An Advanced Framework for Comprehensive Performance Evaluation of Terminal Airspace

Air Traffic Management (ATM) is a complex decision-making process. Air traffic controllers' decision on aircraft trajectory control actions directly leads to the efficiency of traffic flow management. In the Automated Point Merge Trajectory Planning (APMTP) problem, it aims to realize an automated routine trajectory management in Terminal Manoeuvring Area (TMA) with an intelligent decision-making agent. An Artificial Intelligence-based approach, mainly Reinforcement Learning (RL) algorithm, is applied to adaptively and smartly integrate four types of de-conflict actions for solving conflicts with fewer delays on the environment. In this paper, we will mainly discuss the policy optimization in APMTP, focus on improving the agent's learning quality and exploration efficiency. Firstly, application of RL in adaptive trajectory planning is presented. APMTP problem is adaptively divided into several sub-problems. For each sub-problem, an online policy π is applied to guide the simulation and optimization modules to find out the conflict-free and less-delay solution. The online policy π is a scale of weight distribution for choosing desirable actions. It follows the rule of Roulette-wheel selection with weighted probability. The highest desirable decision variable has the largest share of the roulette wheel, while the lowest desirable decision variable has the smallest share of the roulette wheel. The RL direct policy optimization algorithm is designed to update the online policy π, Finally, experiments are built up for validation of the proposed policy optimization algorithm for the intelligent decision-making in APMTP. The results in the test environment show that learning agent with different exploration and exploitation ability will result in different system performance in conflict resolution and delay.

Growing political pressure and widespread social concerns about climate change are triggering a paradigm shift in the aviation sector. Projects with the target of reducing aviation’s CO2 emissions and their impact on climate change are being launched to improve currently used procedures. In this paper, a new coordination process between aircraft flight management systems (FMSs) and an arrival manager (AMAN) was investigated to enable fuel-efficient and more sustainable approaches. This coordination posed two major challenges. Firstly, current capacity-centred AMANs’ planning processes are not optimised towards fuel-efficient trajectories. To investigate the benefit of negotiated trajectories with fixed target times for waypoints and thresholds, the terminal manoeuvring area was redesigned for an independent parallel runway system. Secondly, the FMS-AMAN negotiation process plan the trajectories based on time, whereas air traffic controllers guide traffic based on distance. Three tactical assisting tools were implemented in an air traffic controller’s working position to enable a smooth transition from distance-based to time-based coordination and guidance. The whole concept was implemented and tested in real-time human-in-the-loop studies at DLR’s Air Traffic Validation Center. Results showed that the new airspace design and concept was feasible, and a reduction in flown distance was measured.

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft.

