Air Cargo Demand Research Articles

Modeling the volatility of international air freight: A case study of Singapore using the SARIMAX-EGARCH model

The volume of international air cargo not only generates income and jobs for countries, but also promotes international trade and other economic sectors. This study used SARIMAX-(E)GARCH volatility models to model Singapore's air cargo volume in ten key markets namely Malaysia, Indonesia, Thailand, Vietnam, Mainland China, Hong Kong SAR, Japan, United Kingdom (UK), France and Germany. Using socioeconomic factors and monthly data for the period 1991–2022, empirical models reveal that GDP and bilateral trade are important explanatory factors that have a positive impact on international air cargo volume in most of Singapore's key markets. Own price (international air freight), substitute price (deep sea freight transportation services) and the COVID-19 pandemic in the early stages also have different impacts in some key markets. In addition, the effects of positive and negative air cargo demand shocks are also found in different markets. These findings are the basis for forecasting international air cargo volume and making decisions and policies for air freight development.

Optimization Strategy for Storage Location Allocation in Air Cargo Warehouse Area

Facing rapid growth in air cargo demand within the global supply chain and limitations of traditional storage location allocation methods, this study proposes an intelligent optimization strategy for storage location allocation. The strategy considers key factors like flight schedules, cargo collection and delivery data, cargo terminal topology, and in-and-out paths. It intelligently allocates each cargo batch to the most suitable location, assigning more flights' cargo to locations near the exit, thereby reducing cargo handling distance and offloading time, and enhancing operational efficiency and cargo handling capacity in the air freight sector. The algorithm, based on the latest optimization theories and techniques, seeks optimal solutions under various constraints. The model and algorithm’s effectiveness is validated through simulation experiments using actual flight data. The results indicate that, compared to traditional strategies, the optimized strategy reduces response time by 62%.

Solving the mystery of discrepancies and double counting in air cargo through demand and supply big data analysis

This paper estimates and analyses the air cargo demand and supply imbalances between large geographical regions based on newly collected demand and supply data. Due to the need for more data for academics and the industry, and the lesser research interest compared to its passenger counterpart, limited empirical research on air cargo market dynamics has been conducted. Live flight and aircraft data were collected for five consecutive years to reconstruct the air cargo network and capacity. Air cargo sales tonnes data were collected and introduced as these data eliminate double counting of transfer route volumes. Both datasets were used and compared to analyse the air cargo trade imbalances. The major findings indicate an imbalance in demand on most of the 110 studied region pair combinations. The supply data indicate a high imbalance for freighter capacity, a relatively smaller imbalance for integrator capacity and a limited imbalance for the wide-body belly capacity. The data indicate that the Middle East, Northeast Asia, Russia, Central Asia, and Central America regions are all transfer or in-transit regions where a large amount of cargo passes through. However, limited cargo volumes originate or find their final destination here. Although the general assumption indicates that air cargo supply follows air cargo demand, imbalances between demand and supply in opposing directions were found for several region pairs. To the best of our knowledge, this paper is one of the first academic sources to introduce data analysis based on sales tonnes. This research assists academics and the industry in getting a better understanding of the current air cargo market dynamics. It also provides a base to enhance future air cargo market research, analysis and forecasting.

Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach

The prediction of air traffic demand (passengers and cargo) in a regional/national air transport system is essential. Knowing the behavior of future demand helps, on the one hand, the design and execution of air transport public policies, which, for example, help to focus, guide and prioritize investment (public and private) for the expansion / modernization of airport infrastructures (or development of new airports), act on tariff policies, implement changes in regulatory policy, etc.; on the other hand, it helps airport managers to plan the airport. Therefore, in this paper, a short-term forecast (5 years) of the demand for air cargo transport was carried out, applied to a specific case study (Colombia), taking into account the most severe pandemic period (the year 2020). To perform the forecast, an approach based on Machine Learning/Deep Learning (ML/DL) method comprising a hybrid of convolutional and recurrent memory neural networks (that allow space-temporal non-linear analysis, such as multi-variable spaces and temporal multi-steps), is presented. The analysis developed here establishes the optimal length of the prediction period; on the other hand, the proposed methodology allows the identification of the most relevant socioeconomic features in the prediction of air cargo demand (domestic and international), i.e., interpreting the ML/DL results obtained through the variational analysis of different combinations of features. The results show that international air cargo demand is strongly dependent on Gross Domestic Product (GDP) and PCG (Per Capita GDP), while domestic air cargo demand is significantly dependent on PCG. Finally, the results show, for the case study country, very rapid recovery of air cargo demand at pre-pandemic rates (behavior already found in other recent studies and research).

