A genetic algorithm based stochastic programming model for air quality management

This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region, accounting for the dynamic and stochastic character of meteorological conditions. This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model. The model is demonstrated by using a realistic air urban scale SO 2 control problem in the Yuxi City of China. To evaluate effectiveness of the model, results of the approach are shown to compare with those of the linear deterministic procedures. This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents' health. Finally, a discussion of the areas for further research are briefly delineated.

SESAR:The Past,Present,and Future of European Air Traffic Management Research

The Single European Sky Air Traffic management(ATM)Research(SESAR)project is the technological pillar of the European Commission’s Single European Sky Initiative to modernize ATM.Here,we describe the process of establishing SESAR and the main parts of the project:the research and development(R&D)part,which is led by the SESAR Joint Undertaking;the deployment part,which is managed by the SESAR Deployment Manager;and the European ATM Master Plan,which collects and lays out both the R&D and deployment needs.The latest European ATM Master Plan was adopted just prior to the current pandemic.The huge loss in air traffic due to the pandemic,and the speed of the recovery of the aviation industry will require reprioritization,but the main elements that have been established-particularly those in support of the environment-remain valid.

Evolution of Air Traffic Management Concept of Operations and Its Impact on the System Architecture

The air traffic management system(ATM)has the task of ensuring safe,orderly and expeditious flow of air traffic.The ATM system architecture is very much dependent on the concept of operations(ConOps).Over the years the evolution in ConOps has resulted in changes in the ATM′s physical architecture,improving its physical infrastructure,increasing the levels of automation and making operational changes to improve air traffic flow,to cope with increasing demand for air travel.However,what is less clear is the impact of such changes in ConOps on the ATM′s functional architecture.This is vital for ensuring optimality in the implementation of the physical architecture components to support the ATM functions.This paper reviews the changes in the ConOps over the years,proposes a temporally invariant ATM functional model,and discusses some of the main key technologies expected to make significant improvements to the ATM system.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Ontology-Based Approach for Human Competency Gap Analysis in Air Traffic Management:A Case Study of Georgia

The Global Air Navigation Plan is a flexible global engineering approach that allows all States to advance their Air Navigation capacities based on their specific operational requirements.Aviation professionals have an essential role in the transition to,and successful implementation of the GANP.The research work is focused on the creation of methodology for the partial automation of the comparison competences of Air Traffic Management(ATM)personal and synthesis of training courses and modules,using a formal,ontology-based approach as a tool to solve these problems.One of the problems in the implementation of the GANP is that,on the one hand,there are currently no unified requirements for all categories of ATM personnel,and on the other hand,the development of ATM technologies is far ahead of the pace of training of personnel of appropriate qualifications.This problem becomes even more noticeable in countries that have just started an active modernization of ATC systems and do not have enough experience in this field.The paper describes the general methodological approach based on the education ontology modelling for human competency gap analysis in ATM and for gap analysis between the university curricula outcomes and the ATM requirements.The ontology of key personnel competencies issues for the design and integration of large-scale future ATM programmes is proposed.

Trajectory clustering for arrival aircraft via new trajectory representation

Trajectory clustering can identify the flight patterns of the air traffic,which in turn contributes to the airspace planning,air traffic flow management,and flight time estimation.This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation.The proposed method consists of four significant steps:representing the trajectories,grouping the trajectories based on the new representation,measuring the similarities between different trajectories through dynamic time warping(DTW)in each group,and clustering the trajectories based on k-means and densitybased spatial clustering of applications with noise(DBSCAN).We take the inbound trajectories toward Shanghai Pudong International Airport(ZSPD)to carry out the case studies.The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns,but also improve the performance of flight time estimation.

Analysis on Potential Conflict Frequency of Intersected Air Routes in Terminal Airspace Design

In order to obtain accurate conflict risks in terminal airspace design,the concept and calculation model of potential conflict frequency for intersected routes are proposed.Conflict frequency is represented by the product of horizontal conflict frequency and vertical conflict probability.The horizontal conflict frequency is derived from the probability density distribution of conflicts in a period of time.Based on the recorded radar trajectory data,the concept and model of ROUTE distance are proposed,and the probability density function of aircraft height at a specified ROUTE distance is deduced by kernel density estimation.Furthermore,vertical conflict probability and its horizontal distribution are achieved.Examples of three intersected arrival and departure route design schemes are studied.Compared with scheme 1,the conflict frequency values of the other two improved schemes decrease to53% and 24%,respectively.The results show that the model can quantify potential conflict frequency of intersected routes.