The aviation industry faces the urgent challenge of reconciling projected traffic growth with stringent climate targets, including the European Union’s goal of climate neutrality by 2050. While techno-logical innovations such as sustainable aviation fuels and hydrogen propulsion are critical for long-term decarbonization, their near-term impact remains limited. In this thesis, we address the immediate opportunity of improving the environmental efficiency of aircraft arrival operations in Terminal Manoeuvring Areas (TMAs), where congestion and complex sequencing often lead to excess fuel burn, emissions and noise. Existing research has demonstrated the benefits of Continuous Descent Operations (CDOs) and structured arrival procedures such as Point Merge (PM). However, for TMA performance evaluations, cur-rent practices are limited in real-world assessments of environmental efficiency beyond Carbon Dioxide (CO2). In terms of optimization of the arrival operations, the exploration of dynamic PM usage and early speed adjustments as a sequencing tool, integrated within the optimization framework, remains relatively unexplored. Furthermore, most optimization frameworks do not fully integrate arrival and departure scheduling in mixed-mode runway environments. The thesis addresses four key research questions: (1) What are the environmental benefits of using fuel-efficient CDOs within TMA and how to quantify them? (2) How to evaluate the arrival aircraft performance within TMAs implementing PM procedures? (3) Can the performance of current operations in a TMA where PM is implemented be improved by using optimization? (4) How can speed adjustment during cruise and descent be used, with or without combining it with PM procedures, to safely separate and sequence arriving flights? The overarching aim is to develop methodologies for assessing and optimizing arrival operations to reduce fuel consumption, emissions and noise while maintaining safety and capacity. The research combines data-driven performance evaluation with mathematical optimization. We use real-world Automatic Dependent Surveillance Broadcast (ADS-B) trajectory data from the OpenSky Network, together with meteorological data from ECMWF ERA5 and the performance modeling capabilities of EUROCONTROL BADA, to quantify inefficiencies in current operations. For optimization, we develop a Mixed-Integer Programming (MIP)-based framework to assign conflict-free, fuel-efficient arrival routes and descent profiles, incorporating wake turbulence separation and runway scheduling constraints. We perform case studies at Dublin and Oslo-Gardermoen airports, both operating with PM procedures. Empirical analysis reveals substantial inefficiencies in current TMA operations, with level-flight segments contributing significantly to excess fuel burn. Our studies show that CDOs reduce fuel consumption and noise exposure, although Carbon Monoxide (CO) and Hydrocarbon (HC) emissions may increase under idle-thrust conditions. Optimization results demonstrate that dynamic PM usage and early speed adjustments improve horizontal and vertical efficiency, reduce time in TMA and decrease fuel burn compared to actual operations. Operational improvements in arrival management can deliver immediate environmental benefits, complementing long-term technological solutions. The proposed frameworks support SESAR and NextGen objectives for greener TMAs and are adaptable to future contexts, including Urban Air Mobility (UAM). By integrating rigorous performance evaluation with advanced optimization, this thesis provides actionable insights for reducing aviation’s environmental footprint while enhancing predictability.

Ensuring airspace safety is crucial for the efficient operation of aviation, especially at high-altitude airports where challenging weather and geographic conditions significantly increase operational complexity. With the widespread use of ADS-B data, flight monitoring has improved, providing detailed insights into aircraft trajectories. This study investigates the impact of weather variations on trajectory patterns in Terminal Maneuvering Area (TMA) at high-altitude airports during the rainy season, applying daily clustering analysis of ADS-B data combined with meteorological conditions, while further exploring the synergistic effects of other contributing factors such as air traffic control strategies and traffic density. After data preprocessing, a clustering algorithm was used to identify trajectory patterns and detect outlier trajectories. The results show that wind direction is a key factor influencing trajectory cluster changes, with significant shifts observed when wind direction approaches or exceeds certain thresholds. This research introduces a novel method for analyzing trajectory changes in the rainy season at high-altitude airports, offering valuable insights for optimizing flight path planning and enhancing airspace safety management under complex weather conditions.

Continuous descent operations (CDOs) with required times of arrival (RTA) have been identified as a potential solution for reducing the environmental footprint of aviation in the terminal maneuvering area without compromising capacity. This paper assesses the feasibility of replacing current air traffic control sequencing and merging techniques, mainly based on path stretching and air holding, by a control based on RTA over metering fixes on known and fixed arrival routes. Because the remaining distance to the runway threshold is always known by the aircraft crew, this would allow engine-idle CDOs that do not require speed-brake usage and where only elevator control is used to meet the RTA. The assessment has been performed for Barcelona-El Prat Airport (Spain) using historical traffic demand data. The earliest and latest trajectories at a metering fix for each inbound aircraft were computed assuming engine-idle CDOs. Given the attainable RTA window for each aircraft, the aircraft sequencing problem was solved. The results show that assigning RTA allows optimizing the landing sequence when air traffic is low. For scenarios with high-traffic loads and late RTA assignments, path stretching still was found to be necessary. The minimum distance from the runway where inbound aircraft should receive the RTA to fully remove any radar vectoring was also analyzed. It was demonstrated that the assignment of RTA well before starting the descent would favor to enable full CDOs.

Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities

Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao’an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight’s destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal features we designed when the flight’s entering MAS time is given. For TMA travel time prediction, a case study of Guangdong-Hong Kong-Macao Greater Bay Area (GHM-GBA), a typical MAS which contains five major airports closely located within 120km, is carried out using actual historical 4D trajectory data. Finally, Using two months 4D trajectories data, PEK to ZGSZ, the result exhibits the best accuracy, a measurement we define for prediction, of the longterm prediction of ETA given departure time is 92%, and mean absolute error (MAE) is 6.09 minutes.