Air cargo demand in Africa: Application of cointegration and error correction modelling techniques

This paper examines the short- and long-run impacts of real income, merchandise exports, foreign direct investment, real interest rate and jet fuel prices on air cargo demand in four African countries for the period 1980–2019. Annual data on all the variables were analyzed using cointegration and error-correction modelling techniques. Empirical results reveal long-run equilibrium relationship among the variables. The impact of the variables on air cargo demand vary across the countries. Gross domestic product per capita is a major determinant of air cargo demand in Egypt, Nigeria and South Africa in the short and long term. Real income positively influenced air cargo demand in these countries. For Kenya, real interest rate has positive long-run effect on air cargo demand. Implementation of measures that will enhance economic development, trade and improve airport infrastructure are necessary.

The competitiveness of global cargo airports employing the Best-Worst Method

PurposeThe global economy and air transport business have been negatively affected owing to the COVID-19 pandemic outbreak. As countries tighten restrictions on international movements, the growing emphasis on air cargo places pressure on airports to maintain and upgrade their cargo policies, facilities and operations. Hence, ensuring the competitiveness of cargo airports is pivotal for their survival under volatile global demand. This study aims to evaluate the importance of competitiveness factors for cargo airports and identify areas for further improvement.Design/methodology/approachThis study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors.FindingsThe results identified “Transport Capacity” as the most significant competitiveness factor, implying that airport connectivity is crucial in promoting cargo transportation at hub airports. This result was followed by “Airport Operations' and Facilities' Capacity” and “Economic Growth.”. Additionally, the results identified Hong Kong International Airport as the best-performing cargo airport, followed by Aéroport de Paris-Charles de Gaulle and Incheon International Airport, respectively. Furthermore, both selected European airports are the most competitive airports in terms of “Financial Performance” and appear to be aware of the significance of their brand value.Originality/valueThis study forms a reference framework for evaluating cargo airports’ competitive positions, which may help identify airports’ relative strengths and weaknesses. Moreover, this framework can also serve as a tool to facilitate the strategic design of airports that can accommodate air cargo demand flexibly under demand uncertainty.

Identifying the key influencing factors for the growth of air cargo demand

PurposeAir transport accounts for nearly 40% worth of the global trade cargo volume, where more than 50% of the air cargo is carried on passenger flights. Therefore, this paper aims to focus on identifying the influencing factors for both passenger and cargo demand-driven networks to smoothen the global supply chain.Design/methodology/approachThe data for the study was collected through literature reviews and interviews with industry experts. The analytical hierarchy process was used to analyze the expert's opinions on the critical factors affecting air cargo demand growth. Regression analysis was conducted using the selected variables to develop a model to calculate air cargo demand growth.FindingsAccording to the expert opinion, it was identified that facilities under airport capacities and facilities are mainly affected by the air cargo carried by combi carriers. The model was developed considering the air connectivity index and air cargo demand at destination variables.Research limitations/implicationsThe factors identified here are mainly related to the current situation in Sri Lanka. Applying this methodology to other economic zones will add new factors related to their economic contexts and could be generalized as the influencing factors for the growth of air cargo demand by finding more results.Originality/valuePrevious studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from combi and all-cargo carriers together. More than 98% of air cargo trades in Sri Lanka are happening through combi carriers. Hence, Sri Lanka will be a best case study to analyze the behavior of combi carriers.

Determinants of Air Cargo Demand in The European Region

Air cargo demand estimation is crucial for every part of the air industry for determining policies, making decisions on investments, pricing, marketing, etc. This study analyses factors affecting the air cargo demand of the European Region (23 countries) where no studies were found by making use of the two-step generalized method of moments (GMM). The effects of Total Airport Investments, Consumer Price Index, GDP Per Capita, Population, Foreign Trade Volume, Industrial Production Index, and Exchange Rate on Air Cargo Demand were examined with the data set covering the years between 2009 and 2018. The findings indicated that previous air cargo demand has explanatory power on current demand, and there was an inverse relationship between the change in the consumer price index and the amount of cargo. In addition, the panel regression estimator indicates a positive relationship between Foreign Trade and Industrial Production Index. The results show that price stability is of great importance not only from a macroeconomic perspective but micro economically also.

