Cost savings from trajectory deviations in the European air space

Air transport deregulation has lead to an increase of air traffic, together with a reduction of air fares. Air fare reduction has narrowed operational margins of airlines, bringing financial and employment instability. This has brought airlines to pay increasing attention to flying costs reduction. Two important components of flying costs airlines can try to cut modifying the planned flight are en route charges and operational costs. We rely on Demand Data Repository (DDR2) data to calculate deviations from planned flight trajectories to analyse the extent to what airlines try to cut operational costs making shorter flights than planned if possible, and cut en route charges providing a planned flight with lower en route charges than the planned flight. Our findings show that there is no generalised strategy among airlines to reduce en-route charges asking for deviations of the planned route. On the other hand, airlines are achieving savings of operational costs regularly. Higher savings per nautical mile are obtained in night flights, with longer planned distance and operated by low cost carriers.     

Managing severe airspace flow programs: The Airlines’ side of the problem

This paper presents a heuristic-based approach for minimizing airlines’ schedule disruptions and operation costs associated with severe airspace flow programs. It considers primary decisions made by flight dispatchers such as flight slot substitution and rerouting outside the boundaries of the flow-constrained area. A two-stage heuristic is developed. In the first, a linear approximation of the problem is used to screen inefficient routing and slot substitution alternatives. The second stage examines possible solution improvements through trading flight assignments for every pair of conflicting routes. A genetic algorithm is developed and used to benchmark the performance of the two-stage heuristic. In the algorithm, flight route and slot allocation schemes are modeled as chromosomes. The fitness of these chromosomes measures the magnitude of schedule disruption and overall operating cost. A set of experiments that compare the performance of the two heuristics considering airspace flow programs with different levels of severity is presented.     

Analysis of airport weather impact on on-time performance of arrival flights for the Brazilian domestic air transportation system

Air traffic operations are significantly impacted by weather conditions. These external factors may impose operational constraints and generate demand-capacity imbalances, leading to reduced on-time performance, additional airline costs and inconveniences to passengers. Efficient management of such disruptions requires an understanding of the main causes of flight delays towards increasing their predictability. In this study, we investigate the impacts of airport surface weather conditions on the likelihood of flight delays for the Brazilian domestic air transportation system. We use historical flight schedule, on-time performance and weather data and estimate a logit model to analyze how different meteorological variables at the airport of destination affect the probability of a delayed arrival. We obtain empirical evidence for the impacts of low ceiling and visibility conditions, precipitation and wind gusts on the likelihood of arrival delays for the set of Brazilian airports analyzed.     

Statistical characterization of deviations from planned flight trajectories in air traffic management

Understanding the relation between planned and realized flight trajectories and the determinants of flight deviations is of great importance in air traffic management. In this paper we perform an in-depth investigation of the statistical properties of planned and realized air traffic on the German airspace during a 28 day periods, corresponding to an AIRAC cycle. We find that realized trajectories are on average shorter than planned ones and this effect is stronger during night-time than day-time. Flights are more frequently deviated close to the departure airport and at a relatively large angle-to-destination. Moreover, the probability of a deviation is higher in low traffic phases. All these evidences indicate that deviations are mostly used by controllers to give directs to flights when traffic conditions allow it. Finally we introduce a new metric, termed di-fork, which is able to characterize navigation points according to the likelihood that a deviation occurs there. Di-fork allows to identify in a statistically rigorous way navigation point pairs where deviations are more (less) frequent than expected under a null hypothesis of randomness that takes into account the heterogeneity of the navigation points. Such pairs can therefore be seen as sources of flexibility (stability) of controllers traffic management while conjugating safety and efficiency.     

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.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

CO2 emissions associated with hubbing activities in air transport: an international comparison

Hubbing is an important operational practice in air transport. Many studies have been conducted to examine the benefits and impacts of hubbing from an economic perspective. However, its impact on CO₂ emissions, especially across different air spaces, is not well understood. This paper explores the impact of hubbing activities in air transport from an environmental perspective. With a detailed methodology and data from the Greek and Hong Kong/Sanya flight information regions (FIRs), three levels of CO₂ emissions are estimated: airport-based, airspace-based and flight-based. After contrasting the CO₂ emission efficiencies of Athens International Airport (AIA) and the Hong Kong International Airport (HKIA), aircraft type and flight distance are examined to explain their emission efficiency differences. It is found that HKIA is associated with poorer CO₂ emission efficiency at the airport and airspace levels because of the larger aircraft and longer flight distance. However, when CO₂ emission efficiency at the flight level is considered, HKIA, with a higher passenger load factor, performs better. Major international hub airports should implement additional environmental measures to minimize the impact of hubbing activities on CO₂ emissions at the airport and airspace levels.     

An empirically grounded agent based simulator for the air traffic management in the SESAR scenario

In this paper we present a simulator allowing to perform policy experiments relative to the air traffic management. Different SESAR solutions can be implemented in the model to see the reaction of the different stakeholders as well as other relevant metrics (delay, safety, etc). The model describes both the strategic phase associated to the planning of the flight trajectories and the tactical modifications occurring in the en-route phase. An implementation of the model is available as an open-source software and is freely accessible by any user.More specifically, different procedures related to business trajectories and free-routing are tested and we illustrate the capabilities of the model on an airspace which implements these concepts. After performing numerical simulations with the model, we show that in a free-routing scenario the controllers perform less operations but the conflicts are dispersed over a larger portion of the airspace. This can potentially increase the complexity of conflict detection and resolution for controllers.In order to investigate this specific aspect, we consider some metrics used to measure traffic complexity. We first show that in non-free-routing situations our simulator deals with complexity in a way similar to what humans would do. This allows us to be confident that the results of our numerical simulations relative to the free-routing can reasonably forecast how human controllers would behave in this new situation. Specifically, our numerical simulations show that most of the complexity metrics decrease with free-routing, while the few metrics which increase are all linked to the flight level changes. This is a non-trivial result since intuitively the complexity should increase with free-routing because of problematic geometries and more dispersed conflicts over the airspace.     