A Combined Arrival and Departure Scheduling for Multi-airport System

A combined arrival and departure scheduling problem is investigated for multi-airport system to alleviate the problem of airspace congestion and flight delay.Firstly,the combined scheduling problem for multi-airport system is defined through in-depth analysis of the characteristics of arrival and departure operations.Then,several constraints are taken into account,such as wake vortex separation,transfer separation,release separation,and separation in different runway operational modes.Furthermore,the scheduling model is constructed and simulated annealing algorithm is proposed by minimizing the total delay.Finally,Shanghai multi-airport system is chosen to conduct the simulation and validation.And the simulation results indicate that the proposed method is able to effectively improve the efficiency of arrival and departure operations for multi-airport system.

Identification of Information-Seeking Behaviors from Air Traffic Controllers′ Eye Movements

Air traffic controllers are the important parts of air traffic management system who are responsible for the safety and efficiency of the system.They make traffic management decisions based on information acquired from various sources.The understanding of their information seeking behaviors is still limited.We aim to identify controllers′ behavior through the examination of the correlations between controllers′eye movements and air traffic.Sixteen air traffic controllers were invited to participate real-time simulation experiments,during which the data of their eye ball movements and air traffic were recorded.Tweny-three air traffic complexity metrics and six eye movements metrics were calculated to examine their relationships.Two correlational methods,Pearson′s correlation and Spearman′s correlation,were tested between every eye-traffic pair of metrics.The results indicate that controllers′two kinds of information-seeking behaviors can be identified from their eye movements:Targets tracking,and confliction recognition.The study on controllers′ eye movements may contribute to the understanding of information-seeking mechanisms leading to the development of more intelligent automations in the future.

Dynamic reconfiguration for en-route airspace

As the air traffic demand is anticipated to be increased significantly in the near future,dynamic and effective allocation of the airspace resource is becoming a world-wide focus in the research field of air traffic management（ATM）.Taking the U.S.targeting the en-route airsapce,a dynamic airspace configuration（DAC） algorithm to reconfigure the airspace in consideration of higher efficiency and safety is presented.First,a modeling technique based on graph theory is proposed to generate a mathematical model for the airspace,and then,the graph model is partitioned into subgraphs for the purpose of sectorizatoin.The final step generates sector configuration with desirable geometry shape.Through analysis on the Cleveland airspace center（ZOB） in the U.S.,the algorithm is proved to be robust to time-varying traffic load.

Sectorization Model of Terminal Airspace with Arrival and Departure Separation

Terminal airspace(TMA)is the airspace centering several military and civil aviation airports with complex route structure,limited airspace resources,traffic flow,difficult management and considerable airspace complexity.A scientific and rational sectorization of TMA can optimize airspace resources,and sufficiently utilize the control of human resources to ensure the safety of TMA.The functional sectorization model was established based on the route structure of arriving and departing aircraft as well as controlling requirements.Based on principles of sectorization and topological relations within a network,the arrival and departure sectorization model was established,using tree based ant colony algorithm(ACO)searching.Shanghai TMA was taken as an example to be sectorizaed,and the result showed that this model was superior to traditional ones when arrival and departure routes were separated at dense airport terminal airspace.

Air Quality Trends in Metropolitan Zones in Veracruz, México

Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring stations installed in Xalapa and Minatitlan MZ in 2013-year. The methodology applied used quality criteria to the datasets, followed by data validation and statistics for further analysis to determine the hourly, weekly and yearly trends of NO2, O3, SO2, PM10 and PM2.5. Indicators were compared with Mexican standards, CAI-LAC report, WHO guidelines, EU and USA standards to evaluate the AQ in both sites. We observed AQ trends from moderate to bad in Xalapa and Minatitlan MZ where the PM10 and PM2.5 surpassed the WHO guidelines and Mexican standards. O3 and SO2 in Xalapa presented a quality from good to moderate and in Minatitlan sometimes were from moderate to bad. NO2 did not exceed the value limits of Mexican standards, only Xalapa has exceeded the WHO guidelines. In Minatitlan, the Mexican limits were not exceeded. Concluding, PM10 and PM2.5 concentrations were the main problem. Others pollutants that influenced the AQ were O3, NO2 and SO2 in Minatitlan MZ due probably to meteorology, site conditions, location and oil and petrochemical industries. In Xalapa, MZ NO2 and SO2 are attributed mainly to road transport.