An analysis of the Chinese scheduled freighter network during the first year of the COVID-19 pandemic

COVID-19 caused the vast majority of passenger flights to be grounded, but the crisis raised the importance of the network of dedicated cargo flights and, therefore, interest in its development. This paper aims to evaluate the Chinese scheduled freighter network (CSFN) via its topological properties and to explore its changes following the COVID-19 pandemic. Using spatial analysis with the complex network theory (CNT), the paper found that the CSFN displays small-world and scale-free network properties, similar to that of air passenger network. Hangzhou, Shenzhen and Nanjing are the dominant national hubs in the CSFN because they host the headquarters of many e-commerce giant enterprises and have relatively underutilized airport capacities. The CSFN has improved since the COVID-19 pandemic, with increased network average degree, clustering coefficient, and closeness, and reduced average path. These improvements were mainly driven by major hub cities whose centralities had been strengthened with more route connections. Since China's air passenger traffic had quickly restored in the second half of 2020, we argue that the changes in the CSFN during COVID-19 were unlikely to be a result of the substitution effect between freighter and passenger aircraft. It was more likely a result of the higher air cargo demand during the pandemic and airlines' realisation of the importance of freighter operations in China.

Investigating Paradigm Shift from Price to Value in the Air Cargo Market

This study presents a paradigm shift in the air cargo market based on Korea and Incheon Airport’s empirical data. The air cargo market has traditionally handled expensive items and has been a supplier-oriented market. There has been little room for individual customers in this market. However, “value” gradually replaces “price” in this market. The value depending on consumer preferences significantly impact air cargo demand. Consequently, items transported by air cargo are changing, and airlines and other market participants are revising operation policy. Economic growth may be losing its dominant power as the main growth engine in the air cargo market. This study identifies the weakened link between air cargo increase and economic growth based on the Granger causality test. COVID-19 calls for a deeper understanding of the paradigm shift in the market for sustainable air transport because COVID-19 will further stimulate it. In this regard, the air cargo business, which maintains a stable trend even during COVID-19, is seen as a new opportunity for the aviation industry. Since sustainable air transport requires an accurate understanding of the paradigm shift in the air cargo market, this study enhances our knowledge of the paradigm shift and provides significant implications for sustainable air transport.

Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo

This study aims to forecast air passenger and cargo demand of the Indian aviation industry using the autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models. We utilized 10 years’ (2009–2018) air passenger and cargo data obtained from the Directorate General of Civil Aviation (DGCA-India) website. The study assessed both ARIMA and BSTS models’ ability to incorporate uncertainty under dynamic settings. Findings inferred that, along with ARIMA, BSTS is also suitable for short-term forecasting of all four (international passenger, domestic passenger, international air cargo, and domestic air cargo) commercial aviation sectors. Recommendations and directions for further research in medium-term and long-term forecasting of the Indian airline industry were also summarized.

Demand for air cargo and demand for virtual commerce goods in Brazil

Some studies have reported that current demand for air cargo transport has largely derived from the demand for goods in B2C-type electronic commerce. Marketing experts consider that consumer income bracket, cost of goods, freight costs and other less tangible factors influence consumer decisions to purchase online. This study makes an econometric investigation as to whether determinants of e-commerce demand in Brazil can explain the behavior of the demand for air cargo transport services. The study sample period is 2000 to 2016. Unit root and co-integration tests were conducted prior to the regression analysis of the results obtained for the air transport services demand as well as tests for correlation among explanatory variables for the air cargo demand. The reported results make it possible to conclude that the determinants of e-commerce demand do explain the demand for air cargo transport services. It is important to underscore that studies designed to determine the dimensions of cargo aircraft fleets and airport cargo terminals installed at airports or in their vicinities must always take into account the demand for goods offered by B2C-type e-commerce in their analyses.

A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting

The accurate forecast of air cargo demand is essential for infrastructure construction planning and daily operation management. Evidently, it is extremely difficult to capture the dynamics of time series impacted by distinct sources. To reduce the complexity of data, the current popular method is to decompose the original data into several modal branches with different characteristic attributes. But the new problem is that the components generated by decomposition are still irregular and unstable, and there is no unified method to predict them. In this paper, a new secondary decomposition-ensemble (SDE) approach with a cuckoo search algorithm (CSA) is proposed for air cargo forecasting. More specifically, the original air cargo time series is decomposed into several components by an enhanced decomposition formwork, which consists of variational mode decomposition (VMD), sample entropy (SE) and empirical mode decomposition (EMD). Subsequently, the ARIMA and the Elman neural networks (ENN) optimized by CSA are respectively applied to forecast the trend component and the low-frequency components, during which the phase space reconstruction (PSR) is conducted to determine the input structure of neural networks. The final forecasting results are obtained by integrating the predicted values of each component. Besides, the air cargo series from three different airports in China are adopted to validate the performance of our proposed approach and the empirical results show that it is superior to all other benchmark models in terms of the robustness and accuracy.

