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.

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.

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.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Methodology for Automatically Setting Camera View to Mile Marker for Traffic Incident Management

Traffic incident management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile marker based, with cameras that operate in a Pan-Tilt-Zoom coordinate system relies on dispatchers having detailed knowledge for hundreds of cameras and perhaps some presets. During real-time incident dispatching, reducing the time it takes to identify the most relevant cameras and setting their view on the incident is an important opportunity to improve incident management dispatch times. This research develops a camera-to-mile marker mapping technique that automatically sets the camera view to a specified mile marker within the field-of-view of the camera. Over 350 traffic cameras along Indiana’s 2250 directional miles of interstate were mapped to approximately 5000 discrete locations that correspond to approximately 780 directional miles (~35% of interstate) of camera coverage. This newly developed technique will allow operators to quickly identify the nearest camera and set them to the reported location. This research also identifies segments on the interstate system with limited or no camera coverage for decision makers to prioritize future capital investments. This paper concludes with brief discussion on future research to automate the mapping using LiDAR data and to set the cameras after automatically detecting the events using connected vehicle trajectory data.

Traffic Incident Management Performance Measures: Ranking Agencies on Roadway Clearance Time

This study develops a procedure to rank agencies based on their incident responses using roadway clearance times for crashes. This analysis is not intended to grade agencies but to assist in identifying agencies requiring more training or resources for incident management. Previous NCHRP reports discussed usage of different factors including incident severity, roadway characteristics, number of lanes involved and time of incident separately for estimating the performance. However, it does not tell us how to incorporate all the factors at the same time. Thus, this study aims to account for multiple factors to ensure fair comparisons. This study used 149,174 crashes from Iowa that occurred from 2018 to 2021. A Tobit regression model was used to find the effect of different variables on roadway clearance time. Variables that cannot be controlled directly by agencies such as crash severity, roadway type, weather conditions, lighting conditions, etc., were included in the analysis as it helps to reduce bias in the ranking procedure. Then clearance time of each crash is normalized into a base condition using the regression coefficients. The normalization makes the process more efficient as the effect of uncontrollable factors has already been mitigated. Finally, the agencies were ranked by their average normalized roadway clearance time. This ranking process allows agencies to track their performance of previous crashes, can be used in identifying low performing agencies that could use additional resources and training, and can be used to identify high performing agencies to recognize for their efforts and performance.

Prediction based traffic management in a metropolitan area

In recent years,modern metropolitan areas are the main indicators of economic growth of nation.In metropolitan areas,number and frequency of vehicles have increased tremendously,and they create issues,like traffic congestion,accidents,environmental pollution,economical losses and unnecessary waste of fuel.In this paper,we propose traffic management system based on the prediction information to reduce the above mentioned issues in a metropolitan area.The proposed traffic management system makes use of static and mobile agents,where the static agent available at region creates and dispatches mobile agents to zones in a metropolitan area.The migrated mobile agents use emergent intelligence technique to collect and share traffic flow parameters(speed and density),historical data,resource information,spatio-temporal data and so on,and are analyzes the static agent.The emergent intelligence technique at static agent uses analyzed,historical and spatio-temporal data for monitoring and predicting the expected patterns of traffic density(commuters and vehicles)and travel times in each zone and region.The static agent optimizes predicted and analyzed data for choosing optimal routes to divert the traffic,in order to ensure smooth traffic flow and reduce frequency of occurrence of traffic congestion,reduce traffic density and travel time.The performance analysis is performed in realistic scenario by integrating NS2,SUMO,OpenStreatMap(OSM)and MOVE tool.The effectiveness of the proposed approach has been compared with the existing approach.

A Brief Introduction of Air Traffic Management

Air Traffic Management (ATM) started publication in 1995 by Air Traffic Management Bureau of CAAC. It is the first magazine about ATM field in CAAC. The chairman of Editorial Board of ATM is Mr. Bao Peide, Vice Minister of CAAC. Mr. Chen Ziye, Vice President of the 1st Research Institute of CAAC and Chen Xuhua, General Director of Air Traffic Management Bureau are vice chairmen of the editorial board, and six general directors of local air traffic management bureau and some professors and experts are members of the editorial board.

