Characterizing the Brazilian airspace structure and air traffic performance via trajectory data analytics

This paper presents a data-driven approach for multi-scale characterization of the Brazilian airspace structure and air traffic operational performance from aircraft tracking data recorded by surveillance systems. Unsupervised learning is performed with a flight trajectory clustering analysis to automatically identify spatial traffic patterns in both the terminal and the en route airspace for major origin-destination pairs of the Brazilian air transportation system. Based on the as-flown route structure learned, quantitative metrics are developed to describe the structural efficiency of the airspace and the operational efficiency of the traffic flows. For this, actual flight trajectories are projected onto reference nominal trajectories in space and time. The results allowed for cross-route comparisons of air traffic flow efficiency across multiple flight phases as well as for the identification of causal factors for trajectory deviations from nominal routes. An interactive data analytics tool is also created to output performance statistics and air traffic visualizations. With the provision of a systematic data-driven approach for characterizing actual air traffic operations, the analytics framework is envisioned to assist airspace design and performance monitoring processes and to provide the basis for developing predictive capabilities in support of traffic flow management.     

The impact of airport capacity constraints on future growth in the US air transportation system

This paper simulates airline strategic decision making and its impact on passenger demand, flight delays and aircraft emissions. Passenger flows, aircraft operations, flight delays and aircraft emissions are simulated for 22 airports in the US, under three airport capacity scenarios. The simulation results indicate that most system-wide implications for operations and environmental impact seem to be manageable, but local impacts at congested hub airports may be significant. The response of the air transportation system to avoid airports with high delays could significantly impact passenger demand and air traffic for these and directly dependent airports. The simulations also suggest that frequency competition effects could maintain flight frequencies at high levels, preventing a significant shift toward larger aircraft, which would otherwise reduce the impact of the capacity constraints.     

Analyzing tactical control strategies for aircraft arrivals at an airport using a queuing model

This paper proposes to analyze control strategies for arrival air traffic at an airport using a classical queuing model. The parameters of our model are estimated by means of a data-driven analysis of two years of radar tracks and flight plans for arrival flights at Tokyo International Airport from 2016 to 2017. Our results show that increasing the capacity with one or two more aircraft in the airspace up to 60 NM around the airport significantly mitigates arrival delays, even when assuming future, increased arrival traffic volumes. The outcomes of this study provide insights into the effectiveness of arrival control strategies and are seen as a means to recommend scenarios to be further analyzed with human-in-the-loop simulations.     

A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling,with consideration of fleet assignment problem

One of the most important airline's products is to determine the aircraft routing and scheduling and fleet assignment. The key input data of this problem is the traffic forecasting and allocation that forecasts traffic on each flight leg. The complexity of this problem is to define the connecting flights when passengers should change the aircraft to reach the final destination. Moreover, as there exists various types of uncertainties during the flights, finding a solution which is able to absorb these uncertainties is invaluable. In this paper, a new robust mixed integer mathematical model for the integrated aircraft routing and scheduling, with consideration of fleet assignment problem is proposed. Then to find good solutions for large-scale problems in a rational amount of time, a heuristic algorithm based on the Simulated Annealing (SA) is introduced. In addition, some examples are randomly generated and the proposed heuristic algorithm is validated by comparing the results with the optimum solutions. The effects of robust vs non-robust solutions are examined, and finally, a hybrid algorithm is generated which results in more effective solution in comparison with SA, and Particle Swarm Optimization (PSO).     

Control-based optimization approach for aircraft scheduling in a terminal area with alternative arrival routes

This paper presents an optimization approach for dynamically scheduling aircraft operations and supporting air traffic controllers in both determining and implementing operationally feasible landing and departure times at an airport. The mixed integer linear programming model proposed incorporates air traffic control infrastructure in terms of route network, introduces the concept of alternative approach routes and is designed to generate an output that can be converted into effective advisories for executable flight commands. It shows reasonable computational times for obtaining the optimal solution and delay reductions of up to 35% with practical size instances from Sao Paulo/Guarulhos International Airport.     