INTELLIGENT SIMULATION FOR ALTERNATIVES COMPARISON AND APPLICATION TO AIR TRAFFIC MANAGEMENT

We present a simulation run allocation scheme for improving efficiency in simulation experiments for decision making under uncertainty. This scheme is called Optimal Computing Budget Allocation (OCBA). OCBA advances the state-of-the-art by intelligently allocating a computing budget to the candidate alternatives under evaluation. The basic idea is to spend less computational effort on simulating non-critical alternatives to save computation cost. In particular, OCBA is employed to intelligently provide the smallest number of simulation runs for a desired accuracy. In this paper, we present a new and more general OCBA scheme which can consider cases that users are interested not only the best design, but also any one in a good design set. In addition, this paper also presents the application of our OCBA to a design problem in US air traffic management. The national air traffic system in US is modeled as a large, complex, and stochastic network. The numerical examples show that the computation time can be reduced by 54% to 88% with the use of OCBA.

A new skeleton based flying bird detection method for low-altitude air traffic management

In low-altitude air traffic management, non-cooperation targets are the greatest threat to security of low-flying aircraft. Among various aviation fatalities, flying bird is the main factor with the highest risk and directs economic losses amounted to nearly 10 billion US dollars each year.Therefore, Flying Bird Detection(FBD) has attracted considerable attention in low-altitude air traffic management. In this paper, we propose a skeleton based FBD method via describing bird motion information with a set of key poses. To overcome the variability of birds, the skeleton feature is selected as a relatively fixed and common characteristic for the pose appearance of flying bird. Based on the geometric topology among some key parts of bird body, a set of key poses can be described by some extracted skeleton features, which are used to represent the bird motion information. Aimed at robustly handling with the pose variations, multiple pose-specific classifiers are individually trained to learn the representative poses of the flying bird. At the detection stage,the flying bird skeleton features are combined with extracted key-pose sets to perform the flying bird classification task from each image. Afterwards, the key-frame pose-change set and the consistency of the classification results from sequent images are employed to validate the final detection results.Experiments on flying bird datasets demonstrate the effectiveness and efficiency of the proposed method.

Dynamic Air Route Open-Close Problem for Airspace Management

Dynamic airspace management plans and assigns airspace resources to airspace users on demand to increase airspace capacity. Although many studies of air traffic flow management （ATFM） have sought to optimally allocate air traffic to get the best use of given airspace resources, few studies have focused on how to build an efficient air traffic network or how to adjust the current network in real time. This paper presents an integer program model named the dynamic air route open-close problem （DROP）. DROP has a cost-based objective function which takes into account constraints such as the shortest occupancy time of routes, which are not considered in ATFM models. The aim of DROP is to determine which routes will be opened to a certain user during a given time period. Simulation results show that DROP can facilitate utilization of air routes. DROP, a simplified version of an air traffic network constructing problem, is the first step towards realizing dynamic airspace management. The combination of ATFM and DROP can facilitate decisions toward more reasonable, efficient use of limited airspace resources.

Developing China's roadmap for air quality improvement:A review on technology development and future prospects

Air pollution control policies in China have been experiencing profound changes,highlighting a strategic transformation from total pollutant emission control to air quality improvement,along with the shifting targets starting from acid rain and NO_(x)emissions to PM_(2.5)pollution,and then the emerging O_(3)challenges.The marvelous achievements have been made with the dramatic decrease of SO_(2)emission and fundamental improvement of PM_(2.5)concentration.Despite these achievements,China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality,which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas(GHG)emissions.Against this background,an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China’s long-term air quality improvement strategy.This paper systematically reviews the technical system for developing the air quality improvement roadmap,which was integrated from the research output of China’s National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies(hereafter Special NKP),covering mid-and long-term air quality target setting techniques,quantitative analysis techniques for emission reduction targets corresponding to air quality targets,and pathway optimization techniques for realizing reduction targets.The experience and lessons derived from the reviews have implications for the reformation of China’s air quality improvement roadmap in facing challenges of synergistic mitigation of PM_(2.5)and O_(3),and the coupling with climate change mitigation.

