirical comparison with trade and air passenger flows

AbstractThis paper analyses the effect of the standard gravity model variables and of various multinational agreements—namely the Euro, the European Union (EU), the Schengen Agreement and other regional trade agreements (RTAs)—on the volume of air cargo flows. To compare the impacts, the data set created for this analysis contains intra‐ and extra‐European air cargo flows as well as data on air passenger and total trade flows. The results suggest that the impact of the analysed multinational agreements on air cargo flows diverges completely from their impact on total trade flows—however, the effects on air cargo flows are more similar to the effects on air passenger flows. Whereas the Euro and the Schengen Agreement affect air cargo volumes positively, EU membership and other RTAs do not significantly affect trade by air. Methodology‐wise, different dynamic structural gravity models are formulated and estimated with Poisson pseudo‐maximum likelihood (PPML). Including intranational flows and controlling for multilateral resistance, endogeneity and globalisation effects reverses the impact of some of the policy variables compared with an estimate based on a simple structural gravity model.

Determinants of Intercity Air-Passenger Flows in the “Belt and Road” Region

Air-passenger flow, denoting intercity connections, has been a focal point of studies pertaining to urban networks. While most existing studies include only the geoeconomic characteristics of nodes as explanatory variables, this analysis developed a gravity model by incorporating further factors (e.g., cultural disparity and institutional disparity) that might influence air-passenger flows in the “Belt and Road” region. The primary findings are as follows: (1) The cultural and institutional disparities correlate negatively with the air-passenger flows in this region; (2) air-passenger flows are positively related to border, population and economy size, and economic disparity; (3) flows tend to first increase and then subsequently decrease as geographical distance increases; (4) the impact of the factors on the flows varies by subregion. This study could serve as a reference for those interested in gaining a greater insight into air-passenger flows and could also help improve regional strategies for air-transportation development.

The effect of the European Emissions Trading System (EU ETS) on aviation demand: An empirical comparison with the impact of ticket taxes

To regulate the rising environmental impact of flying, the EU integrated aviation into the European Emissions Trading System (EU ETS) in 2012. This study gives an empirical ex-post evaluation on the effect of the EU ETS on aviation demand using a gravity model approach. The results of the Poisson pseudo-maximum likelihood (PPML) estimates suggest that the EU ETS has so far had no statistically significant effects on intra-EEA (European Economic Area) air passenger flows. This study additionally controls for the impact of the Austrian and German air transportation tax on aviation demand. The ticket tax brings about statistically significant and robust demand reductions on the affected country-pairs. But although the tax reduces demand, this policy may still not be the adequate measure as the aviation taxes that are in place do not relate to the amount of CO2 emitted by air travel. Generally, policy instruments are preferred that incentivize emission reductions, as is the case with kerosene taxes or emissions trading systems (ETS).

Linking the Determinants of Air Passenger Flows and Aviation Related Carbon Emissions: A European Study

Increasing global concern regarding the negative consequences of climate change will see the introduction of comprehensive policy governing aviation-related carbon emissions. With demand for air travel set to accelerate over the next three decades airlines are faced with the task of catering to increased demand while simultaneously achieving emission reductions. In this study we identify the determinants of air passenger flows and flight frequency and assess their impact on carbon emissions and carbon efficiency. Confining our analysis to the European market, we employ Fixed-Effects (FEIV) and Random-Effects (REIV) instrumental variables modelling techniques to 150 intra-Europe routes and find that the factors that are significant in influencing the operational planning decisions of an airline often lead to carbon inefficiencies. Our findings have important implications for both airlines and policymakers. For airlines, we show how the decisions they make to optimise operations can have negative environmental consequences, while we make suggestions as to how policymakers can incentivise airlines to achieve emission reductions.