Using causal machine learning for predicting the risk of flight delays in air transportation

Delays in air transportation are a major concern that has negative impacts on the airline industry and the economy. Given the complexity of the National Air Space system, predicting the risk of flight delays and identifying significant predictors is vital to risk mitigation. The purpose of this paper is to perform data mining using causal machine learning algorithms in the USELEI process (Understanding, Sampling, Exploring, Learning, Evaluating, and Inferring) to predict the probability of flight delays in air transportation using data collected from different sources. The findings indicated significant effects of predictors, including reported arrivals and departures, arrival and departure demands, capacity, efficiency, and traffic volume at the origin and destination airports on the risk of flight delays. More importantly, causal interrelationships among variables in a fully structural network are presented to how these predictors interact with one another and how these interactions lead to delay incidents. Finally, sensitivity analysis and causal inference can be performed to evaluate various what-if scenarios and form effective strategies to mitigate the risk of delays.     

Air freight hubs in the FedEx system: Analysis of fuel use

This paper provides a data based analysis of FedEx air freighter activities from selected hub locations. The basic idea is that air freighters have a set of range and payload parameters and their corresponding fuel burn depends on weight and distance. Data from 2011 to 12 (FlightAware) are used for 180,000 + flights on origin, destination and aircraft type. The particular aircraft vary widely in payload, but additional parameters may be derived from industry web sites and BTS. The research uses flight activity at hubs such as Memphis and Indianapolis (among others) and computes the aggregate distance flown on specific aircraft. The linkage between the hub and aggregate fuel use (assuming that the out bound flights are allocated to the hub) will give some quantifiable measures of the costs allocated to the hub. The paper examines particular aspects of the air freight system that are especially vulnerable to a spike in the costs of aviation fuel. These observations suggest that traffic to regional air express and air freight hubs is likely to respond in complex ways to fuel costs.     

Air express network design based on express path choices – Chinese case study

Due to the shortage of cargo aircraft, Chinese express companies have to provide transportation services with a hybrid method that consists of “rented bellies and self-owned cargo aircraft”. Because the bellies are rented from air passenger companies and because the cargo aircraft are owned by the express companies, it is important for the express companies to use their cargo aircraft as effectively as possible. This paper constructs a bi-level model to optimize the flight transportation network of an express company. The upper model designs the network and allocates the transportation capacity with the objective of minimizing the total transportation cost, and the lower model calculates the link flows in user equilibrium. Data from ShunFeng (SF) Express Company (China) are used to conduct the case study. Using the model outputs, the flows on links and at nodes are analyzed to illustrate the transportation and transshipment situations.     

Automated data-driven prediction on aircraft Estimated Time of Arrival

4D trajectory prediction is the core element of the future air transportation system. It aims to improve the operational ability and the predictability of air traffic. In this paper, a novel automated data-driven framework to deal with the prediction of Estimated Time of Arrival (ETA) on the runway at the entry point of Terminal Manoeuvring Area (TMA) is introduced. The proposed framework mainly consists of data preprocessing and machine learning models. Firstly, the dataset is divided, analyzed, cleaned, and estimated. Then, the flights are clustered into partitions according to different runway-in-use (QFU). Several candidate machine learning models are trained and selected on the corresponding dataset of each QFU. Feature engineering is conducted to transform raw data into features. After that, the experiments are performed on real ADS-B data in Beijing TMA with nested cross validation. By comparing the prediction performance on the preprocessed and un-preprocessed datasets, the results demonstrate that the proposed data preprocessing is able to improve the data quality. It is also robust to outliers, missing data, and noise. Finally, an ensemble learning strategy named stacking is introduced. Compared to other individual models, the stacked model has a more complex structure and performs best in ETA prediction. This fact reveals that the framework proposed in this study could make accurate and reliable ETA predictions.     

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.     

