Air Traffic Forecast Research Articles

Air Traffic Demand Forecasting with a Bayesian Structural Time Series Approach

Airport planning, and therefore the development of air infrastructure, depends to a large extent on the demand forecast for the future. To plan investments in the infrastructure of an airport system and to be able to meet future needs, it is essential to predict the level and distribution of demand, both for passengers and air cargo. In the present work, a forecast was made, in the medium-long term (10 years), of the demand for passengers and air cargo, applied to a specific case study (Colombia), and where the impact on air traffic during the most severe period of the COVID-19 pandemic was taken into account. To achieve this objective, and as a methodological approach, a model of the Bayesian Structural Time Series (BSTS) type was developed, designed to work with time series data, and widely used for feature selection, time series forecasting, and the immediate inference of the causal impact. From the results obtained, two relevant aspects can be highlighted, firstly, both demand and its growth trend will recover very soon (in just a couple of years), compared to the pre-pandemic year 2019, which was analyzed in the case study. And, secondly, the model presents very acceptable MAPE values (between 1% and 7%, depending on the variable to be forecasted), which makes the BSTS method a viable alternative methodology for calculating air traffic forecasts.

An empirical evaluation of airport capacity and demand: insights regarding air traffic design hours and delay

PurposeAn important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.Design/methodology/approachBy reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.FindingsThe clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.Originality/valueThe functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

An empirical analysis of airport capacity evaluation: insights regarding air traffic design hours

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Airport capacity is rather difficult to describe due to its multi-faceted and dynamic nature, and it depends both on the available infrastructure, on external factors and on operating procedures. Moreover, annual capacity is used for long term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. By reviewing the relationships between hourly and annual air traffic volumes at 50 European airports during the period 2004-2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. Additionally, we appraise different daily traffic distribution patterns and their variation by hour of the day. The clustering of airports with respect to their capacity, operational, and traffic characteristics allows us to discover functional relationships between design hours and annual volumes. These relationships help us to propose empirical methods to derive expected traffic in design hours from annual volumes. This could be used to properly assess airport expansion projects or to optimise resource allocation tasks. Finally, we provide new evidence on the nature of airport capacity and the dynamics of air traffic design hours.

Air passenger demand forecast through the use of Artificial Neural Network algorithms

Airport planning depends to a large extent on the levels of activity that are anticipated. To plan the facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) (Machine Learning (ML)) was developed. In particular, for the analysis of the available time series, techniques of encoder-decoder networks of the type ConvLSTM2D have been applied. These architectures are a hybrid between Convolutional Neural Networks (CNN), very useful for the extraction of invariant patterns in their spatial position, and Recurrent Neural Networks (RNN), appropriate for the extraction of patterns within their temporal context (time series). The most relevant result of the present research is that the recovery in demand (volume and trend) to the levels reported before the pandemic is forecast for the period between the end of 2022 and the beginning of 2024 (depending on the type of traffic and scenario considered). Finally, the application of the forecasting model based on Machine Learning/Deep Learning (DL) presents, as a metric performance, a Mean Absolute Percentage Error (MAPE) value from 3% to 9% (depending on the scenario), which enables predictions of relative precision and introduces a new alternative technical approach to develop reliable air traffic forecasts, at least in the short and medium term.

Forecast of Aircraft Retirement Probabilities using Neural Networks

In this paper, we present a novel approach for the prediction of retirement curves, which summarize the survival / retirement probabilities of aircraft. Retirement curves are used to predict the future aircraft fleet composition, as an element of air traffic and emissions forecasts. Furthermore, retirement curves are a tool for aircraft manufacturers and leasing companies to estimate the need for replacement aircraft as part of global aircraft demand. We have applied a methodology involving neural networks, previously being used in the area of predictive maintenance. Transferring this method of data analysis to a new research field goes beyond previously applied methodologies, as neural networks are known for finding connections in data that cannot be explicated with other methodologies. In the context of retirement curves, neural networks can help to explain the influence of economic factors or general market development on aircraft retirement.We implement a neural network to calculate survival probabilities of aircraft and estimate the impact of economic framework data on survival probabilities. The neural network uses the global passenger aircraft fleet as training data base and predicts survival probabilities which then are arranged into retirement curves. We then compare the results to other retirement curve prediction methods. Finally, we take an outlook on possible enhancements of this method and amplification options to improve the quality of the forecast.