Factors Affecting Air Cargo Demand: Focus on Trade Volumes between South Korea and Intra-Asia

우리나라 항공화물의 수출입 비중은 물동량 기준으로 0.3%로 낮은 수치를 나타내고 있지만, 운송 화물가액 관점에서 2017년 기준 항공화물 수출액은 1,750억 달러로 총 수출액의 30.5%를 차지하고 있다. 최근 항공화물 운송 부문은 IT 제품 및 특수화물의 수요 확대 등으로 수출 증가세를 전망하였지만 무역 갈등과 세계 무역량 약화 등으로 인해 운송 실적이 하향세를 보이고 있다. 이처럼 다양한 요인들로 인하여 항공화물수요가 민감하게 반응하는 것을 알 수있다. 본 연구에서는 인트라아시아 국가를 대상으로 서울(인천국제공항, 김포국제공항)을 기종점으로하여 요인들의 영향력을 확인할 수 있는 항공화물 수입/수출 모형을 제시하였다. 모형 도출결과 항공화물수요에 양의 상관관계를 나타내는 요인으로 1인당 GDP, 총수출, 한한령, FTA 체결, 첨단기술제품 수출을 확인하였고 음의 상관관계를 나타내는 요인으로 인플레이션, 실업률,전염병, 금융위기를 확인하였다. 모형을 기반으로 국가별 영향 요인에 따라 항공화물수요 변화량 예측 및 이를 통한 사전대비와 전략 마련에 도움이 될 것으로 기대된다.Purpose : The purpose of this study is to identify the factors that affect air cargo demand between South Korea and Intra-Asia. Research design, data, methodology : The OFOD data provided by ICAO was used as a dependent variable and factors were used as independent variables. The research method is an analysis using ARIMAX/SARIMAX model. Results : As a result this research, we found that air cargo demand has positive correlations with GDP per capita, Gross exports, A law that bans Korean culture in China, FTA, and High-tech product exports. On the other hand, we found that air cargo demand has negative correlations with Inflation, Unemployment rate, Epidemic, Financial crisis. Conclusions : This study when it expected that changes or occur of factor, we anticipate changes the air cargo demand, which will help country or airline prepare ahead and strategies.

항공화물수요예측에서 계절 ARIMA모형 적용에 관한 연구: 인천국제공항발 미주항공노선을 중심으로

본 연구는 2003년 1사분기부터 2016년 2사분기 까지 인천국제공항에서 미주노선을 통하여 미주 내 공항에 도착하는 항공화물의 시계열 자료를 통하여 SARIMA 모형을 활용하여 항공화물 수요예측을 시행하였다. 또한 SARIMA 모형을 활용하여 만들어진 수요예측 모형과 기존 연구에 주로 활용되어졌던 ARIMA 모형을 활용하여 만들어진 수요예측 모형과 비교분석함으로써, 주기적인 특성 및 계절성을 가진 시계열 자료에 대한 SARIMA 모형의 상대적으로 우수한 예측 정확성을 입증하였다. 기존의 항공 관련 연구는 주로 여객에 관한 연구가 상대적으로 많았다. 또한 화물과 관련된 연구에서도 특정노선이 아닌 공항이나 전체에 대한 연구가 대부분이었다. 이러한 상황에서, SARIMA 모형을 활용하여 미주지역이라는 특정 노선에 대한 항공화물의 수요를 예측한 본 연구는 큰 의의가 있다고 생각된다. For forecasting air cargo demand from Incheon National Airport to all of airports in the United States (US), this study employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) method and the time-series data collected from the first quarter of 2003 to the second quarter of 2016. By comparing the SARIMA method against the ARIMA method, it was found that the SARIMA method performs well, relatively with time series data highlighting seasonal periodic characteristics. While existing previous research was generally focused on the air passenger and the air cargo as a whole rather than specific air routes, this study emphasized on a specific air cargo demand to the US route. The meaningful findings would support the future research.