Methods of air traffic management in the airport area including the environmental factor

In the modern approach to air traffic management (ATM), issues related to air quality and climate protection have led to the introduction of a growing number of new restrictions instead of having these issues be the objectives of actions taken. The aim of the study was to investigate whether it is possible to improve air quality while controlling pollution emission by introducing changes in the organization of air traffic control at the airport. For this purpose, mathematical models of emissions were created. These concern the movement of aircraft near the airport and emission of carbon dioxide and the spread of human health related substances emitted from a landing airplane's engine. The first case refers to the possibility of using different arrival procedures. The second case was to adapt the Pasquill formula by treating landing aircraft as a sequence of point-source emissions. As a result of applying the designed models and software tools, it was proven that both a change in the arrival trajectory and a change of the runway can contribute to a reduction of CO₂ emission into the atmosphere. The emission maps created for different aircraft approach profiles show the ability to control the local concentration of harmful emissions. The study clearly shows that the current approach of maximizing throughput is not beneficial to air quality. ATM services should consider the use of other variants of air traffic organization, particularly during periods of reduced traffic.     

Airline fuel usage and carbon emissions: Determining factors

Using annual data on individual US airlines over the 1995–2015 period, this paper presents regression results relating an airline's total fuel usage to seven variables: the available ton miles of capacity (passengers plus freight and mail) provided by the airline; the average seat capacity of its aircraft, average stage length (flight distance); average load factor (measured by weight); the average vintage (construction year) of its aircraft; the percentage of the airline's flights that are delayed; and the average annual fuel price. The results show how fuel usage and carbon emissions depend on a small set of crucial variables. The estimated fuel-price effect allows the emissions impact of an optimal emissions charge to be computed, and the estimated delay effect shows the emissions impact of an industry-wide reduction in flight delays. The regression model is generated from a theoretical framework.     

A systematic approach to assess the effectiveness of airport noise mitigation strategies

Regional airports are often located very close to the urban area they serve and the increasing traffic rate that many of them have experienced in the last years has produced several impacts on the communities living close to the airport area, mainly aviation noise. If not properly managed, noise impacts produced by airport operations can cut down significantly the development of airport air traffic with direct effects on the economic and territorial systems. Aeronautical noise has greatly reduced in the last decade, due to aircraft design technological improvements and more severe regulations. However, the noise reduction during a single event does not make the issue of the airport location – and then the whole noise impact – less significant. This paper proposes an assessment process to evaluate the effects of actions adopted to reduce airport noise impacts on populated areas. Both airport-related factors – such as number of take-off; day-evening-night distributions of movements; aircraft type; flying paths – and land-use characteristics have been considered and combined in a density index that synthesizes the impacts of airport noise on the territory. The assessment process has been tested on a real case, the airport of Bologna in Northern Italy. The predicted results, compared with available real data for the test case, are significant and encourage the use of the proposed assessment process as decision support system for the airport management.     

A global model for estimating the block time of commercial passenger aircraft

This paper uses a two-stage statistical model to estimate the block time of commercial passenger aircraft. The model considers many of the factors contributing to airport congestion and provides a basis for future development of multivariate statistical models of the flight operation process. The model is tested using 2 million US domestic flights by six airlines in 2004. Model analysis provided insight regarding the relative impact of weather conditions and airport utilization on block time. In particular, population, arrival time, airport utilization, ice, and the interaction of poor weather conditions and traffic were found to be significant predictors of block time.     

An AHP analysis of air traffic management with target windows

The main operational concept of Single European Sky ATM Research Programme is the notion of business trajectory. One possible implementation is based on the notion of a contract of objectives; an agreement among the main air traffic management actors on spatial and temporal intervals called target windows. These 4D windows are defined prior to flight departure by the airlines, airports and air navigation service providers to increase punctuality. We use an analytic hierarchy process to assess the opportunity of implementing this concept by considering the views of experts. The findings indicate that there are net benefits for airlines and air navigation service providers but not for airports     

Analysis of the recent evolution of commercial air traffic CO2 emissions and fleet utilization in the six largest national markets of the European Union

This paper presents the results of a study performed to analyze the evolution of commercial air traffic and CO₂ emissions in the European Union, from 2010 to 2013. Data sources are the European Commission's Eurostat Air Transport Statistics (Eurostat) and EUROCONTROL flight plans database. The changes in the fuel efficiency are analyzed and the potential reasons for those changes investigated. The evolution in the airline fleet composition during the last decade is presented as one of the reasons for the improvement in fuel efficiency, measured in burnt fuel per total Revenue Tonne Kilometre (RTK), as well as the different parameters depending on the airline business model (network carriers, low cost companies, etc.) and the aircraft type.Results show a slight reduction in the traffic, both for passengers and cargo (about −0.8%), and a more important reduction in CO₂ emissions (−4.3%), thanks to an improvement in the fuel efficiency parameter (−3.5%) for the three years period. There has been a relevant change in the fleet composition in the last ten years, with the replacement of older models for more efficient ones, and a shift to larger aircraft, particularly in the regional segment. Traffic has decreased in shorter distances (internal EU traffic), but increased in more efficient long range flights (extra-EU traffic), resulting also in an improvement of the efficiency parameter as average aircraft size and stage length increases.     