Characterizing Flight Delay Profiles with a Tensor Factorization Framework

In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.

Accurate Multi-Site Daily-Ahead Multi-Step PM_(2.5)Concentrations Forecasting Using Space-Shared CNN-LSTM

Accurate multi-step PM_(2.5)(particulate matter with diameters≤2.5 um)concentration prediction is critical for humankinds’health and air populationmanagement because it could provide strong evidence for decisionmaking.However,it is very challenging due to its randomness and variability.This paper proposed a novel method based on convolutional neural network(CNN)and long-short-term memory(LSTM)with a space-shared mechanism,named space-shared CNN-LSTM(SCNN-LSTM)for multi-site dailyahead multi-step PM_(2.5)forecasting with self-historical series.The proposed SCNN-LSTM contains multi-channel inputs,each channel corresponding to one-site historical PM_(2.5)concentration series.In which,CNN and LSTM are used to extract each site’s rich hidden feature representations in a stack mode.Especially,CNN is to extract the hidden short-time gap PM_(2.5)concentration patterns;LSTM is to mine the hidden features with long-time dependency.Each channel extracted features aremerged as the comprehensive features for future multi-step PM_(2.5)concentration forecasting.Besides,the space-shared mechanism is implemented by multi-loss functions to achieve space information sharing.Therefore,the final features are the fusion of short-time gap,long-time dependency,and space information,which enables forecasting more accurately.To validate the proposed method’s effectiveness,the authors designed,trained,and compared it with various leading methods in terms of RMSE,MAE,MAPE,and R^(2)on four real-word PM_(2.5)data sets in Seoul,South Korea.The massive experiments proved that the proposed method could accurately forecast multi-site multi-step PM_(2.5)concentration only using self-historical PM_(2.5)concentration time series and running once.Specifically,the proposed method obtained averaged RMSE of 8.05,MAE of 5.04,MAPE of 23.96%,and R^(2)of 0.7 for four-site daily ahead 10-hourPM_(2.5)concentration forecasting.

Identifying and managing risks of AI-driven operations:A case study of automatic speech recognition for improving air traffic safety

In this work,the primary focus is to identify potential technical risks of Artificial Intel-ligence(AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience.The case study is performed to evaluate the AI-driven techniques and applications using objective metrics,in which several risks and technical facts are obtained to direct future research.Considering the safety–critical specificities of the air traffic control system,a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview.In this procedure,the potential risks obtained from the case study are confirmed,and the impacts on human working are considered.Both the case study and the evaluation of user experience provide compatible results and conclusions:(A)the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance;(B)the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers.Finally,a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.

Accurate prediction of air quality response to emissions for effective control policy design

Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3and PM2.5and the emissions of their precursors.However,the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O3and PM2.5,which are formed by multiple precursors under complex physical and chemical processes;this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions.Different from the traditional sensitivity analysis and source apportionment methods,the reduced form models(RFMs)based on chemical transport models(CTMs)are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost.Here we review recent approaches used in RFMs and compare their structures,advantages and disadvantages,performance and applications.In general,RFMs are classified into three types including(1)sensitivity-based models,(2)models with simplified chemistry and physical processes,and(3)statistical models,with considerable differences in principles,characteristics and application ranges.The prediction of nonlinear responses by RFMs enables more in-depth analysis,not only in terms of real-time prediction of concentrations and quantification of human exposure,health impacts and economic damage,but also in optimizing control policies.Notably,data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management.In future studies,improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses.Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes,which pose great challenges to the quantification of response relationships.Additionally,with rising requirements for fine-scale air quality management,improving the performance of urban-scale simulations is worth considering.In short,accurate predictions of the response of air quality to emissions,though challenging,holds great promise for the present as well as for future scenarios.