A Brief Introduction of Air Traffic Management

Air Traffic Management(ATM)started publication in 1995 by Air Traffic Management Bureauof CAAC.It is the first magazine about ATM field in CAAC.The chairman of Editorial Board ofATM is Mr.Bao Peide,Vice Minister of CAAC.Mr.Chen Ziye,Vice President of the 1st ResearchInstitute of CAAC and Chen Xuhua,General Director of Air Traffic Management Bureau are vicechairmen of the editorial board,and six general directors of local air traffic management bureau andsome professors and experts are members of the editorial board.ATM includes more than 20 columns,which are communication,navigation,surveillance,air trafficmanagement,special articles(policy,planning and management of implementation of

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

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.

KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in Smart Cities

Simulation is a powerful tool for improving,evaluating and analyzing the performance of new and existing systems.Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail.There are many types of traffic simulators that allow simulating traffic in modern cities.The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner.In many cities of Saudi Arabia,traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents.Unfortunately,employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors.Commercial simulators are usually expensive,closed source and inflexible as they allow limited functionalities.In this project,we developed a local traffic simulator“KSUtraffic”for traffic modeling,planning and analysis with respect to different traffic control strategies and considerations.We modeled information specified by GIS and real traffic data.Furthermore,we designed experiments that manipulate simulation parameters and the underlying area.KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.

Excavation of the Internet of Things in Urban Areas Based on an Intelligent Transportation Management System

Transportation, as one of the most common aspects people use in their daily lives, has resulted in highly complex traffic in urban areas due to the large number of private vehicles. As some results of the traffic congestion, there is energy consumption, environmental pollution, unplanned accidents, and time is wasted due to congestion and traffic jams. With the aid of the Internet of Things (IoT), which is an excellent computerized technology solution for all field claims, Internet of Things (IoT) technology has recently provided an efficient and effective traffic management system, especially in transportation, due to the combined functions IoT can handle, there are management, monitoring, tracking, identifying, computing, and so on. This article provided a comprehensive overview of a variety of intelligent management systems that have been built using IoT to alleviate traffic congestion.

Real-time high-speed train rescheduling based on a Human-Computer Interaction framework

During railway operations,trains normally run as scheduled,but the occurrence of unexpected events will disrupt traffic flow and cause train deviation from the original timetable.In order to assist dispatchers in rescheduling trains,this paper introduces an innovative Human-Computer Interaction framework.This framework enables train dispatchers to propose different timetable adjustment instructions to the original or adjusted timetable.These instructions will be processed,stored,analyzed,and digested by computer program,which finally lead to the modification and calculation of the embedded mathematical model,then a new adjusted timetable will be produced and provided to dispatchers for checking and modifying.This framework can iterate for unlimited times based on dispatchers'intentions,until the final results satisfy them.A demonstration system named RTARS(Real-time Timetable Automatic Rescheduling System)is developed based on this framework and it has been applied in Beijing Railway Administration,which shows its effectiveness in reality.

SDN-Enabled Content Dissemination Scheme for the Internet of Vehicles

The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness.

Urban Accessibility in Motorcycle Dependent Cities - Case Study in Ho Chi Minh City, Vietnam

Motorcycle dependent cities have specific characteristics in terms of urban accessibility.A rapid increase in the number of motorcycles and other private motorized modes make transport problems more serious and cause severe capacity problems for the infrastructure systems in these cities.Therefore,it is necessary to optimize the development of different modes to meet travel demand and to ensure accessibility in all urban areas.This paper aims to explore accessibility conditions in Ho Chi Minh City,a typical motorcycle dependent city in Vietnam.Understanding of accessibility could be the key element for urban planning in Ho Chi Minh City in particular and motorcycle dependent cities in general.Then,management measures for motorcycles and competitive modes will be proposed to improve the accessibility conditions and thus support sustainable urban transport development for motorcycle dependent cities.

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.