Air Route Research Articles

Review: Traveling Black: A Story of Race and Resistance, by Mia Bay

Review: <i>Traveling Black: A Story of Race and Resistance</i>, by Mia Bay

A Deep Learning Approach for Flight Delay Prediction Through Time-Evolving Graphs

Flight delay prediction has recently gained growing popularity due to the significant role it plays in efficient airline and airport operation. Most of the previous prediction works consider the single-airport scenario, which overlooks the time-varying spatial interactions hidden in airport networks. In this paper, the flight delay prediction problem is investigated from a network perspective (i.e., multi-airport scenario). To model the time-evolving and periodic graph-structured information in the airport network, a flight delay prediction approach based on the graph convolutional neural network (GCN) is developed in this paper. More specifically, regarding that GCN cannot take both delay time-series and time-evolving graph structures as inputs, a temporal convolutional block based on the Markov property is employed to mine the time-varying patterns of flight delays through a sequence of graph snapshots. Moreover, considering that unknown occasional air routes under emergency may result in incomplete graph-structured inputs for GCN, an adaptive graph convolutional block is embedded into the proposed method to expose spatial interactions hidden in airport networks. Through extensive experiments, it has been shown that the proposed approach outperforms benchmark methods with a satisfying accuracy improvement at the cost of acceptable execution time. The obtained results reveal that deep learning approach based on graph-structured inputs have great potentials in the flight delay prediction problem.

A Unified Airspace Risk Management Framework for UAS Operations

Collision risk modelling has a long history in the aviation industry, with mature models currently utilised for the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the associated gaps in the existing airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and transformation of the associated models into the spatial domain to generate a protection volume around each predicted air traffic conflict. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting, respectively, a terminal manoeuvring area and an enroute scenario.

Hybrid Air Route Network Simulation Based on Improved RW-Bucket Algorithm

The fixed-route network is the basis for airlines to carry out flight direction planning. Some countries and regions have introduced temporary routes as a supplement to the fixed route structure. Temporary routes have the advantages of intercepting curves and straightening, and less detouring areas, which can save airlines fuel consumption and improve operational efficiency. This article takes the mixed airline's route network as the research object. Firstly, the structural properties of mixed air route networks are analyzed by using complex network theory. Secondly, the RW-bucket algorithm to generate a mixed air route network structure is analyzed. According to the conclusion of the analysis, the RW-bucket algorithm is improved. Finally two flight information region route network data are used to verify the proposed hybrid route network construction method by computer simulation.

Accounting for the aircraft emissions of China’s domestic routes during 2014–2019

The “13th Five-Year Plan” of civil aviation energy conservation and emission reduction impacts China’s domestic aviation exchanges. However, few researchers pay attrition to the impact of the 13th Five-Year Plan on aviation emissions. This paper intends to calculate the emissions of six pollutants (CO2, CO, HC, NOx, PM2.5, and SO2) in China’s domestic airlines from 2014 to 2019 and explore the impact of the 13th Five-Year Plan on emissions changes. In this paper, the improved BFFM2-FOA-FPM method is used to unify the calculation of CO2 and other aviation emissions. The error rate between the estimate and the official data was about 6.45%. The results show that the 13th Five-Year Plan has impacted on aviation emissions, including the number of routes and airlines, aircraft configuration, air routes, and airline unit turnover emissions. Additionally, the 13th Five-Year Plan’s effect is not significant, and it does not promote the reduction of emissions on domestic routes.

The impacts of longitudinal separation, efficiency loss, and cruise speed adjustment in robust terminal traffic flow problem under uncertainty

ABSTRACT Minimization of approaching time and maximization of the utilization of air route and airport resources are the two ultimate goals of air traffic control. This research considers arrival sequence and the uncertain arrival times at entry waypoints of flights and investigates the impact of air traffic controller specific parameters in the model. The two-stage optimization framework will first determine a deterministic schedule and optimize the approaching time while considering additional longitudinal separation, efficiency loss from deterministic schedule, and cruise speed adjustment in the second-stage optimization model via sample average approximation. The numerical experiments suggested an efficiency loss of 20%, which can absorb the empirical probabilistic lateness at entry waypoints with the real-world data. The proposed method could determine the estimated average delay time at runways with different settings of additional buffer for longitudinal separation and trade-off parameters between the estimated average delay time and estimated penalty cost of cruise speed adjustment.