Development of an Algorithm for Determining the Aircraft Pushback Sequence

The air transport crisis, caused by the Covid Pandemic, not only forced the limiting of air traffic operations, but also, the verification existing air traffic forecasts. This time can be used for an in-depth look at the existing procedures at the airport, namely the procedures for performing aircraft take-off operations. After the renewed increase in air traffic, the problem of proper positioning of aircraft for take-off will reappear. The capacity problem at airports will return as air traffic increases. Capacity is a parameter that determines the traffic capacity of an airport, expressed as the theoretical maximum number of air operations that the airport is able to handle per unit of time. This parameter may also result from the seasonality of air traffic both throughout the year and changes over single days or even hours. Achieving a certain capacity may require the expansion of airport infrastructure or more efficient management of airport operations. An undesirable effect of increasing air traffic are delays related to airport capacity. The existing queue of aircraft waiting for take-off operations at the runway threshold, could be due to poor management and queuing of aircraft or insufficient airport infrastructure. It is possible to eliminate the queue phenomenon, at the runway threshold, through better sequencing of aircraft push-outs. This makes it possible to determine the order of the take-off of aircraft, already at the stage of push-out. The aim of the article is to present the possibility of a more effective use of the aircraft pushback sequence, increasing the capacity of the airport maneuvering area. The proposed algorithm sequencing of aircraft to taxi in order to minimize delays associated with deviations from the flight plan, the taxiway of the stand to the runway threshold, thereby reducing emissions into the atmosphere. The algorithm is based on the A-CDM project, which unites airports to exchange information necessary to better use the capacity of these airports.

Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm

In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Air traffic forecast and its impact on runway capacity. A System Dynamics approach

The uninterrupted growth of air traffic in Colombia has been reinforced since the 1990s by a public policy of liberalization of airspace, and by redirecting public and private investment towards the modernization and updating of airport infrastructures, giving in concession the busiest airports in the country, 19 to date. In the commercial air sector, and in this same period, the flag airline was privatized and new air operators, including low cost airlines (LCC), entered the market. Since 2012, air fares are completely deregulated. Consequently, in the last two and a half decades, passenger transport in Colombia grew by 863%. This important growth rate has been driven and led by the country's main airport, the Bogotá-El Dorado International Airport (BOG), in the capital of Colombia. But some technical reports consider the airport will reach its maximum capacity in the short term mainly because of its inability to expand its airfield (runway system). Due to this circumstance, the aeronautical public authority of Colombia gave course to the planning, design and construction of a new airport (complementary) on the outskirts of the city of Bogotá, which will enter the design phase in 2026/2027. Therefore, it is considered suitable the development of a traffic forecast for BOG in the medium term and to evaluate the impact of future demand on the runway capacity of the airport. Then, and due to the complexity of the air transport forecast, the use of System Dynamics (SD), is considered to be appropriate as an analysis approach. System Dynamics is based on feedback control theory and it is equipped with mathematical computer simulation models, which uses linear and non-linear differential equations. The results suggests a need to expand the airport case study (runway system) after mid-2019, where the current capacity utilization factor is around 100% and two to three runways will be required for the normal operation; after October 2022 the number runways required is set to three until the last period simulated (2023).

Forecasts of future scenarios for airport noise based on collection and processing of web data

PurposeThis paper presents an analysis of short-term (2025) scenarios for noise emission from civil air traffic in airport areas.MethodsFlight movements and noise levels at a given airport are predicted using a web-data-informed methodology based on the ECAC Doc.29 model. This methodology, developed by the authors in a previous work, relies on the collection and processing of air traffic web data to reconstruct flight events to be fed into the ECAC model. Three new elements have been included: i) topographic information from digital elevation models, ii) a fleet substitution algorithm to estimate the impact of newer aircraft, and iii) a generator of flight events to simulate the expected traffic increase.ResultsThe effects of these elements are observed in 2025 scenarios for the airports of London Heathrow, Frankfurt and Vienna-Schwechat. The results quantify the noise reduction from new aircraft and its increment due to the air traffic growth forecast by EUROCONTROL.

Forecasting Air Passengers of Changi Airport Based on Seasonal Decomposition and an LSSVM Model

This work aimed to determine a suitable method to provide air traffic passenger forecasts of Changi airport. A linear forecasting technique in the form of a seasonal autoregressive integrated moving average (SARIMA) model and a nonlinear technique known as the least squares support vector machine (LSSVM) were compared. A hybrid X-13 LSSVM approach was also compared. A fourth approach was proposed to leverage the outputs of the hybrid X-13 LSSVM method to conduct forecasts for longer forecasting horizons. Results showed that SARIMA, direct LSSVM and X-13 LSSVM methods were able to provide accurate 1-month-ahead forecasts. However, SARIMA and direct LSSVM methods both suffered from forecasting inaccuracy, as the forecasting horizon increased. The X-13 LSSVM outperformed both SARIMA and direct LSSVM methods, in terms of small magnitude errors and forecasting directional changes across the forecasting horizons. The proposed fourth approach was able to provide 24-months-ahead forecasts and was easy to implement.

