Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions

Allocating Air Traffic Flow Management Slots

In Europe, when an imbalance between demand and capacity is detected for air traffic network resources, Air Traffic Flow Management slots are allocated to flights on the basis of a First Planned First Served principle. We propose a market mechanism to allocate such slots in the case of a single constrained en-route sector or airport. We show that our mechanism provides a slot allocation which is economically preferable to the current one as it enables airlines to pay for delay reduction or receive compensations for delay increases. We also discuss the implementation of our mechanism through two alternative distributed approaches that spare airlines the disclosure of private information. Both these approaches have the additional advantage that they directly involve airlines in the decision making process. Two computational examples relying on real data illustrate our findings.Air Transportation, Market Mechanism Design, Air Traffic Flow Management slots, Collaborative Decision Making, SESAR.

DEMAND CAPACITY BALANCING IN MULTI-MODAL TRANSPORTATION THROUGH OPTIMIZATION AND SIMULATION

International audienceThe current Air traffic System in Europe relies on airspace and airport capacity estimates computed by the Air National Service Providers (ANSPs) using demand forecast and Air traffic Controllers operations schedules. The Demand Capacity Balancing (DCB) aims at reducing the Air Traffic Management resources held in reserve to cope with demand peaks by providing the system with demand smoothing means. A recent study on the subject suggests introducing a congestion-based route fee that encourages users to avoid crowded slots for a given departure and arrival airport [1]. An optimal equilibrium point can then be reached through a clever choice of penalties incurred by flying at departure times adversely impacting congestion. Alternative routes may also be considered in the planning, as for a whole category of customers price tag is more important than travel time. However, taking into account that for short haul flights alternative means of transportation may be a viable option, DCB can be addressed in a wider scope by considering surface vehicles along aircraft. A side effect of this holistic approach is the ability to cope with disruptive events. The present work describes a simulation and optimization model tailored to this particular problem

Relationship between Weather, Traffic and Delay Based on Empirical Methods

The steady rise in demand for air transportation over the years has put much emphasis on the need for sophisticated air traffic flow management (TFM) within the National Airspace System (NAS). The NAS refers to hardware, software and people, including runways, radars, networks, FAA, airlines, etc., involved in air traffic management (ATM) in the US. One of the metrics that has been used to assess the performance of NAS is the actual delays provided through FAA's Air Traffic Operations Network (OPSNET). The OPSNET delay data includes those reportable delays, i.e. delays of 15 minutes or more experienced by Instrument Flight Rule (IFR) flights, submitted by the FAA facilities. These OPSNET delays are caused by the application of TFM initiatives in response to, for instance, weather conditions, increased traffic volume, equipment outages, airline operations, and runway conditions. TFM initiatives such as, ground stops, ground delay programs, rerouting, airborne holding, and miles-in-trail restrictions, are actions which are needed to control the air traffic demand to mitigate the demand-capacity imbalance due to the reduction in capacity. Consequently, TFM initiatives result in NAS delays. Of all the causes, weather has been identified as the most important causal factor for NAS delays. Therefore, in order to accurately assess the NAS performance, it has become necessary to create a baseline for NAS performance and establish a model which characterizes the relation between weather and NAS delays

A multi-level predictive methodology for terminal area air traffic flow

Over the past few decades, the air transportation system has grown significantly. In particular, the number of passengers using air transportation has greatly increased. As the demand for air travel expands, airport departure/arrival demand almost reaches its capacity. In consequence, the level of delays increases since the system capacity cannot manage the increased demand. With this trend, the national airspace system (NAS) will be saturated, and the congestion at the airport will become even more severe. As a result of congestion, a considerable number of flights experience delays. According to the Bureau of Transportation Statistics (BTS), over 1 million flights are operated in a year, and about twenty percent of all scheduled commercial flights are delayed more than 15 minutes. These delays cost billions of dollars annually for airlines, passengers, and the US economy. Therefore, this study seeks to find out why the delays occur and to analyze patterns in which the delays occurred. Analysis of airport operations generally falls into a macro or micro perspective. At the macro point of view, very few details are considered, and delays are aggregated at the airport level. Especially, shortfalls in airport capacity and a capacity-demand imbalance are the primary causes of delays in this respect. In the micro perspective, each aircraft is modeled individually, and the causes of delays are reproduced as precisely as possible. Micro reasons for air traffic delays include inclement weather, mechanics problems, operation issues. In this regard, this research proposes a methodology that can efficiently and practically predict macro and micro-level air traffic flow in the terminal area. For a macro-level analysis of delays, artificial neural networks models are proposed to predict the hourly airport capacity. Multi-layer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM) are trained with historical weather and airport capacity data of Hartsfield-Jackson Atlanta airport (ATL). In the performance evaluation, the models have presented decent predictive performance and successfully predicted the test data as well as the training data. On the other hand, Random Forests and AdaBoost are implemented in the micro-level modeling of the air traffic. The micro-level models trained with on-time flight performance data and corresponding weather data focus on a classification of the individual flight delays. The model provides interpretability and imbalanced data handling while the accuracy is as good as the existing methods. Lastly, the predictive model for individual flight delays is refined using the cost-proportionate rejection sampling (costing) method. Along with the integration of the costing method, general machine learning algorithms have been converted to cost-sensitive classifiers. The cost-sensitive classifiers were able to account for asymmetric misclassification costs without losing their diagnostic functionality as binary classifiers. This study presents a data-driven approach to air traffic flow management that can effectively utilize air traffic data accumulated over decades. Through data analysis from the macro and micro perspective, an integrated methodology for terminal air traffic flow prediction is provided. An accurate prediction of the airport capacity and individual flight delays will assist stakeholders in taking more informed decisions.Ph.D