Where does flygskam come from? The role of citizens’ lack of knowledge of the environmental impact of air transport in explaining the development of flight shame

A growing number of citizens are concerned about the environmental impact of air transport, and aviation has become synonymous with high carbon emissions and global warming, which has led to the development of flygskam (or flight shame) in Europe. While its impact on air traffic remains unclear, flight shame has forced the airline industry to react and better understand its origin. In this research, building on the growing literature on industry and organizational stigma, we assume that flight shame can be partly explained by a distorted public perception of the environmental impact of air transport. Accordingly, we investigate the level of knowledge of the environmental footprint of air transport. Based on a sample of 1018 French respondents, we reveal that more than 90% of respondents overestimate the share of air transport in global carbon emissions. We also show that 98% of the respondents underestimate the reduction in carbon emissions per passenger. Finally, we investigate the awareness of the measures taken by the industry to curb its carbon emissions and highlight, for instance, that 70% of respondents overestimate the fuel consumption of the newest generations of aircraft. Based on these results, we draw lessons for airlines and for the air transport industry to help cope with flight shame in Europe.     

Modeling resilience of the ATC (Air Traffic Control) sectors

This paper develops a theoretical framework containing the methodology for assessing resilience of the ATC (Air Traffic Control) sectors affected by the impact of a given disruptive event. The resilience is considered as ability of these sectors to retain a certain level of the regular/nominal performance during the impact and fully recover relatively fast afterwards. The actually rear disruptive event is considered to be the large-scale failure of a component of the ATC facilities and equipment supporting safe, efficient, and effective air traffic. Under such conditions, different mitigating contingency measures are generally applied resulting in deteriorating the operational, economic, and environmental performance of the affected sectors while maintaining the required level of safety. This performance is represented by the indicators such as demand, capacity, traffic complexity, the ATC controller workload, aircraft/flight delays and their costs, and additional fuel consumption and related emissions of GHG (Green House Gases). The proposed methodology consists of the generic model of resilience, the analytical models for estimating the indictors of ATC sectors’ performance, and the analytical models of resilience based the indicators as figures-of-merit for assessing resilience. These models are based on the practice-close mitigating contingency measures applied to the ATC sectors affected by a given disruptive event. The possible application of the proposed methodology is also elaborated.     

A heuristic approach for solving an integrated gate reassignment and taxi scheduling problem

Capacity limitation of airport ground operation is one of the major limiting factors in air traffic operation. The congestion on the gate and taxiway causes severe delay and propagate effect on the flight schedule. This paper considers the problem of integrated gate reassignment and taxiway scheduling, in which complex constraints related to runway restriction, gate allocation and taxiway conflict are all incorporated when determining the schedule. To solve this problem, we propose a novel heuristic approach. First, all possible aircraft schedules are enumerated by disretizing the waiting time along the path. Then, the cost is evaluated for each schedule and the conflict detection is conducted to generate constraint sets. Finally, we propose a set partition model, in which each decision variable denotes a candidate schedule that takes into account the possible constraints when generated. This method is compared with a sequential method that solves gate reassignment and taxiway scheduling problem separately. Computational results highlight the strength of our method.     

Coordinated aviation network resource allocation under uncertainty

Congestion in the air traffic system, both recurrent and non-recurrent, is typically handled by rationing access rights to individual resources such as airports or important parts of the airspace. Under the planning paradigm employed in the US, this rationing process occurs independently at each resource. The stochastic integer programming model proposed in this paper brings coordination to this process while considering capacity uncertainty. Results of a case study suggest that the model is tractable, and generates capacity allocations that improve efficiency and enable greater responsiveness to changing capacity conditions.