Coordinated Airport Facility Development under Uncertainty

This paper presents an applied computation tool for coordinating various airport facility expansions and gives explicit consideration of the impacts of various uncertainties. An airport is modeled as a system consisting of components (e.g., runways, air cargo terminals, and maintenance facilities) that operate in series or in parallel. To balance (i.e., equalize) roughly the capacities of the components acting in series, the paper proposes a network flow formulation in which interactions between user flows and facilities are modeled. In addition to the uncertainty in air traffic forecasts, the effect of an uncertain aircraft mix is also considered. The final model is a stochastic mixed-integer nonlinear program that is converted to its deterministic equivalent because the number of stochastic scenarios is finite. An interactive solution framework is proposed to solve the problem after the problem's properties are explored. Numerical studies were conducted to demonstrate the usefulness of the proposed model and effectiveness of the solution algorithm. Prospective work includes application of the proposed development approach to a real-world case study.

Determining the Future Demand: Studies for Air Traffic Forecasting

The world has seen a rapid increase in air traffic and movement of passengers in the past two decades. Within Asia-Pacific itself, the number of aircraft departures saw a growth of approximately 200% in less than 10 years [1]. Air traffic forecast models would be useful in predicting the growth of air traffic in the future, so as to facilitate the development of technology, operations and infrastructure for the aviation industry. This paper aims to study air traffic models and find an approach for forecasting of the air traffic growth in Singapore. This paper applies the forecasting methodologies to predict the air traffic growth and checks the growth within the region by using simulations and modeling.

Air passenger demand forecast through the use of Artificial Neural Network algorithms

Airport planning depends to a large extent on the levels of activity that are anticipated. In order to plan facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) and Machine Learning (ML) was developed. In particular, for the analysis of the available time series, techniques of encoder-decoder networks of the type ConvLSTM2D have been applied. These architectures are a hybrid between Convolutional Neural Networks (CNN), very useful for the extraction of invariant patterns in their spatial position, and Recurrent Neural Networks (RNN), appropriate for the extraction of patterns within their temporal context (time series). The most relevant result of the present research is that the recovery in demand (volume and trend) to the levels reported before the pandemic is forecast for the period between the end of 2022 and the beginning of 2024 (depending on the type of traffic and scenario considered). Finally, the application of the forecasting model based on ML/Deep Learning (DL) presents, as a metric performance, a Mean Absolute Percentage Error (MAPE) values from 3% to 9% (depending on the scenario), which enables predictions of relative precision and introduces a new alternative technical approach to develop reliable air traffic forecasts, at least in the short and medium term.

Using Hybrid Fuzzy Neural Network to Improve the Accuracy of Air Traffic Flow Forecasts

Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the hybrid fuzzy neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by hybrid fuzzy neural network.

Competition and Cooperation on European Southeast Air Transport Market

Regional Air Transport Market of Southeast Europe is becoming affected by all modes of increasing competition. This Paper will consider the different levels of achievements in traffic results of air carriers and airports of the Southeast Europe including macroeconomic analyses of 11 countries belonging to this the region. They represent more than 30 carriers and almost 40 airports. The year 2007 and 2008 saw high increase of the passenger and cargo carried within the region. More than 30 million passengers were carried and almost 84,000 tones of freight and mail. On 19 primary airports of the Southeast Europe it was carried more than 29 million passengers which is increase of 33 percent compared with the year 2006. Under above-mentioned increase, it is important to emphasize that the growth of airport handled passenger on new born EU countries Bulgaria and Romania was extremely high. Paper will also analyze categorization of the airports according to EU documents and directives. The future development under the new economic circumstances means also adjustments of business models that have been implemented. It also means necessity to cooperate and find out efficient mode of integration which will follow air traffic and legal framework of the EU. Positive example can be implementation of PSO (Public Service Obligation) for air carriers of the Southeast Europe network. Another challenge is capability to provide and realize privatization process for some of the most successful air carriers. The paper will also consider the air traffic forecasts of the Southeast Europe region, which will follow expected implementation of improved model and benefits for the population and economy of 11 countries.

Air Traffic Forecast Empirical Research Based on the MCMC Method

Airport air traffic is one of the most important and hardest one among all the airport data forecasts. In this paper, the Markov Chain Monte Carlo (MCMC) method of applied theory of statistics has been introduced into the aviation sector, and the discussion on airport air traffic forecast has been conducted taking Shanghai airport as the application background. MCMC method is designed to solve the problem parameters expectations where some baysian inference can not be directly calculated. This paper conducts the iterative computation using WinBUGS, EViews is used to estimate the initial iteration parameter of the regression model, and it is on this basis that the regression equation under the MCMC method is obtained.