The delegation to the flight crew of some tasks currently performed by air traffic controllers provides new perspectives to potentially increase air traffic control efficiency. More specifically, the task of establishing properly spaced landing sequences is very demanding in heavy traffic conditions for the air traffic controllers in charge of the terminal maneuvering area. Automatic merging and station keeping operations could relieve air traffic controller of providing time consuming radar vectoring instructions. The objective of this communication is to provide technical insight into the airborne devices and algorithms which may be used onboard aircraft to automatically achieve a specified distance or delay with respect to another aircraft at a specified meter fix. A nonlinear control law based on sliding mode control is proposed to control the lateral motion of the trailing aircraft. The design is followed by two illustrative examples which show the effectiveness of the proposed approach.

In this paper we attempt to answer the question of whether the display of cumulonimbus clouds on air traffic controller radar screens has a significant impact on air traffic control. The developed cumulonimbus cloud mapping simulator allowed the study to be performed by performing simulations with a flight control simulator. This allowed an assessment of how the air traffic controller makes decisions when cumulonimbus is displayed and when it is not. The benefits of cumulonimbus imaging for aircraft trajectories and flight time were also assessed. The research was conducted in the space of Vilnius terminal manoeuvring area.

Free Route Airspace (FRA) is a concept introduced as a part of the Single European Sky Research program, enabling users to arrange routes between defined entry and exit fixes with the goal of addressing some of aviation’s concerns, such as efficiency, capacity, and the environment. This study aims to analyze the mitigation potential of the FRA with different implementation strategies from the perspectives of several stakeholders, particularly in high-complexity airspace. The impact this operational improvement would have on different stakeholders of aviation (such as air traffic controllers, airlines, passengers, and the climate) is studied with the developed models. Simulations are performed in high-complexity airspace with the presence of wind, and the concept is assessed using different planning strategies to analyze the mitigation potentials in different cases and potential impacts on aviation stakeholders and environment. The results show that the operational improvement implemented in the high-complexity airspace in the ECAC (European Civil Aviation Conference) area has a reduction potential of around 7–9% in fuel consumption and carbon dioxide, 26–31% in nitrogen oxide, and 19–27% in the average temperature response over 20 years (ATR20); with respect to the baseline scenario in 2018. And, the impacts obtained by different planning strategies on the operational-, economic-, safety-, and air-traffic-control-related key performance indicators are analyzed in detail throughout the study.

In Finance 101, future corporate managers are taught that the social mission of public companies is to maximize their own longrun (or “intrinsic”) value by investing in all positive net present value (NPV) projects—that is, projects that are expected to earn at least their opportunity cost of capital. In markets that are reasonably efficient, provided management does an effective job of communicating its business plan and its progress in meeting its strategic goals, companies that follow this “NPV rule” can expect to be rewarded with increases in their share prices, at least in the longer run.But in the real world, of course, the pursuit of earnings and other “key performance indicators” (KPIs) often leads to managerial shortsightedness and destruction of value. To explain why—and to help companies avoid this outcome—this article presents an approach that envisions the intrinsic value of the company as an invisible “blue line” that moves through time on a graph, while showing observable key performance indicators, including revenue and earnings (and even the current stock price), as “red lines” on the same graph. The root of the problem is the failure of many companies to distinguish between their KPIs and the underlying drivers of value. KPIs, to be sure, are reflections of important aspects of the business; but however important and useful for strategic planning, they should not be used in performance evaluation or compensation plans for top management as surrogates for the underlying value of the business. Genuine value creation requires systems and a corporate culture that compel managers to pursue all projects that promise to earn the opportunity cost of capital—while treating earnings and other KPIs as means to creating value rather than ends in themselves.

