Quantile Regression–Based Estimation of Dynamic Statistical Contingency Fuel

Transportation Science ◽

10.1287/trsc.2020.0997 ◽

2020 ◽

Author(s):

Lei Kang ◽

Mark Hansen

Keyword(s):

Quantile Regression ◽

Traffic Management ◽

Carbon Dioxide Emission ◽

Estimation Procedure ◽

Air Traffic Management ◽

Aviation Industry ◽

Fuel Loading ◽

Data Set ◽

Fuel Burn ◽

Quantile Regression Method

Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is reducing contingency fuel loading by dispatchers. Many airlines’ flight planning systems (FPSs) provide recommended contingency fuel for dispatchers in the form of statistical contingency fuel (SCF). However, because of limitations of the current SCF estimation procedure, the application of SCF is limited. In this study, we propose to use quantile regression–based machine learning methods to account for fuel burn uncertainties and estimate more reliable SCF values. Utilizing a large fuel burn data set from a major U.S.-based airline, we find that the proposed quantile regression method outperforms the airline’s FPS. The benefit of applying the improved SCF models is estimated to be in the range $19 million–$65 million in fuel expense savings as well as 132 million–451 million kilograms of carbon dioxide emission reductions per year, with the lower savings being realized even while maintaining the current, extremely low risk of tapping the reserve fuel. The proposed models can also be used to predict benefits from reduced fuel loading enabled by increasing system predictability, for example, with improved air traffic management.

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Operating in the North Atlantic MNPS Airspace

Journal of Navigation ◽

10.1017/s0373463398008091 ◽

1999 ◽

Vol 52 (1) ◽

pp. 11-27

Author(s):

Conor Whelan

Keyword(s):

North Atlantic ◽

Traffic Control ◽

Traffic Management ◽

Air Traffic Management ◽

Air Traffic ◽

Performance Specification ◽

The North ◽

Fuel Burn ◽

The North Atlantic ◽

Reporting Procedures

This paper considers the issue of operating aircraft through the North Atlantic's Minimum Navigation Performance Specification (MNPS) airspace. Noting that aircraft constantly strive for reduced fuel burn and uplift, it describes how flight operators and pilots conduct safe, efficient flights through the region. Reference is made to mechanisms of the North Atlantic MNPS airspace in terms of its Organized Track Structure and other routes that exist. These different structures emphasize the level of flexibility available. Flight planning procedures and requirements necessary to obtain oceanic Air Traffic Control (ATC) clearances are mentioned, as is an account of how communication and position reporting procedures operate to apply the Mach Number technique. Other aspects of MNPS operations such as ETOPS operational restrictions, meteorological effects, the employment of Reduced Vertical Separation Minima and planned regional changes aim to provide an overview of the MNPS system's current and future air traffic management.

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Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

Journal Of Big Data ◽

10.1186/s40537-021-00438-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mahdi Yousefzadeh Aghdam ◽

Seyed Reza Kamel Tabbakh ◽

Seyed Javad Mahdavi Chabok ◽

Maryam Kheyrabadi

Keyword(s):

Deep Learning ◽

Traffic Management ◽

Short Term Memory ◽

Air Traffic Management ◽

Air Traffic ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Data Mining Algorithms ◽

Long Short Term Memory

AbstractNowadays this concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. In this paper, to increase the air traffic management accuracy and legitimacy we used the bidirectional long short-term memory (Bi-LSTMs) and extreme learning machines (ELM) to design the structure of a deep learning network method. The Kaggle data set and different performance parameters and statistical criteria have been used in MATLAB to validate the proposed method. Using the proposed method has improved the criteria factors of this study. The proposed method has had a % increase in air traffic management in comparison to other papers. Therefore, it can be said that the proposed method has a much higher air traffic management capacity in comparison to the previous methods.

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Air Traffic Management during Rare Events Such as a Pandemic: Paris Charles de Gaulle Case Study

Aerospace ◽

10.3390/aerospace8060155 ◽

2021 ◽

Vol 8 (6) ◽

pp. 155

Author(s):

Paolo Scala ◽

Miguel Mujica Mota ◽

Daniel Delahaye

Keyword(s):

Traffic Management ◽

Rare Events ◽

Negative Impact ◽

Public Information ◽

Air Traffic Management ◽

Air Traffic ◽

Aviation Industry ◽

Charles De Gaulle ◽

Airport Capacity ◽

De Gaulle

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.