Air transport and tourism of Montenegro in terms of global recession

Today's dynamic environment can be characterized as unstable and unpredictable, which significantly affects the development of tourism and the flow of air traffic management which is why the tourism industry should be understood as a process based on continuous research, analysis and understanding of the interactions between the identified changes. Starting from the geographical position and strategic documents on Transport and Tourism, Montenegro is positioned as a air traffic destination whose success in the tourism market is conditioned by the willingness and ability of the holder of the tourist and transport policy pursuant to the challenges of today's approaches to planning and precisely define the development goals and courses of action. Based on the above and due recognition of the economic crisis as the challenges that face our country emphasized the need to analyze the effects of the global economic crisis on the performance of air traffic and future development of tourism in Montenegro. With the intention of the comprehensive observation of the current situation, this paper will present quantitative data from the previous period with the development of air traffic forecasts, organized under the development of Montenegrin tourism. In this way we want the consequences of the global economic crisis displayed as a serious threat to the development of air traffic and tourism in Montenegro, with the ultimate aim of highlighting the importance of establishing high-quality air traffic as part of an integrated approach to tourism development of our country that can achieve a certain degree of resistance to contemporary challenges.

Airport capacity constraints: future avenues for growth of global traffic

Current air traffic forecasts are mainly based on the hypothesis that airports have ample capacity to accommodate future growth, or, if this is not the case, that increasing airport capacity more than marginally is possible within an acceptable time frame. Improving airport capacity more than marginally usually means adding new runways; however, especially in highly developed countries, this may take many years to complete and for some airports, it is even an insurmountable task. This paper gives a brief overview over the historic development of global air traffic and compares it with the more recent developments at specific airports, which are important hubs in the global air traffic network. Thereafter, we develop an index of airport capacity utilisation to assess the capacity reserve of an airport. This index is designed to be applied on a global level and it is based on the concepts of the 5% peak hour and the average hour of an airport which is deduced from so-called “ranking curves”, i.e. operating hours of an airport are ranked according to their traffic volume in descending order. The more this curve is inclined, the greater is the capacity reserve of an airport. This procedure is carried out for around 2,500 airports worldwide on the basis of Official Airline Guide data. The number of flights of an airport serves as a measure for its importance in the global air traffic network. In connection with the airport capacity utilisation index, we are able to categorise airports with regard to the dimensions “level of importance for the global air traffic system” and “level of capacity reserve”, thereby, we are able to identify those airports that are critical for future growth of air traffic, i.e. those airports that serve a large share of the global flight volume and at the same time have only a small capacity reserve.

New empirical evidence on airport capacity utilisation: Relationships between hourly and annual air traffic volumes

An important question in forecasting air traffic at airports is: How does the future traffic volume compare with capacity? We concentrate in this paper on air transport movements (ATMs) and runway capacity, since in many instances runways form the most critical airport component for expansion, due to environmental constraints. Air traffic forecasts like those of aircraft manufacturers typically yield annual volumes, whereas capacities are correctly measured in short time periods, i.e. hours. In answering the question of the conformity of demand (in this case ATMs) with capacity, future annual volumes have to be converted into peak hour volumes and then compared with capacity. The paper discusses the problem of selecting a suitable peak hour and informs on the annual capacity utilisation of airports worldwide in the form of “traffic ranking curves”. Functional relationships between peak hour and annual ATM volumes have been derived for each type of airport capacity.

Forecasting world and regional aviation jet fuel demands to the mid-term (2025)

This article provides jet fuel demand projections at the worldwide level and for eight geographical zones until 2025. Air traffic forecasts are performed using dynamic panel-data econometrics. Then, the conversion of air traffic projections into quantities of jet fuel is accomplished by using a complementary approach to the ‘Traffic Efficiency’ method developed previously by the UK Department of Trade and Industry to support the Intergovernmental Panel on Climate Change (IPCC, 1999). According to our main scenario, air traffic should increase by about 100% between 2008 and 2025 at the world level, corresponding to a yearly average growth rate of 4.7%. World jet fuel demand is expected to increase by about 38% during the same period, corresponding to a yearly average growth rate of 1.9% per year. According to these results, energy efficiency improvements allow reducing the effect of air traffic rise on the increase in jet fuel demand, but do not annihilate it. Jet fuel demand is thus unlikely to diminish unless there is a radical technological shift, or air travel demand is restricted.