Investigating the impact of adding new airline routes on air transportation resilience in China

When building new airports various strategies for initiating new air routes prominently affect the overall system performance. From a perspective of disruption management, however, very little is known regarding the impacts of different strategies exerted to the resilience of airline networks in a multi-layer system configuration. In this paper, we established a multi-layer China airline network (CAN) using route data from 46 airlines in mainland China and proposed four optimization strategies for adding new routes based on service-oriented perspectives. The geographical traits of the multi-layer CAN was first explored and, taking the Daxing International Airport (a new-built airport in Beijing, China) as a demonstrative example, we then conducted both qualitative and quantitative analyses to investigate the impact of the proposed strategies and their effectiveness of improving network resilience. It is revealed that those strategies have heterogeneous effects on the network performance in terms of meeting passenger demands and balancing overall connectivity at a national strategic level, and the overall resilience of the CAN could be significantly improved under the selected strategy by 59.8% on average. Based on the findings, several managerial and policy implications are put forward and discussed. This paper sheds new lights on resilience-building strategies for service carriers and airport managers and is also of value to regional governments for offering incentives for expanding connections to new destinations.

Route-based performance evaluation for airlines – A metafrontier data envelopment analysis approach

The international aviation industry is essential for both passengers and commodities. Previous literature tends to evaluate aviation performance from the perspective of airlines or airports. However, each route faces a different operating environment and thus has a different profit portfolio. Therefore, a route-based performance evaluation can better reflect the range of operating characteristics by considering the differing characteristics of routes. This study conducted an empirical analysis of 112 international routes from two Taiwanese airlines. To first identify the key variables that have a significant impact on airline performance, a systematic literature review was conducted using a meta-analysis approach. This study then used those key variables to measure route-based performance with data envelopment analysis (DEA) modeling. Finally, k-medoids cluster analysis was used to explore successful factors for air route performance as complementary research to the DEA modeling. Model results show three groups of routes can be clearly identified by considering route efficiency. Each group has very distinct route characteristics (e.g., Asian routes vs US routes) and some route planning recommendations can be developed. The results of this study can help operators and policy makers improve operating performance and identify key factors for route development and planning.

Impact of Spatial Orientation Ability on Air Traffic Conflict Detection in a Simulated Free Route Airspace Environment.

In the selection of job candidates who have the mental ability to become professional ATCOs, psychometric testing has been a ubiquitous activity in the ATM domain. To contribute to psychometric research in the ATM domain, we investigated the extent to which spatial orientation ability (SOA), as conceptualized in the spatial cognition and navigation literature, predicted air traffic conflict detection performance in a simulated free route airspace (FRA) environment. The implementation of free route airspace (FRA) over the past few years, notably in Europe, have facilitated air traffic services by giving greater flexibility to aviation operators in planning and choosing preferred air routes that can lead to quicker arrivals. FRA offers enhanced system safety and efficiency, but these benefits can be outweighed by the introduction of air traffic conflicts that are geometrically more complex. Such conflicts can arise from increased number and distribution of conflict points, as well as from elevated uncertainty in aircraft maneuvering (for instance, during heading changes). Overall, these issues will make conflict detection more challenging for air traffic controllers (ATCOs). Consequently, there is a need to select ATCOs with suitably high levels of spatial orientation ability (SOA) to ensure flight safety under FRA implementation. In this study, we tested 20 participants who are eligible for ATCO job application, and found that response time-based performance on a newly developed, open access, computerized spatial orientation test (SOT) predicted time to loss of minimum separation (tLMS) performance on an air traffic conflict detection task (AT-CDT) we designed. We found this predictive relationship to be significant to a moderately large extent under scenarios with high air traffic density (raw regression coefficient = 0.58). Moreover, we demonstrated our AT-CDT as a valid test in terms of eliciting well-known mental workload and spatial learning effects. We explained these findings in light of similar or overlapping mental processes that were most likely activated optimally under task conditions featuring approximately equal numbers of outcome-relevant stimuli. We conclude by discussing the further application of the SOT to the selection of prospective ATCOs who can demonstrate high levels of conflict detection performance in FRA during training simulations.