Business Flies: The Trade Promoting Effect of Air Connectivity

Despite the increasing dominance of digital technology in modern communications, face-to-face interactions are believed to play an irreplaceable role in facilitating complex cross-border business transactions. However, empirical research to identify the impact often faces the challenge of two-way causality when cross-country in-person meetings are measured by international air passenger flows. We propose a novel instrumental variable to estimate the causal effect of international air travel on bilateral trade. Our identification strategy exploits variations in connecting flight capacities in the global flight network to leverage exogenous variations in the air connectivity between two countries. With the inclusion of stringent fixed effects in our estimations and checked for robustness against many possible threats to the identification, our results show that international air connectivity facilitates trade between countries, but only for industries with a higher reliance on relationship-specific investments or incomplete contracts. We also find that stronger enforcement of contracts in the importing country amplifies the trade promoting effect of air connectivity, and that trade in new products, relative to existing products, responds more positively to improved air transport links. Together, these findings suggest that in-person communications reduce transaction costs in international trade by facilitating the exchange of complex knowledge that cannot be easily and completely codified and by breaking market entry barriers.

Business Flies: The Trade Promoting Effect of Air Connectivity

Despite the increasing dominance of digital technology in modern communications, face-to-face interactions are believed to play an irreplaceable role in facilitating complex crossborder business transactions. However, empirical research to identify the impact often faces the challenge of two-way causality when cross-country in-person meetings are measured by international air passenger flows. We propose a novel instrumental variable to estimate the causal effect of international air travel on bilateral trade. Our identification strategy exploits variations in connecting flight capacities in the global flight network to leverage exogenous variations in the air connectivity between two countries. With the inclusion of stringent fixed effects in our estimations and checked for robustness against many possible threats to the identification, our results show that international air connectivity facilitates trade between countries, but only for industries with a higher reliance on relationship-specific investments or incomplete contracts. We also find that stronger enforcement of contracts in the importing country amplifies the trade promoting effect of air connectivity, and that trade in new products, relative to existing products, responds more positively to improved air transport links. Together, these findings suggest that in-person communications reduce transaction costs in international trade by facilitating the exchange of complex knowledge that cannot be easily and completely codified and by breaking market entry barriers.

The evolutionary mechanism of China's urban network from 1997 to 2015: An analysis of air passenger flows

To reveal the complexity of China's urban network, this paper proposes an empirical approach to examining the evolutionary mechanism of, rather than just mapping, the spatial pattern of the Chinese urban network from 1997 to 2015. The results show that the Chinese urban network is a complex structure that is embedded in China's unique context, where two interwoven processes, namely, globalization and decentralization, have shaped the network's spatial pattern. Moreover, the Chinese urban network is shifting from a single-core structure, with Beijing as its center, towards a diamond structure, anchored by the four ‘national centers’ of Beijing, Shanghai, Guangzhou, and Chengdu. Along with the transformation of their network structure, Chinese cities are interacting more closely and frequently. The results of the exponential random graph model (ERGM) show that the two interconnected forces, namely, globalization and decentralization, have tremendous impacts on the evolution of the Chinese urban network. Specifically, the uneven decentralization has a complex effect on the evolution of China's urban network in terms of a polarization-trickle-down-repolarization process. And the integration of inland China into the process of globalization has a significant effect on the connectivity of Chinese cities and causes a significant rise of central and western cities within the Chinese urban network.

Forecasting air passenger traffic flow based on the two-phase learning model

The future airports will head toward a highly intelligent direction, like the unmanned check-in services, while the scale and resources allocation of the ground service are tightly related to the air passenger flow. Therefore, forecasting passenger flow accurately will affect the development of future airports and the optimization of service of civil airlines significantly. As a kind of time series, air passenger flow is influenced by multiple factors, particularly, the stochastic part of seasonality, trend and volatility. These will ultimately affect the accuracy of the prediction. Therefore, this paper introduces a prediction model based on a two-phase learning framework. In phase one, various predictors cope with different features of time series in parallel and the prediction results are integrated in phase two. Furthermore, this paper has compared principal error indicators with actual data and results show that the two-phase learning model performs better than current fusion models and owns stable performance.