The Influence of Traffic Structure on Airspace Capacity

Best paper award for the Network Management trackInternational audienceAirspace structure can be used as a procedural mechanism for a priori separation and organization of en-route air traffic. Although many studies have explored novel structuring methods to increase en-route airspace capacity, the relationship between the level of structuring of traffic and airspace capacity is not well established. To better understand the influence of traffic structure on airspace capacity, in this research, four airspace concepts, representing discrete points along the dimension of structure, were compared using large-scale simulation experiments. By subjecting the concepts to multiple traffic demand scenarios, the structure-capacity relationship was inferred from the effect of traffic demand variations on safety, efficiency and stability metrics. These simulations were performed within the context of a future personal aerial transportation system, and considered both nominal and non-nominal conditions. Simulation results suggest that the structuring of traffic must take into account the expected traffic demand pattern to be beneficial in terms of capacity. Furthermore, for the heterogeneous, or uniformly distributed, traffic demand patterns considered in this work, a decentralized layered airspace concept, in which each altitude band limited horizontal travel to within a predefined heading range, led to the best balance of all the metrics considered

A theoretical framework of sustainability in air transportation planning and future prospects of airport infrastructure upgrading : a case study of Kuala Lumpur International Airport 2 (KLIA 2)

Air transportation has become the fastest growing mode of transportation in adapting with the transportation facilities and services provided. The developments of air transportation have surrounded with the introduction of much larger transport aircraft and rising airport infrastructure upgrading nowadays. Due to the increasing importance of enlargements in airport facility, this has become a concern for policy makers and academics. Although the time value and greatest cost efficiency are obtained from the airport facilities, there are negative externalities produced by airport developments. Due to the increasing importance of enlargements in airport capacity, it should also regard as the management of the environmental impact on surrounding areas. Literature has found that airport operations may produce various regulated pollutants, including volatile organic compounds (VOCs), carbon monoxide (CO), and particulate matter (PM) (Luther, 2007).This paper aims at identifying the sustainability in air transportation planning and future prospects of airport infrastructure upgrading; using the KLIA 2 as a case study. At most airports, the major environmental concerns embrace local air quality, noise, sustainability and recycling along with habitat and wildlife management. Issues relating to the sustainability of specific industrial sectors such as aviation are relatively under researched. Procedures and technologies for environmental protection, environmental efficiency and impact mitigation receive a considerable degree of attention from industry, government and academia alike has to be increased. Even though the airport expansion is very important to cater the demand, however, there are some policies and strategies that need to be considered to balance the need and the future. Conventionally, the planning of airport infrastructure upgrading has only focused on elements surrounded by the airport; such as supply and demand forecasts and other aeronautical and engineering. But the recent airport framework presents new situations that cannot be solved by traditional methods since new and external variables are intrinsic to the decision-making process (Graham and Guyer, 1999).The study will focus on the environmental impacts of the KLIA 2 constructions which are ongoing. However, this paper highlight the literature background on impact of airport expansion on air pollution and noise issues to the environment as well as to the community

Urban Air Mobility: Vision, Challenges And Opportunities

Urban Air Mobility (UAM) involving piloted or autonomous aerial vehicles, is envisioned as emerging disruptive technology for next-generation transportation addressing mobility challenges in congested cities. This paradigm may include aircrafts ranging from small unmanned aerial vehicles (UAVs) or drones, to aircrafts with passenger carrying capacity, such as personal air vehicles (PAVs). This paper highlights the UAM vision and brings out the underlying fundamental research challenges and opportunities from computing, networking, and service perspectives for sustainable design and implementation of this promising technology providing an innovative infrastructure for urban mobility. Important research questions include, but are not limited to, real-Time autonomous scheduling, dynamic route planning, aerial-To-ground and inter-vehicle communications, airspace traffic management, on-demand air mobility, resource management, quality of service and quality of experience, sensing (edge) analytics and machine learning for trustworthy decision making, optimization of operational services, and socio-economic impacts of UAM infrastructure on sustainability

Machine Learning Application in Air Traffic Management Resiliency based on Capacity Regulations

There is a considerable interest in air transportation resilience as a mechanism to cope with the consequences of disruptions to local authorities. Although the identification of metrics and baselines for measuring resilience are still regarded as challenges, we believe that the meaning of disruptions is no longer driven solely by safety threats but also by emergent performance issues. In this paper, resilience of the European Air Traffic Management Network (EATMN) is studied from a performance perspective. In fact, improved predictability and reliability of planning data across the EATMN, allow reduction of reserved Air Traffic Management (ATM) capacity. Consequently, the management of emergent demand-capacity imbalances, regarded as disruptions, is added to tactical phase of Air Fraffic Flow and Capacity Management (ATFCM). In this phase of operations (i.e. day-of-operations) a limited number of variables are available to form aggregated indicators for network resilience. We consider that available ATFCM regulations data reveal restorative mechanisms for tactical Demand-Capacity Balancing (DCB). Aggregated indicators are regarded as enablers to monitor the resilient management of Area Control Centers and to observe spatial distribution of network resiliency. This paper presents an exploratory effort of the needed situational awareness by exploring supervised learning techniques in the context of ATFCM regulations to predict Air Traffic Flow Management (ATFM) delay. In particular, it focuses on the application of machine learning algorithms and comparison of different architecture variants to a regression study on tactical DCB disruptions