Safety performance functions to predict separation minima infringements in en-route airspace

PurposeThe purpose of this study is to establish a systematic framework to characterise the safety of air routes, in terms of separation minima infringements (SMIs) between en-route aircraft, based on the definition of models known as safety performance functions.Design/methodology/approachTechniques with high predictive capability were selected that enable both expert knowledge and data to be harnessed: Bayesian networks. It was necessary to establish a conceptual framework that integrates the knowledge currently available on the causality and precursors of SMIs with the hindsight derived from the analysis of the type of data available. To translate the conceptual framework into a set of causal subnets, the concepts of air traffic management (ATM) barrier model and event trees have been incorporated.FindingsThe model combines analytics and insights, as well as predictive capability, to answer the question of how airspace separation infringements are produced and what their frequency of occurrence will be. The main outputs of the network are the predicted probability of success for the ATM barriers and the predicted probability distribution of the vertical and horizontal separation of an aircraft in its closest point of approach.Originality/valueThe main contribution of this work is that, by virtue of the calculation capacity obtained, the network can be used to draw conclusions about the impact that a modification of the airspace and of the traffic, or operational conditions, would have on the effectiveness of the barriers and on the final distributions of distance between aircraft in the CPA, thereby estimating the probability of SMI.

The hazards, treatment measures and sustainable development of electronic waste

The faster upgrading and the rising consumer’s demand for electrical and electronic equipment (EEE) results in an ever-increasing number of waste electrical and electronic items (WEEE), which became a severe pollution problem at both local and global scales. This paper presents the current six categories of EEE that are widely used for electronic waste (E-waste) management worldwide and the major component of E-waste: non-hazardous materials, heavy metal, and persistent organic pollutants (POPs). To learn about E-waste’s negative impacts on both environment and the human body, this paper will show the specific polluting route in air, water, and soil. Those pollutants would affect the environment through burning and directly released into the air, penetrating and leaking into water and soil. The polluted environment and the poor sanitary conditions will further destroy people’s health. This paper shows and summarizes the development of current E-waste management, from direct landfill to chemical disposal, to today’s eco-friendly way--bioleaching. Future E-waste disposal technology needs to become more environmentally friendly and more efficient for reusable material recovery. Therefore, we hope that E-waste management can closely connect with Sustainable Development Goals (SDGs). We need to apply SDGs into every aspect of E-waste management, which can significantly reduce the adverse effects of E-waste and be beneficial for the electronic industry.

The Application of Geodesic in Deployable Antenna Mesh Dividing

Space deployable antennna has the characteristics of Lightweight, large diameter, high precision etc, which can meet the requirements of the launch vehicle carrying and payload’s Electromagnetic performance. Antenna’s mesh dividing have the direct bearing on payload’s Electromagnetic performance, geodesic mesh dividing has the advantage of shortest length of mseh line. but the equation solving of geodesic is complicated,which Impeding the geodesic’s application In academic and engineering fields. in this paper, a method has be proposed to solve the difficulty of geodesic equation solving. the solving result can be used in antenna’s mesh dividing, meanwhile the solving result can be used in Robot trajectory planning, Air route planning,Navigation route planning,Architectural structure design,etc. great care should be taken in constructing both.

В ПОИСКАХ ПУТЕЙ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ ИСПОЛЬЗОВАНИЯ КРИОГЕННЫХ РЕСУРСОВ В ЭКОНОМИКЕ СОВЕТСКОГО СОЮЗА: ИНТЕЛЛЕКТУАЛЬНЫЕ ПРАКТИКИ И ОТВЕТЫ НА ВЫЗОВЫ ВРЕМЕНИ

Based on the analysis of scientific literature and archive materials, including those being introduced into scientific turnover by the author, the article reconstructs the history of research activities intended to develop new methods and optimize existing practices of exploitation of cryogenic resources (i.e. cold, snow, ice and permafrost) by economic entities of the Soviet Union. In methodological terms, the article is based on principles of a new philosophical strand in ontology: cryosophy. Its key principles regard the Earth's cryosphere as an active element of the Universe and a source of goods and opportunities for human beings. Such a positive approach to cold allows, among other things, to develop the context of the "Cryoanthropology". Research into this context aims to reveal the role of natural cold and his "domestication" in creating the unique culture of native ethnic groups in the Russian Arctic and its present-day status, including how scientific research of the Soviet and post-Soviet periods has influenced it. In this regard, the article contains general results of investigations aimed to optimize the use of natural cold in providing long-term food storage, the use of snow and ice for irrigation of agricultural land and in fortification and civil engineering. At the same time, research into the scientific basis for exploiting the cold, snow, ice and permafrost in the arrangement of transport links — overland and air routes — has been reviewed. As established, one of the key drivers for the development of research of this kind was the need to ensure accelerated industrial development of the Soviet North and respond to geopolitical challenges. Taken together, the findings showed that the cold holds considerable promise for the balanced development of northern communities and of humankind as a whole.

