Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.     

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).     

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.     

Airport congestion and near-midair collisions

This paper presents a model that systematically integrates, for the first time, the association between a region's aviation near-midair collision risk and its traffic levels, its type and amount of air traffic control, and the complexity of its airspace. The model incorporates the tight interrelatedness (and correlation) between traffic, airspace complexity, and air traffic controller staffing. An estimation of the model using cross-sectional data on 143 U.S. airports in 1985 indicates that the frequency of reported near-midair collisions (NMACs) is positively associated with regional traffic and airspace complexity, despite the fact that busier, more complex regions generally have more air traffic controllers. Also, in regions governed by “terminal radar service areas” (TRSAs), the reported near-midair collisions are positively associated with the presence of more satellite airports than would be expected on the basis of traffic alone. Finally, deviations from controller staffing levels that would be expected on the basis of traffic and airspace complexity alone are significantly associated with variations in reported NMACs in terminal control areas but not in terminal radar service areas.     

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.     

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.     

Air holding problem solving with reinforcement learning to reduce airspace congestion

The Air Holding Problem Module is proposed as a decision support system to help air traffic controllers in their daily air traffic flow management. This system is developed using an Artificial Intelligence technique known as multiagent systems to organize and optimize the solutions for controllers to handle traffic flow in Brazilian airspace. In this research, the air holding problem is modeled with reinforcement learning, and a solution is proposed and applied in two case studies of the Brazilian airspace. The system can suggest more precise and realistic actions based upon past situations and knowledge of the professionals and forecast the impact of restrictive measures at the local and/or overall level. The first case study shows performance improvements in traffic flows between 8 and 47% at the local level up to 49% at the overall level. In the second case study, performance improvements were between 15 and 57% at the local level and between 41 and 48% at the overall level. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

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.     

Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

This paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles’ routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of Cluster Representative Subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis.     

Multi-criteria analysis of optimal signal plans using microscopic traffic models

Increasing concerns on environment and natural resources, coupled with increasing demand for transport, put lots of pressure for improved efficiency and performance on transport systems worldwide. New technology nowadays enables fast innovation in transport, but it is the policy for deployment and operation with a systems perspective that often determines success. Smart traffic management has played important roles for continuous development of traffic systems especially in urban areas. There is, however, still lack of effort in current traffic management and planning practice prioritizing policy goals in environment and energy. This paper presents an application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability. Microscopic traffic simulator is integrated with micro-scale emission model for estimation of emissions and fuel consumption at high resolution. A stochastic optimization engine is implemented to facilitate optimal signal planning for different policy goals, including delay, stop-and-goes, fuel economy etc. In order to enhance the validity of the modeling framework, both traffic and emission models are fine-tuned using data collected in a Chinese city. In addition, two microscopic traffic models are applied, and lead to consistent results for signal optimization. Two control schemes, fixed time and vehicle actuated, are optimized while multiple performance indexes are analyzed and compared for corresponding objectives. Solutions, representing compromise between different policies, are also obtained in the case study by optimizing an integrated performance index.     

Optimization of terminal airspace operation with environmental considerations

The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.     

Air traffic management: Optimization models and algorithms

Current air traffic control systems are mainly conceived to ensure the safety of flights by means of tactical interventions, because of the difficulty of accurately foreseeing the traffic evolution. In fact, in real traffic conditions, planes are often penalized since sometimes safety standards are redundant. Today, this management philosophy is no longer valid because of congestion phenomena which often occur in the most important terminal areas. Therefore, as to future control systems it is necessary to introduce not only more automated procedures to keep adequate safety levels, but also planning functions in order to increase the system capacity and to improve system efficiency. In recent years several studies have been carried out, new control concepts have been introduced and some optimization models and algorithms developed to improve air traffic management. In this paper a survey of our early works in this field is reported and a multilevel model of air traffic management is proposed and discussed. The functions corresponding to the on-line control, that is flow control, strategic control of flights and aircraft sequencing in a terminal area, are examined and the optimization models and solution algorithms are illustrated. Finally, relevant problems coped by recent research are mentioned and new trends are indicated.     

Stochasticity of freeway operational capacity and chance-constrained ramp metering

The notion of capacity is essential to the planning, design, and operations of freeway systems. However, in the practice freeway capacity is commonly referred as a theoretical/design value without consideration of operational characteristics of freeways. This is evident from the Highway Capacity Manual (HCM) 2000 in that no influence from downstream traffic is considered in the definition of freeway capacity. In contrast to this definition, in this paper, we consider the impact of downstream traffic and define freeway operational capacity as the maximum hourly rate at which vehicles can be expected to traverse a point or a uniform section of a roadway under prevailing traffic flow conditions. Therefore freeway operational capacity is not a single value with theoretical notion. Rather, it changes under different traffic flow conditions. Specifically, this concept addresses the capacity loss during congested traffic conditions. We further study the stochasticity of freeway operational capacity by examining loop detector data at three specifically selected detector stations in the Twin Cities’ area. It is found that values of freeway operational capacity under different traffic flow conditions generally fit normal distributions. In recognition of the stochastic nature of freeway capacity, we propose a new chance-constrained ramp metering strategy, in which, constant capacity value is replaced by a probabilistic one that changes dynamically depending on real-time traffic conditions and acceptable probability of risk determined by traffic engineers. We then improve the Minnesota ZONE metering algorithm by applying the stochastic chance constraints and test the improved algorithm through microscopic traffic simulation. The evaluation results demonstrate varying degrees of system improvement depending on the acceptable level of risk defined.     