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The Nonlinear Links Between Urbanization And CO2: Evidence From Unconditional Quantile And Threshold Regression

10.21203/rs.3.rs-676290/v1 ◽

2021 ◽

Author(s):

Mustafa Kocoglu ◽

Ashar Awan ◽

Ahmet Tunc ◽

Alper Aslan

Keyword(s):

Carbon Dioxide ◽

Quantile Regression ◽

Carbon Dioxide Emissions ◽

Carbon Dioxide Emission ◽

Regression Method ◽

Marginal Effect ◽

Threshold Regression ◽

Impact Of Urbanization ◽

Quantile Regression Method ◽

The Relationship

Abstract The extant literature has provided empirical evidences about the relationship between urbanization and environment, however, a little attention has been paid to non-linear relationship among them. This study aims to measure the effects of urbanization on carbon dioxide emission using quantile and threshold regression method. To this end, the study employed threshold analysis and quantile regression method and analyzed the variation of such non-linearity for different levels of carbon dioxide using quantile regression. The results illustrate that a single threshold and two regimes exist and the threshold for urbanization is 29.56%. Across both regimes, the elasticity estimates form an inverted U-shape impact of urbanization on the carbon dioxide emission. The increase in the marginal effect of urbanization on carbon dioxide emissions up to the median level and a declining trend after this level implies that environmental quality significantly improves for emerging country.

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Quantile regression forests to identify determinants of stroke: implications for neighborhoods with high prevalence

10.21203/rs.3.rs-34390/v1 ◽

2020 ◽

Author(s):

Liangyuan Hu ◽

Jiayi Ji ◽

Yan Li ◽

Bian Liu

Keyword(s):

Machine Learning ◽

Quantile Regression ◽

Cardiovascular Health ◽

Multiple Sources ◽

Data Set ◽

Stroke Outcomes ◽

High Prevalence ◽

Analytical Approaches ◽

Quantile Regression Method ◽

Neighborhood Level

Abstract Background : Stroke exerts a massive burden on the U.S. health and economy. Place-based evidence is increasingly recognized as a critical part of stroke management but identifying the key determinants of stroke and the underlying effect mechanisms at the neighborhood level is a topic that has been treated sparingly in the literature. We aim to fill in the research gaps. We develop and apply analytical approaches to address two challenges. First, domain expertise on drivers of neighborhood-level stroke outcomes is limited. Second, commonly used linear regression methods may provide incomplete and biased conclusions.Methods: We created a new neighborhood health data set at census tract level by pooling information from multiple sources. We developed and applied a machine learning based quantile regression method to uncover crucial neighborhood characteristics for neighborhood stroke outcomes among vulnerable neighborhoods burdened with high prevalence of stroke. Results: Neighborhoods with a larger share of non-Hispanic blacks, older adults or people with insufficient sleep tended to have a higher prevalence of stroke, whereas neighborhoods with a higher socio-economic status in terms of income and education had a lower prevalence of stroke. The effects of five major determinants varied geographically and were significantly stronger among neighborhoods with high prevalence of stroke. Conclusions: Highly flexible machine learning identifies true drivers of neighborhood cardiovascular health outcomes from wide-ranging information in an agnostic and reproducible way. The identified major determinants and the effect mechanisms can provide important avenues for prioritizing and allocating resources to develop optimal community-level interventions for stroke prevention.

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Safety Culture and Safety-Relevant Behavior in Air Traffic Management

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000068 ◽

2015 ◽

Vol 5 (1) ◽

pp. 3-17 ◽

Cited By ~ 5

Author(s):

Michaela Schwarz ◽

K. Wolfgang Kallus

Keyword(s):

Safety Culture ◽

Traffic Management ◽

Service Providers ◽

Five Factor Model ◽

Principal Component ◽

Air Traffic Management ◽

Air Traffic ◽

Psychometric Validation ◽

Safety Attitudes ◽

Culture Development

Since 2010, air navigation service providers have been mandated to implement a positive and proactive safety culture based on shared beliefs, assumptions, and values regarding safety. This mandate raised the need to develop and validate a concept and tools to assess the level of safety culture in organizations. An initial set of 40 safety culture questions based on eight themes underwent psychometric validation. Principal component analysis was applied to data from 282 air traffic management staff, producing a five-factor model of informed culture, reporting and learning culture, just culture, and flexible culture, as well as management’s safety attitudes. This five-factor solution was validated across two different occupational groups and assessment dates (construct validity). Criterion validity was partly achieved by predicting safety-relevant behavior on the job through three out of five safety culture scores. Results indicated a nonlinear relationship with safety culture scales. Overall the proposed concept proved reliable and valid with respect to safety culture development, providing a robust foundation for managers, safety experts, and operational and safety researchers to measure and further improve the level of safety culture within the air traffic management context.

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