Aircraft trajectory forecasting using local functional regression in Sobolev space

This paper considers the problem of short to mid-term aircraft trajectory prediction, that is, the estimation of where an aircraft will be located over a 10–30 min time horizon. Such a problem is central in decision support tools, especially in conflict detection and resolution algorithms. It also appears when an air traffic controller observes traffic on the radar screen and tries to identify convergent aircraft, which may be in conflict in the near future. An innovative approach for aircraft trajectory prediction is presented in this paper. This approach is based on local linear functional regression that considers data preprocessing, localizing and solving linear regression using wavelet decomposition. This algorithm takes into account only past radar tracks, and does not use any physical or aeronautical parameters. This approach has been successfully applied to aircraft trajectories between several airports on the data set that is one year air traffic over France. The method is intrinsic and independent from airspace structure.     

ATCEM: a synthetic model for evaluating air traffic complexity

Air traffic complexity, which measures the disorder of air traffic distribution, has become the critical indicator to reflect air traffic controller workload in air traffic management (ATM) system. However, it is hard to assess the system accurately because there are too many correlated factors, which make the air traffic complexity nonlinear. This paper presents an air traffic complexity evaluation model with integrated classification using computational intelligence (ATCEM). To avoid redundant factors, critical factors contributing to complexity are analyzed and selected from numerous factors in the ATCEM. Subsequently, to construct the mapping relationship between selected factors and air traffic complexity, an integrated classifier is built in ATCEM. With efficient training and learning based on aviation domain knowledge, the integrated classifier can effectively and stably reflect the mapping relationship between selected factors and the category of air traffic complexity to ensure the precision of the evaluation. Empirical studies using real data of the southwest airspace of China show that the ATCEM outperforms a number of state‐of‐the‐art models. Moreover, using the critical complexity factors selected in ATCEM, the air traffic complexity could be effectively estimated. Copyright © 2015 John Wiley & Sons, Ltd.     

Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty

Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs).     

Research review of air traffic management

As air transport demand keeps growing more quickly than system capacity, efficient and effective management of system capacity becomes essential to the operation of the future global air traffic system. Although research in the past two decades has made significant progress in relevant research fields, e.g. air traffic flow management and airport capacity modelling, research loopholes in air traffic management still exist and links between different research areas are required to enhance the system performance of air traffic management. Hence, the objective of this paper is to review systematically current research in the literature about the issue of air traffic management to prioritize productive research areas. Papers about air traffic management are discussed and categorized into two levels: system and airport. The system level of air transport research includes two main topics: air traffic flow management and airspace research. On the airport level, research topics are: airport capacity, airport facility utilization, aircraft operations in the airport terminal manoeuvring area as well as aircraft ground operations research. Potential research interests to focus on in the future are the integration between airspace capacity and airport capacity, the establishment of airport information systems to use airport capacity better, and the improvement in flight schedule planning to improve the reliability of schedule implementation.     

Evaluating locational accessibility to the US air transportation system

Although there are hundreds of airports that support commercial air passenger traffic in the United States (US), not all areas are equivalently served by the commercial air transportation system. Locations in the US differ with respect to their level of access to the commercial air network and their overall accessibility within the system. Given the complexity of the domestic commercial air passenger network and supporting infrastructure, past research has only been able to provide a limited assessment of locational accessibility within the United States. To address these complexities, this paper proposes a new metric that incorporates measures of access to air transport as well as accessibility within air transportation networks. Using a comprehensive dataset on scheduled airline service, the developed approach is then applied to the US domestic commercial passenger air transportation network to explore geographic differentials in accessibility. Results suggest marked differences between core-based statistical areas throughout the US.     