EARTH SECTION PATHS. SOLUTION TO THE INVERSE AND DIRECT PROBLEMS, AND WAYPOINTS WITHOUT ITERATIONS

The use of central elliptical sections in the calculation of air and sea routes and normal sections in Geodesy is common. Elliptic sections do not represent the shortest path between two points, although are often used in navigation to replace the geodesic lines. All developments include some kind of iteration to solve one of the problems, direct or inverse. When using vector algebra methods and perturbed series, the problems can be solved using an equidistant circle that represents the path of the elliptical section. This is possible because the flattening of the elliptical section is less than or equal to the Earth’s flattening, which implies that the series calculated for the terrestrial ellipsoid, used in the section, always converge. Three direct methods are described in order to calculate: the distance, the azimuth, the coordinates of a point and intermediate positions of an elliptical section. Those algorithms provide solutions to the inverse and direct algorithms with a consistency of the order of truncation error of double-type numbers.

Routes of Aircraft Cabin Air Contamination from Engine Oil, Hydraulic and Deicing Fluid

Purpose: This paper discusses potential contamination of the air in passenger aircraft cabins. It gives an overview of cabin air contamination basics. It further names possible contamination sources and possible routes of contamination. – Methodology: Evidence follows from a review of material found on the Internet and from the documentation of a visit to an aircraft recycling site. Parts were retrieved at the site and investigated later with more time. – Findings: Jet engine seals leak oil in small quantities. Metallic nanoparticles are found in the oil and have been detected in human fatty tissue of aviation workers. It has been observed that the potable water on board can also be contaminated. Oil traces have been found in bleed ducts, air conditioning components, and in air conditioning ducts. Deicing fluid and hydraulic fluid can find their way into the air conditioning system via the APU air intake. Fuel and oil also leak down onto the airport surfaces. These fluids can be ingested by the engine from the ground and can enter the air conditioning system from there. Entropy is the law of nature that states that disorder always increases. This is the reason, why it is impossible to confine engine oil and hydraulic fluids to their (predominantly) closed aircraft systems. This is why engine oil with metal nanoparticles hydraulic fluids, and deicing fluids eventually can go everywhere and finally into the human body. – Research Limitations: No measurements have been made. – Practical Implications: Awareness and prevention of contaminated cabin air can protect passengers and crew. – Social Implications: The exposure of contaminated cabin air provides a basis for a general discussion and shows that people should be alerted and need to act. New technologies need to be implemented such as a bleed free architecture. – Originality: This paper shows many original images of contaminated parts and air ducts between engine compressor and cabin air outlet. Own observations are combined with similar observations found in literature and online. The collected evidence is visualized in a diagram showing the routes of possible aircraft cabin air (and water) contamination.

Aircraft Ticket Price prediction using Machine Learning

Abstract: With ever increasing air route connectivity throughout the world, air travel has become a common, integral and faster way to travel. Predicting fares for airlines is an important as well as challenging task since a constant fluctuation in fares is observed and it is known to be dependent on varied set factors. With tremendous study in area, it is observed that using Machine Learning, Artificial Intelligence and Deep Learning techniques an estimation of flight fares at a given time can be obtained within seconds. In this paper, we use a Machine Learning Regression approach to predict flight fare by providing basic details of departure date and time, arrival time, source, destination, number of stops and name of the airline. The results show that Random Forest Regression Model provides highly optimal results. Keywords: Machine Learning, Random Forest, Prediction models, Airfare Prices, data analytics

What is driving the passenger demand on new regional air routes in India: A study using the gravity model

Are there unique determinants of air travel demand for regional routes? Is the passenger mix different for airlines flying these routes in developing countries? This paper attempts to answer these questions through a gravity model that identifies significant factors influencing demand for air travel on new routes connecting regional and remote locations. The data for the analysis is from India, where an ongoing regional connectivity scheme encourages the addition of new air routes to the national network. We estimate a multiple regression model with passenger demand as the response variable. The model tests some new predictors along with the traditional explanatory variables of gravity models. The results show that passengers find the saving in travel time due to the introduction of regional air service most attractive. Rather than the size of the population, the presence of prominent tertiary educational institutions at the origin or destination of a regional route is a more significant determinant of demand. This paper derives policy implications and opens new research questions.