Summary Managing large-scale electrical submersible pump (ESP) operations and assessing their performance can be a challenging task. Diverse operational environments, widely spread geographical areas, large ESP populations, and different service providers are some of the complications facing operators. Nonetheless, it is vital to the success of any artificial lift project to establish a performance evaluation structure that can effectively capture deficiencies and highlight improvements. While many operators focus on run life statistics as the central key performance indicator (KPI) for ESPs, these types of statistics may not be sufficient in providing meaningful information to decision makers. Other important ESP performance parameters include ESP shutdowns (both planned and unplanned), restart time of tripped ESPs, commissioning time, failure rate, and the number of premature ESP failures. Thus, a comprehensive study was jointly initiated between an oil operator and ESP vendors to establish KPIs that drive improvements in all aspects. The selected KPIs were developed in a structure that ultimately focuses on maximizing production availability and revenue generation. By constructing the ESP KPI framework, subpar performance areas were clearly visible by both the operator and the service provider. Decision makers were able to identify and act on fields that lag in performance while exerting efforts to improve underperforming service providers. Furthermore, regular meetings were conducted to review the established KPIs and recommend some action items, which might focus on either technical or operational solutions. Finally, KPI targets were set on the basis of the review of historical trends and were assigned to be challenging yet relevant and attainable. The followed practice can prove to be successful in forming a common ground where service providers can quantify losses by the operator as a result of ESP performance deficiencies. Comprehensive data collection and keeping of ESP trips, failures, and replacements are critical to the success of this work. Regular review of field reports and well performance are imperative to accurately compute the various KPI formulas. In fact, many of the KPI calculations can be automated to capitalize on the available communication networks installed in the field to improve ESP monitoring and accurately assess their performance.

Small and Medium Enterprises (SMEs) do not operate in isolation but within an environment shaped by both opportunities and threats. Given the challenges of low performance and high failure rates among SMEs, this study examined the influence of the socio-cultural environment on the performance of SMEs in Owo Metropolis, Ondo State. Specifically, it focused on the impact of religious beliefs and social attitudes on Fashion designing performance, with revenue growth used as the key performance indicator. The study adopted a quantitative survey research design. The population consisted of all registered SMEs in Owo Metropolis, from which a convenience sample of 261 SMEs was selected. Data was collected using a structured questionnaire, administered in person. Analysis was conducted using multiple regression, revealing that 6.3% of the variance in SME performance could be explained by the independent variables. The model parameters were estimated using the Ordinary Least Squares (OLS) method. Religious beliefs showed a moderate impact on SME performance with a coefficient value of 2.307 (>2), while social attitude demonstrated the highest variability and potential for influence, with a coefficient of 3.446 (>2). The overall model is statistically significant (F = 5.781, p = 0.001) which confirms that, collectively, Religion Beliefs and Social Attitude have a significant impact on performance of SMEs, even if their individual impacts vary. Based on these findings, the study concluded that the socio-cultural environment has a significant positive impact on the performance of SMEs in Owo Metropolis. It was recommended that SME owners and managers in the region align their business strategies with prevailing socio-cultural realities to enhance revenue growth and ensure sustainable performance. Finally, the government should constantly utilize the Social Attitude of consumers towards time, quality products and safety in formulating business strategies.

The purpose of this study was to analyze the effects of fatigue and tension on the physical characteristics and abilities of air traffic controllers (ATCOs) and determine their influence mechanisms. A simulated experiment was designed to evaluate the responses of ATCOs in four states: alertness, fatigue, tension, and fatigue and tension. Thirty young male ATCOs participated in the experiment. Fifteen parameters of their physical characteristics and abilities were collected and analyzed to estimate the effects and the decreasing order of influence of fatigue and tension on the indicators. The results showed that most of the parameters of the ATCOs were significantly affected by fatigue and tension. The attention, perception, reaction time, decision-making ability, and comprehensive performance of the ATCOs were adversely affected by fatigue, and tension had negative effects on their attention, decision-making ability, and comprehensive performance. Fatigue and tension impair the physical characteristics and abilities of ATCOs. Both states initially affected the physical characteristics of the ATCOs and then impaired their abilities. However, the influence mechanisms involved were different. The primary effect of the fatigue state was slowing down, whereas the effect of the tense state was instability. These results provide a reference for the evaluation and management of fatigue and tension states in ATCOs.