A multi-objective approach for Dynamic Airspace Sectorization using agent based and geometric models

A key limitation when accommodating the continuing air traffic growth is the fixed airspace structure including sector boundaries. The geometry of sectors has stayed relatively constant despite the fact that route structures and demand have changed dramatically over the past decade. Dynamic Airspace Sectorization is a concept where the airspace is redesigned dynamically to accommodate changing traffic demands. Various methods have been proposed to dynamically partition the airspace to accommodate the traffic growth while satisfying other sector constraints and efficiency metrics. However, these approaches suffer from several operational drawbacks, and their computational complexity increases fast as the airspace size and traffic volume increase. In this paper, we evaluate and identify the gaps in existing 3D sectorization methods, and propose an improved Agent Based Model (iABM) to address these gaps. We also propose three additional models using KD-Tree, Bisection and Voronoi Diagrams in 3D, to partition the airspace to satisfy the convexity constraint and reduce computational cost. We then augment these methods with a multi-objective optimization approach that uses four objectives: minimizing the variance of controller workload across the sectors, maximizing the average sector flight time, and minimizing the distance between sector boundaries and the traffic flow crossing points. Experimental results show that iABM has the best performance on workload balancing, but it is restrictive when it comes to the convexity constraint. Bisection- and Voronoi Diagram-based models perform worse than iABM on workload balancing but better on average sector flight time, and they can satisfy the convexity constraint. The KD-tree-based model has a lower computational cost, but with a poor performance on the given objectives.     

Dynamic airspace sectorisation for flight-centric operations

Today’s air traffic operations follow the paradigm of ‘flow follows structure’, which already limits the operational efficiency and punctuality of current air traffic movements. Therefore, we introduce the dynamic airspace sectorisation and consequently change this paradigm to the more appropriate approach of ‘structure follows flow’. The dynamic airspace sectorisation allows an efficient allocation of scarce resources considering operational, economic and ecological constraints in both nominal and variable air traffic conditions. Our approach clusters traffic patterns and uses evolutionary algorithms for optimisation of the airspace, focusing on high capacity utilisation through flexible use of airspace, appropriate distribution of task load for air traffic controllers and fast adaptation to changed operational constraints. We thereby offer a solution for handling non-convex airspace boundaries and provide a proof of concept using current operational airspace structures and enabling a flight-centric air traffic management. We are confident that our developed dynamic airspace sectorisation significantly contributes to the challenges of future airspace by providing appropriate structures for future 4D aircraft trajectories taking into account various operational aspects of air traffic such as temporally restricted areas, limited capacities, zones of convective weather or urban air mobility. Dynamic sectorisation is a key enabling technology in the achievement of the ambitious goals of Single European Sky and Flightpath 2050 through a reduction in coordination efforts, efficient resource allocation, reduced aircraft emissions, fewer detours, and minimisation of air traffic delays.     

A knowledge-transfer-based learning framework for airspace operation complexity evaluation

A sector is a component airspace whose operation is allocated to an air traffic controller. The operation complexity of a sector plays a critical role in the current Air Traffic Management system, e.g. it determines the workload volume of air traffic controllers and serves as a reliable index for airspace configuration and traffic flow management. Therefore, accurately evaluating the sector operation complexity is a problem of paramount importance in both practice and research. Due to numerous interacting factors, traditional methods based on only one single complexity indicator fail to accurately reflect the true complexity, especially when these factors are nonlinearly correlated. In light of these, the attempt to use machine learning models to mine the complex factor-complexity relationship has prevailed recently. The performance of these models however relies heavily on sufficient samples. The high cost of collecting ample data often results in a small training set, adversely impacting on the performance that these machine learning models can achieve. To overcome this problem, this paper for the first time proposes a new sector operation complexity evaluation framework based on knowledge transfer specifically for small-training-sample environment. The proposed framework is able to effectively mine knowledge hidden within the samples of the target sector, i.e. the sector undergoes evaluation, as well as other sectors, i.e. non-target sectors. Moreover, the framework can properly handle the integration between the knowledge derived from different sectors. Extensive experiments on real data of 6 sectors in China illustrate that our proposed framework can achieve promising performance on complexity evaluation when only a small training set of the target sector is available.     

Surrounding traffic complexity analysis for efficient and stable conflict resolution

The constant increase in air traffic demand increases a probability of the separation minima infringements in certain areas as a consequence of increased traffic density. The Annual Safety Report 2016 reports that in recent years the number of infringements, measured per million flight hours, had been increased at a lower rate (Eurocontrol, 2018). However, this level of infringements still generates a continuous pressure on the air traffic control (ATC) system and seeks for more control resources ready to tactically solve potential conflicts, while increasing at the same time the operational costs. Considering present air traffic management (ATM) trade-off criteria: increased airspace capacity and traffic efficiency but reducing the cost while preserving safety, new services must be designed to distribute the separation management ATC task loads among other actors. Based on the Single European Sky Air Traffic Management Research and Next Generation Air Transportation System initiatives, this paper proposes an innovative separation management service to shift the completely centralized tactical ATC interventions to more efficient decentralized tactical operations relying on an advanced surrounding traffic analysis tool, to preserve the safety indicators while considering the operational efficiency. A developed methodology for the proposed service is an application-oriented, trying to respond to characteristics and requirements of the current operational environment. The paper further analysis the traffic complexity taking into consideration the so-called domino effect, i.e. a number of the surrounding aircraft causally involved in the separation management service by the means of identification of the spatiotemporal interdependencies between them and the conflicting aircraft. This complexity is driven by the interdependencies structure and expressed as a time-criticality in quantifying the total number of the system solutions, that varies over time as the aircraft are approaching to each other. The results from two randomly selected ecosystem scenarios, extracted from a simulated traffic, illustrate different avoidance capacities for a given look-ahead time and the system solutions counts, that in discrete moments reach zero value.     

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

This paper presents a dynamic network‐based approach for short‐term air traffic flow prediction in en route airspace. A dynamic network characterizing both the topological structure of airspace and the dynamics of air traffic flow is developed, based on which the continuity equation in fluid mechanics is adopted to describe the continuous behaviour of the en route traffic. Building on the network‐based continuity equation, the space division concept in cell transmission model is introduced to discretize the proposed model both in space and time. The model parameters are sequentially updated based on the statistical properties of the recent radar data and the new predicting results. The proposed method is applied to a real data set from Shanghai Area Control Center for the short‐term air traffic flow prediction both at flight path and en route sector level. The analysis of the case study shows that the developed method can characterize well the dynamics of the en route traffic flow, thereby providing satisfactory prediction results with appropriate uncertainty limits. The mean relative prediction errors are less than 0.10 and 0.14, and the absolute errors fall in the range of 0 to 1 and 0 to 3 in more than 95% time intervals respectively, for the flight path and en route sector level. Copyright © 2017 John Wiley & Sons, Ltd.     

Assessing the effect of urban socioeconomic factors and the financial crisis of 2008 on domestic air cargo traffic in Florida

In this paper, we investigate the relationship between the social and economic attributes of metropolitan areas and their corresponding domestic cargo traffic. We considered a period of 14 years (2003–2016) and studied the impact of population demographics, employment, and regional industries on domestic cargo traffic of a sampled set of metropolitan areas in Florida. We considered all-cargo carriers and mixed passenger–cargo carriers. Our results provide empirical insights into factors determining the air cargo traffic in Florida. Both population and age demographics of a region is shown to be influential on cargo traffic. Manufacturing industries are shown to prefer all-cargo carriers to mixed passenger–cargo carriers and their concentration in a metro area results in an increase of cargo traffic. In contrast, service industries generate low demand for air cargo. Our results show that larger airports tend to attract cargo traffic away from smaller airports in their close proximity. We also provide insights into the impact of the financial crisis of 2008 on domestic cargo traffic in the region. We study the recovery trend and the impact of the high fuel jet prices on slowing down this trend.

     

Demand for rail travel to and from airports

Rail access to airports is becoming increasingly important for both train operators and the airports themselves. This paper reports analysis of inter-urban rail demand to and from Manchester and Stansted Airports and the sensitivity of this market segment to growth in air traffic and the cost and service quality of rail services. The estimated demand parameters vary in an expected manner between outward and inward air travellers as well as between airport users and general rail travellers. These parameters can be entered into the demand forecasting framework widely used in the rail industry in Great Britain to provide an appropriate means of forecasting for this otherwise neglected market segment. The novel features of this research, at least in the British context, are that it provides the first detailed analysis of aggregate rail flows to and from airports, it has disaggregated the traditional generalised time measure of rail service quality in order to estimate separate elasticities to journey time, service headway and interchange, and it has successfully explored departures from the conventional constant elasticity position.     

Determinants of air cargo traffic in California

Studies on the economic impacts of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised pressing questions about the determinants of air cargo traffic. Specifically, it would be useful to know how California’s air cargo traffic is affected by urban economic characteristics. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003–2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentrations of both service and manufacturing employment impact the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for traffic diversion between cities, showing that 80% of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010–2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9% (4.4%) per year.     

Air traffic assignment to reduce population noise exposure using activity-based approach

Aircraft noise has been regarded as one of the major environmental issues related to air transport. Many airports have introduced a variety of measures to reduce its impact. Several air traffic assignment strategies have been proposed in order to allocate noise more wisely. Even though each decision regarding the assignment of aircraft to routes should consider population exposure to noise, none of the air traffic assignment strategies has addressed daily migrations of population and number of people exposed to noise. The aim of this research is to develop a mathematical model and a heuristic algorithm that could assign aircraft to departure and arrival routes so that number of people exposed to noise is as low as possible, taking into account temporal and spatial variations in population in an airport’s vicinity. The approach was demonstrated on Belgrade airport to show the benefits of the proposed model. Numerical example showed that population exposure to noise could be reduced significantly by applying the proposed air traffic assignment model. As a consequence of the proposed air traffic assignment, overall fuel consumption increased by less than 1%.     

Collision course? The North Airfield Safety Study at Los Angeles International Airport (LAX)

The LAX North Airfield Safety Study was undertaken by an Academic Panel consisting of the present authors, and was based in large part on a simulation that was conducted at FutureFlight Central at NASA Ames Research Center. The primary aim of the study was “to estimate as specifically as possible the level of future safety associated with several geometrical configurations of the LAX North Airfield.” This paper describes the study, and how it combined information from human-in-the-loop simulations at NASA with historical data from LAX and other US airports about runway incursions and collisions. The analysis indicated that, even under its existing physical layout, LAX North would experience very low risk of runway collisions at traffic levels projected for 2020. That risk could be reduced by about half if the North Airfield runways were reconfigured, and some reconfigurations would also add appreciably to the operational efficiency of the airport. But because the “baseline” level of risk is so low, the Study concluded that “it would be difficult to construct a compelling case on safety grounds alone for reconfiguring the North Airfield.”     

Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Reprint of “Modelling the resilience,friability and costs of an air transport network affected by a large-scale disruptive event”

This paper deals with developing a methodology for estimating the resilience, friability, and costs of an air transport network affected by a large-scale disruptive event. The network consists of airports and airspace/air routes between them where airlines operate their flights. Resilience is considered as the ability of the network to neutralize the impacts of disruptive event(s). Friability implies reducing the network’s existing resilience due to removing particular nodes/airports and/or links/air routes, and consequently cancelling the affected airline flights. The costs imply additional expenses imposed on airports, airlines, and air passengers as the potentially most affected actors/stakeholders due to mitigating actions such as delaying, cancelling and rerouting particular affected flights. These actions aim at maintaining both the network’s resilience and safety at the acceptable level under given conditions.Large scale disruptive events, which can compromise the resilience and friability of a given air transport network, include bad weather, failures of particular (crucial) network components, the industrial actions of the air transport staff, natural disasters, terrorist threats/attacks and traffic incidents/accidents.The methodology is applied to the selected real-life case under given conditions. In addition, this methodology could be used for pre-selecting the location of airline hub airport(s), assessing the resilience of planned airline schedules and the prospective consequences, and designing mitigating measures before, during, and in the aftermath of a disruptive event. As such, it could, with slight modifications, be applied to transport networks operated by other transport modes.     

Operating cost sensitivity to required time of arrival commands to ensure separation in optimal aircraft 4D trajectories

Trajectory optimisation has shown good potential to reduce environmental impact in aviation. However, a recurring problem is the loss in airspace capacity that fuel optimal procedures pose, usually overcome with speed, altitude or heading advisories that lead to more costly trajectories. This paper aims at the quantification in terms of fuel and time consumption of implementing suboptimal trajectories in a 4D trajectory context that use required times of arrival at specific navigation fixes. A case study is presented by simulating conflicting Airbus A320 departures from two major airports in Catalonia. It is shown how requiring an aircraft to arrive at a waypoint early or late leads to increased fuel burn. In addition, the efficiency of such methods to resolve air traffic conflicts is studied in terms of both fuel burn and resulting aircraft separations. Finally, various scenarios are studied reflecting various airline preferences with regards to cost and fuel burn, as well as different route and conflict geometries for a broader scope of study.     

Predicting the operational acceptance of airborne flight reroute requests using data mining

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.