Safety Performance Functions to predict Separation Minima Infringements in en-route airspace

The objective of this methodology is to characterise the safety of air routes under a systematic framework, in terms of Separation Minimum Infringements (SMI) between en-route aircraft based on models known as Safety Performance Functions (SPF). Bayesian Networks have been selected as techniques with high predictive capability and low probability event estimation. Moreover, they allow the integration of knowledge modelling with data inference. It is complex to develop a Bayesian Network model for SMI prediction. It is necessary to integrate the available knowledge about the precursors of SMIs together with the accumulated knowledge from the analysis of the collected data into a conceptual framework. This framework underpinning the Bayesian Network model focuses on the Closest Point of Approach (CPA) analysis and considers the general scenario of aircraft evolution in an air traffic sector. In order to translate this conceptual framework into a set of causal sub-networks, the ATM barrier and event tree models are used.

The Effect of Energy Level of Transport Layer on the Performance of Ambient Air Prepared Perovskite Solar Cell: A SCAPS-1D Simulation Study

The perovskite solar cell (PSC) as an emerging and promising type has been extensively studied. In this study, a model for a PSC prepared in ambient air was established by using SCAPS-1D. After that, it was further analyzed through varying the defect density of the perovskite absorber layer (Nt), the thin film thickness and energy-level matching between the electron transport layer (ETL), the perovskite absorber layer and the hole transport layer (HTL), for a better understanding of the carrier features. The Nt varied from 1.000 × 1011 to 1.000 × 1017 cm−3. The performance of the solar cell is promoted with improved Nt. When Nt is at 1.000 × 1015 cm−3, the carrier diffusion length reaches μm, and the carrier lifetime comes to 200 nm. The thickness of the absorber layer was changed from 200 to 600 nm. It is shown that the absorber layer could be prepared thinner for reducing carrier recombination when at high Nt. The thickness effect of ETL and HTL is weakened, since Nt dominates the solar cell performance. The effect of the affinity of ETL (3.4–4.3 eV) and HTL (2.0–2.7 eV), together with three energy-level matching situations “ETL(4.2)+HTL(2.5)”, “ETL(4.0)+HTL(2.2)” and “ETL(4.0)+HTL(2.5)” on the performance of the solar cell were analyzed. It was found that the HTL with valence band 0.05 eV lower than that of the perovskite absorber layer could have a blocking effect that reduced carrier recombination. The effect of energy-level matching becomes more important with improved Nt. Energy-level matching between the ETL and perovskite absorber layer turns out counterbalance characteristic on Jsc and Voc, and the “ETL(4.0)+HTL(2.5)” case can result in solar cell with Jsc of 27.58 mA/cm2, Voc of 1.0713 V, FF of 66.02% and efficiency of 19.51%. The findings would be very useful for fabricating high-efficiency and low-cost PSC by a large-scale ambient air route.

The determinants of air transport passenger demand of the Chinese and Taiwan aviation markets from Lisbon International Airport: an empirical modelling analysis

The aim of this article is to investigate the factors that enhance the development of new air routes, in order to expand the airport network, namely, to the Chinese and Taiwanese markets, from the Lisbon International Airport, from 2002 to 2009. An essay was designed to illustrate some potentialities of the use of concepts and methodologies related to statistical models in aeronautical management applications. We used two gravitational models comparatively, using panel data, pooled OLS estimation methods, fixed effects, and random effects, and the application of the circuit level analysis method on air routes from Brazil to Hong Kong via Lisbon. Statistical results have shown that the most important determinants of passenger demand for the Chinese and Taiwanese markets from Lisbon International Airport are gross domestic product, population, business factor and y2008 (calendar year 2008).