Metaheuristics for efficient aircraft scheduling and re-routing at busy terminal control areas

Intelligent decision support systems for the real-time management of landing and take-off operations can be very effective in helping air traffic controllers to limit airport congestion at busy terminal control areas. The key optimization problem to be solved regards the assignment of airport resources to take-off and landing aircraft and the aircraft sequencing on them. The problem can be formulated as a mixed integer linear program. However, since this problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY solver (a state-of-the-art optimization solver to deal with complex routing and scheduling problems) in order to improve the possibility of finding good quality solutions quickly. The proposed framework starts from a good initial solution for the aircraft scheduling problem with fixed routes (given the resources to be traversed by each aircraft), computed via a truncated branch-and-bound algorithm. A metaheuristic is then applied to improve the solution by re-routing some aircraft in the terminal control area. New metaheuristics, based on variable neighbourhood search, tabu search and hybrid schemes, are introduced. Computational experiments are performed on an Italian terminal control area under various types of disturbances, including multiple aircraft delays and a temporarily disrupted runway. The metaheuristics achieve solutions of remarkable quality, within a small computation time, compared with a commercial solver and with the previous versions of AGLIBRARY.     

The Time Dependent Traveling Salesman Planning Problem in Controlled Airspace

The integration of drones into civil airspace is one of the most challenging problems for the automation of the controlled airspace, and the optimization of the drone route is a key step for this process. In this paper, we optimize the route planning of a drone mission that consists of departing from an airport, flying over a set of mission way points and coming back to the initial airport. We assume that during the mission a set of piloted aircraft flies in the same airspace and thus the cost of the drone route depends on the air traffic and on the avoidance maneuvers used to prevent possible conflicts. Two air traffic management techniques, i.e., routing and holding, are modeled in order to maintain a minimum separation between the drone and the piloted aircraft. The considered problem, called the Time Dependent Traveling Salesman Planning Problem in Controlled Airspace (TDTSPPCA), relates to the drone route planning phase and aims to minimize the total operational cost. Two heuristic algorithms are proposed for the solution of the problem. A mathematical formulation based on a particular version of the Time Dependent Traveling Salesman Problem, which allows holdings at mission way points, and a Branch and Cut algorithm are proposed for solving the TDTSPPCA to optimality. An additional formulation, based on a Travelling Salesman Problem variant that uses specific penalties to model the holding times, is proposed and a Cutting Plane algorithm is designed. Finally, computational experiments on real-world air traffic data from Milano Linate Terminal Maneuvering Area are reported to evaluate the performance of the proposed formulations and of the heuristic algorithms.     

Three-dimensional conflict count models for unstructured and layered airspace designs

This paper presents analytical models that describe the safety of unstructured and layered en route airspace designs. Here, ‘unstructured airspace’ refers to airspace designs that offer operators complete freedom in path planning, whereas ‘layered airspace’ refers to airspace concepts that utilize heading-altitude rules to vertically separate cruising aircraft based on their travel directions. With a focus on the intrinsic safety provided by an airspace design, the models compute instantaneous conflict counts as a function of traffic demand and airspace design parameters, such as traffic separation requirements and the permitted heading range per flight level. While previous studies have focused primarily on conflicts between cruising aircraft, the models presented here also take into account conflicts involving climbing and descending traffic. Fast-time simulation experiments used to validate the modeling approach indicate that the models estimate instantaneous conflict counts with high accuracy for both airspace designs. The simulation results also show that climbing and descending traffic caused the majority of conflicts for layered airspaces with a narrow heading range per flight level, highlighting the importance of including all aircraft flight phases for a comprehensive safety analysis. Because such trends could be accurately predicted by the three-dimensional models derived here, these analytical models can be used as tools for airspace design applications as they provide a detailed understanding of the relationships between the parameters that influence the safety of unstructured and layered airspace designs.     

From ‘Our Air Is Not for Sale’ to ‘Airtrack’: The Part Privatization of the UK's Airspac

Air traffic in the UK has increased rapidly in the past two decades and is forecast to grow at a rate of 4.3% per annum between 1998 and 2020. The failure to develop the air traffic system in order to cope with this growth has had undesirable consequences, e.g. a rise in flight delays and near misses. Given the investment required in air traffic control systems to cater for this growth, the UK government in 2000 part privatized the National Air Traffic Services (NATS), the body in charge of the UK's airspace in a public–private partnership (PPP). The UK Airline Group acquired 46% of NATS and effective operational control, though the government retains a share in NATS and safety regulation is in the public sector. However, serious doubts about safety were raised during the debate on the PPP. Similar moves towards a commercial operation (i.e. corporatization) of air navigation services have been made in New Zealand and Canada over the past decade and these provide useful insights into the results of the corporatization process. This paper analyses the main issues surrounding the part privatization of NATS. First, it highlights the experience from New Zealand and Canada of the major issues involved in corporatized air navigation services in six different categories: funding, new technology and project management, safety, pricing regime, international opportunities, and customer responsiveness. The likely impacts for NATS given the lessons from New Zealand and Canada are considered. The UK government's provisions for the PPP and their implementation in the post‐PPP NATS are then outlined for these six categories. Finally, the 11 September 2001 terrorist attacks have had major impacts on air travel and their consequences for NATS in the six categories are highlighted. This paper concludes with some of the issues that need to be addressed to ensure the success of the PPP for NATS.     

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.     

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.