International trade drivers and freight network analysis - The case of the Chinese air cargo sector

This study identifies the key drivers for China's international trade delivered by air with an augmented gravity model, and investigates the country's air freight network using complex network analysis. Gravity model estimation suggests that income elasticity for airborne trade at country level is 1.1, whereas the values for the three major economic zones are significantly higher. More importantly, we found that for China's international trade by air, the composition of economy is a more important driver than the size of economy. These results suggest that the Chinese air cargo sector will outpace the overall economic growth, with leading airports in the economic zones well-positioned to grow into major freight gateways. Air cargo demand in China was not as high as past GDP numbers suggested in the past, but is likely to experience sustained growth in the years to come. Network analysis suggests that cargo traffic volumes are concentrated in the catchments of metropolitan regions, where passenger hubs also serve as air cargo bases. However, domestic air freights currently mainly flow through a relatively small point-to-point network, suggesting that the leading airports are yet to become cargo gateway hubs despite their significant growth potential.

The underlying drivers and future development of air cargo

Air cargo plays a crucial role in the air transport chain and in the globalized economy. As for other modes of transport, the demand for air cargo is a derived demand. Previous studies showed that the main determinants of air cargo demand are merchandise trade and the share of manufactures in merchandise trade. This paper aims to fill a gap in the existing literature by additionally taking the influence of air freight yields and oil prices into account when modelling the global air cargo development. Furthermore, it provides an insight into the future development of air cargo. Forecasts until 2023 are made based on a number of scenarios for the main determinants of air cargo demand. Moreover, an insight is provided into the current air cargo market, including traffic levels and different types of actors and traffic flows. The results are useful not only for academics but also for industry stakeholders for which air cargo is an important contributor to profit and/or cash levels.

Empirical estimation of price and income elasticities of air cargo demand: The case of Hong Kong

This paper estimates the price and income elasticities of air cargo demand and examines how they may change after the 2008 financial crisis. Using a set of time series data, we simultaneously estimate the aggregated demand and supply functions of air cargo at Hong Kong International Airport (HKIA). We find that during the entire sampling period of 2001–2013, the price elasticity for air cargo transport demand at HKIA ranges from −0.74 to −0.29, suggesting that air cargo demand in Hong Kong reacts negatively to price (as expected) but does not appear to be very sensitive to price. The income elasticity ranges from 0.29 to 1.47 and appears sensitive to seasonality adjustment approaches. However, in terms of the speed of changes, air cargo demand changes much faster than overall economy, indicating the presence of a pro-cyclical pattern of air cargo traffic with respect to the overall economy. Our analysis shows that air cargo demand becomes more sensitive to changes in both price and income after 2008.

Optimisation of key performance measures in air cargo demand management

This article sought to facilitate the optimisation of key performance measures utilised for demand management in air cargo operations. The focus was on the Revenue Management team at Virgin Atlantic Cargo and a fuzzy group decision-making method was used. Utilising intelligent fuzzy multi-criteria methods, the authors generated a ranking order of ten key outcome-based performance indicators for Virgin Atlantic air cargo Revenue Management. The result of this industry-driven study showed that for Air Cargo Revenue Management, ‘Network Optimisation’ represents a critical outcome-based performance indicator. This collaborative study contributes to existing logistics management literature, especially in the area of Revenue Management, and it seeks to enhance Revenue Management practice. It also provides a platform for Air Cargo operators seeking to improve reliability values for their key performance indicators as a means of enhancing operational monitoring power.

Prioritisation of performance indicators in air cargo demand management: an insight from industry

Purpose – Real operational data are used to optimise the performance measurement of air cargo capacity demand management at Virgin Atlantic Cargo by identifying the best KPIs from the range of outcome-based KPIs in current use. Design/methodology/approach – Intelligent fuzzy multi-criteria methods are used to generate a ranking order of key outcome-based performance indicators. More specifically, KPIs used by Virgin Atlantic Cargo are evaluated by experts against various output criteria. Intelligent fuzzy multi-criteria group making decision-making methodology is then applied to produce rankings. Findings – A useful ranking order emerges from the study albeit with the important limitation that the paper looked solely at indices focussing exclusively on outcomes while ignoring behavioural complexity in the production of outcomes. Originality/value – This paper offers a practical overview of the development of performance measures useful for air cargo capacity demand management.