A method to estimate air traffic controller mental workload based on traffic clearances

Workload estimation is a complex domain which has been investigated extensively over the years. Past estimation techniques have focused on measuring workload directly from the air traffic controllers (ATCOs) or inferring it from traffic factors. The limitations of these techniques are interfering into the ATCO job and not being able to capture the differences amongst individual ATCOs respectively. This paper presents a novel technique overcoming these limitations, able to accurately estimate the workload experienced by the ATCO based exclusively on the clearances provided to air traffic. The technique, which was calibrated for the EUROCONTROL Maastricht Upper Area Control (MUAC) Centre, thereby has the potential to more accurately estimate actual airspace capacity. It is independent of the level of system automation and therefore applicable not only with the current ATM system, but also in the anticipated future highly automated environments as well as during the transition period. The paper discusses potential applications such as real time monitoring of operational workload and post-operations identification of sector workload imbalances. Both can contribute towards enhancing the performance of the ATM system.     

Time efficiency assessment of ship movements in maritime ports: A case study of two ports based on AIS data

Operational efficiency of a maritime port is an important issue for shipping lines and port authorities. It is desirable to move ships in and out of a maritime port as efficiently as possible. Automatic identification system (AIS) data recording the trajectory of ship movements allow us to assess the operational efficiency as ships move in and out of a port. This study proposes a time efficiency assessment framework that evaluates the amount of time each ship spends in the different areas within a port (i.e., berth, anchorage, and fairway) based on the space-time trajectories of ship movements derived from AIS data. According to the statistical distributions of time spent by different types of ship in each port area, the proposed framework can compare time efficiency across different zones within a port and between different ports. This study uses AIS data of four types of ships (i.e., container, cargo, tanker, and passenger ships) at two selected ports in China, Shanghai Yangshan Port and Xiamen Port, to demonstrate how the proposed approach can effectively assess and compare time efficiency levels of ship movements between the times entering and leaving the vessel traffic service (VTS) lines of a port. This study demonstrates the value of deriving space-time trajectories from AIS data based on the concepts of time geography to assess time efficiency levels of maritime ports and monitor their performance over time, which offer useful information to both shipping lines and port authorities for operations such as efficient scheduling and logistic support.     

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.     

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     

Air-traffic complexity resolution in multi-sector planning

This paper considers the problem of minimizing the traffic complexities in an airspace of adjacent sectors. The traffic complexity of a sector is determined by the numbers of flights within it, near its border, and on non-level segments within it. The dimensions of complexity resolution involve changing the take-off times of non-airborne flights, changing the approach times into the chosen airspace of airborne flights by slowing and accelerating within the two layers of feeder sectors around that airspace, as well as changing the altitude at way-points in that airspace. Experiments with European flight profiles from the Central Flow Management Unit show that these forms of resolution can lead to significant complexity reductions and rebalancing.     

Analyzing the effect of aviation infrastructure over aviation fuel consumption reduction

The purpose of this paper is to examine the effect of various aviation infrastructure dimensions over aviation fuel consumption reduction (AFCR) performance. This study is an effort that considers the role of dimensions collectively from all aspects belonging to aviation infrastructure. The relevance of dimensions and constructs for hypothesis development are based on extensive literature review. Exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed in the consecutive purification processes. Also, hypothesis testing was conducted using Structural Equation Modeling (SEM). A customized questionnaire was developed for collecting data from both kinds of respondents: Aviation industry experts and academic experts. Out of 382 approaches through mail survey, a total of 194 valid responses were collected. Analysis of the results shows the positive and significant impact of various factors such as: airport design, airspace management and air traffic control over the aviation fuel consumption reduction. Maximum importance is adjudged on air traffic control (ATC) and airspace route flexibility. The results of this study will encourage airlines and airport development authorities to increase their insight over aviation infrastructure, also to perform deeper analysis and find out precise values for real life implications.     

Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT.     

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.     

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 multi-criteria analysis framework including environmental and health impacts for evaluating traffic calming measures at the road network level

Recent interests in both vehicle emissions and public health have facilitated the development of more eco-friendly transportation systems. This study developed a multi-criteria evaluation framework to evaluate the effectiveness of traffic calming measures (TCMs) from the various perspectives at the road network level; operational efficiency, traffic safety, environmental and health impacts. The proposed methodology employs four-step sequential simulation experiments, including driving, traffic flow, emissions, and air dispersion simulations. The results obtained from these four simulations are used to evaluate the effectiveness in terms of safety and operational efficiency in addition to environmental and health impacts. A multi-criteria value function based on the weights estimated from the analysis of an analytical hierarchy process (AHP) is applied in the evaluation framework. As an application, chicanes and speed humps widely implemented in Korean school zones were evaluated at the road network level. The proposed simulation-based approach is expected to be effectively used for the decision-making process in selecting better alternatives for TCM.     

Characterizing the performance of queuing networks in terminal control systems

The terminal infrastructure is critical for the effective and efficient operation of air traffic flow. The increase in air traffic flow creates pressure on air transport infrastructures and networks. Therefore, decision-makers need to develop and utilize efficient evaluation and analysis tools based on the limited availability of resources. Inspired by the underlying structure of queues for air traffic flow, a Petri-net-based model is developed in this study to characterize the system performance. In this study, air transportation networks are analyzed using a series of performance indicators that are based on a discrete-event system model.     

Mitigation of airspace congestion impact on airline networks

In recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD.