Search results

Airspace sectorization is an important task, which has a significant impact in the everyday work of air control services. Especially in recent years, because of the constant increase in air traffic, existing airspace sectorization techniques have difficulties to tackle the large air traffic volumes, creating imbalanced sectors and uneven workload distribution among sectors. The purpose of this paper is to propose a new approach to find optimal airspace sectorization balancing the traffic controller workload between sectors, subject to airspace requirements.

A constraint programming (CP) model called equitable airspace sectorization problem (EQASP) relies on ordered weighted averaging (OWA) multiagent optimization and the parallel portfolio architecture has been developed, which integrates the equity into an existing CP approach (Trandac et al., 2005). The EQASP was evaluated and compared with the method of Trandac et al. (2005), according to the quality of workload balancing between sectors and the resolution performance. The comparison was achieved using real air traffic low-altitude network data sets of French airspace for five flight information regions for 24 h a day and the Algerian airspace for three various periods (off peak hours, peak hours and 24 h).

It has been demonstrated that the proposed EQASP model, which is based on OWA multicriteria optimization method, significantly improved both the solving performance and the workload equity between sectors, while offering strong theoretical properties of the balancing requirement. Interestingly, when solving hard instances, our parallel sectorization tool can provide, at any time, a workable solution, which satisfies all geometric constraints of sectorization.

This study can be used to design well-balanced air sectors in terms of workload between control units in the strategic phase. To fulfil the airspace users’ constraints, one can refer to this study to assess the capacity of each air sector (especially the overloaded sectors) and then adjust the sector’s shape to respond to the dynamic changes in traffic patterns.

This theoretical and practical approach enables the development and support of the definition of the “Air traffic management (ATM) Concept Target” through improvements in human factors specifically (balancing workload across sectors), which contributes to raising the level of capacity, safety and efficiency (SESAR Vision of ATM 2035).

In their approach, the authors proposed an OWA-based multiagent optimization model, ensuring the search for the best equitable solution, without requiring user-defined balancing constraints, which enforce each sector to have a workload between two user-defined bounds (W_min, W_max).

Modeling and system performance analysis play an important role in the planning of air traffic system. In particular, this paper aims to concentrate on the modeling and the performance evaluation of air transportation system.

It has shown that system matrices inherent in the airspace network can be acquired based on the service times of flights. This paper deals with a logical modeling which can avoid temporal synchronization conflict of resources. As a result, a class of queuing system is investigated to obtain a representation of the airspace network dynamics using dioid model.

The analysis of the air traffic system is conducted by solving the system state model. This indicates that it is feasible to use eigenvalues of system model for acquiring characteristics of the considered air transport systems.

The departure scheduling prototype presented in this paper can offer air traffic controller a decision support tool to build optimal departure sequences for aircraft.

The properties of max-plus algebra described allow us to apply linear algebra concepts like eigenvalue and eigenvectors to obtain a solution to the air traffic system being modeled. By solving and analyzing the dioid model, this paper evaluated some performance measures of airspace network.

This paper aims to analyze the direction and balance of transnational information flows and look at how nonpriced digital information exchanges related to international trade in goods and services.

The authors obtained quantitative data about Web-related data flows between countries and regions using Telegeography data on “Server Location as a Percentage of Top Websites.” They then explore how those flows are correlated to trade in goods.

Web traffic is highly transnational. More than half of the top 100 websites in 9 of the world’s 13 sub-regions are hosted in the USA. More than 15 per cent of the top 100 websites in 9 of the 13 subregions are hosted in Western Europe. East Asia has the largest negative balance in the relationship between incoming and outgoing Web requests. The authors found a very strong negative correlation (−0.878) between Web traffic balances and the balance of trade in goods across all subregions. A similarly strong positive correlation was found with services trade; however, the incompleteness of the data does not allow for strong conclusions yet.

Further research is needed to correlate Web traffic flows with capital flows. The authors also do not have a well-developed theory to explain the strong negative correlation between information flows and goods trade.

The data and analysis have useful implications for digital economy policy. It indicates that digital protectionism of the sort practice by China may succeed in increasing domestic producers’ share of Web requests, but does not make them globally competitive. The strong negative correlation between the balance of unpriced Web information and the balance of trade in goods indicates interdependence rather than domination, challenging narratives that information flow imbalances are caused by market power of the big platforms.

The paper demonstrates the degree to which unpriced digital exchanges are transnational and how various countries are more or less globally competitive in the supply of information that the rest of the world finds attractive.

No other published papers have used the data on website traffic data, and previous research has not explored empirically the correlation between information flows and goods trade.

The purpose of this paper is to understand the changes in Swedish and Finnish companies' traffic flows and evaluate the future prospects.

Research was implemented through web‐based questionnaire. Furthermore, numerous second‐hand sources were used to gain knowledge. Research was conducted in three parts: first research was executed in 2006, second in 2009 and the third was realized in 2010.

The main findings support previous studies arguing that the traffic between Europe and China will continue to grow in the future. However, contrary to earlier studies, the balance will change from eastbound to westbound traffic. The future prospects to Russian traffic are stated as a slight increase in demand: however, the balance is shifting from eastbound to westbound transport, and it is noted that the transport flows from Russia to Europe might increase in the near future.

Empirical data were gathered from two North European countries, Sweden and Finland. To follow the development in the market, and in order to make more general conclusions, research should be extended to include other countries. Furthermore, economic downturn's influences on traffic flows and its development could be analyzed in a year's time.

The research aggregates data from three surveys and evaluates the companies' standpoints. The study results could be used to evaluate the companies' development trends in Finland and Sweden. In addition, the research provides valuable data for the business world, as well as for academia, by adducing the market actors' outlook.

The paper contributes actor‐level data to the subject, which previously has been scrutinized mainly via second‐hand data and literature analyses.

Considering the varied and dynamic workload of vessel traffic service (VTS) operators, design an adaptive rotating shift solution to prevent them from getting tired while ensuring continuous high-quality services and finally guarantee a benign maritime traffic environment.

The problem of rotating shift in VTS and its influencing factors are analyzed first, then the framework of automatic identification system (AIS) data analytics is proposed, as well as the data model to extract spatial–temporal information. Besides, K-means-based anomaly detection method is adjusted to generate anomaly-free data, with which the traffic trend analysis and prediction are made. Based on this knowledge, strategies and methods for adaptive rotating shift design are worked out.

In VTS, vessel number and speed are identified as two most crucial factors influencing operators' workload. Based on the two factors, the proposed data model is verified to be effective on reducing data size and improving data processing efficiency. Besides, the K-means-based anomaly detection method could provide stable results, and the work shift pattern planning algorithm could efficiently generate acceptable solutions based on maritime traffic information.

This is a pioneer work on utilizing maritime traffic data to facilitate the operation management in VTS, which provides a new direction to improve their daily management. Besides, a systematic data-driven solution for adaptive rotating shift is proposed, including knowledge discovery method and decision-making algorithm for adaptive rotating shift design. The technical framework is flexible and can be extended for managing other activities in VTS or adapted in diverse fields.

Tourist traffic is so closely linked with the general state of economic activity, that it will be of interest to compare it with some other economic barometer. A good example is foreign trade, especially in countries dependent upon international exchange of goods. In those countries foreign trade plays a decisive role in their entire economy and emphasis is laid on its connection with employment, which is considerably influenced by imports of raw materials and exports of finished goods. Thus foreign trade can be an actual indicator of overall economic prosperity, so that variations in foreign trade activity must have a most far‐reaching influence on the value of tourist traffic, which will tend to expand and contract in sympathy with variations of economic activity in general and of foreign trade in particular.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

The purpose of this paper is to expound the concept of the coordinated development of the transport system, and provide a quantitative method to support decision making in urban transportation development planning.

To analyze the organic composition of urban transport system, research the meaning and mechanism of coordinated development of transport system. On the basis of researching relations of transport and external systems and its subsystems, to research quantitative theoretical model of the coordinated development and assessment of the transport system.

Establishing models needs to analyze the coordination among the various indices of subsystem, calculate the overall coordination between indices of the subsystem and all the indices of other subsystems, then inspect the coordinated development among subsystems.

How to design the indexes of the subsystem of urban transport system is the main limitation.

Offering theoretical foundation for coordinated development of urban transport system.

Putting forward quantitative theoretical model on coordinated development of urban transport system, which lays a foundation for further study.

In multihop wireless networks, the number of neighbors has an important role in the network performance since links are dynamically formed between a node and its neighbors. This paper aims to investigate this issue.

The paper quantitatively studies the effects of the average number of neighbors in multihop wireless networks on the network connectivity, the number of hops needed to traverse a certain distance, which can be used to determine the hop diameter of a network, and the total energy consumed by packet transmission, which can be used to choose an optimum average number of neighbors that minimizes the energy consumption. This paper also presents an analysis of the energy consumption that can be applied to a wide range of access protocols and show the effect of a variety of factors.

Results show that the minimum average number of neighbors to guarantee the overall network connectivity depends on the size of a network coverage. There is a sharp knee in the network connectivity with decrease of the average number of neighbors, N. If the distance between a source and destination, d, is known, the number of hops needed to reach the destination is usually between d/R∼2d/R, where R is the transmission range. A larger average number of neighbors N leads to a smaller number of hops to traverse a certain distance, which in turn results in a smaller traffic load caused by relaying packets. However, a bigger N also causes more collisions when a contention medium access scheme is used, which leads to more energy consumed by packet transmission. The results show that the optimum N which minimizes the energy is obtained by balancing several factors affecting the energy.

The paper provides a useful study on the effects of the number of neighbors in multihop wireless networks.