• Title/Summary/Keyword: Traffic network model

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Effect of the Variable Packet Size on LRD Characteristic of the MMPP Traffic Model

  • Lee, Kang-Won;Kwon, Byung-Chun
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.1B
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    • pp.17-24
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    • 2008
  • The effect of the variable packet size on the LRD characteristic of the MMPP traffic model is investigated. When we generate packet traffic for the performance evaluation of IP packet network, MMPP model can be used to generate packet interarrival time. And a random length of packet size from a certain distribution can be assigned to each packet. However, there is a possibility that the variable packet size might change the LRD characteristic of the original MMPP model. In this study, we investigate this possibility. For this purpose the 'refined traffic' is defined, where packet arrival time is generated according to the MMPP model and a random packet length from a specific distribution is assigned to each generated packet. Hurst parameter of the refined traffic is estimated and compared with the original Hurst parameter, which is the input parameter of the MMPP model. We also investigate the effect of the packet size distribution on the queueing performance of the MMPP traffic model and the relationship between the Hurst parameter and queueing performance.

Hierarchical Optimal Control of Urban Traffic Networks

  • Park, Eun-Se
    • ETRI Journal
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    • v.5 no.2
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    • pp.17-28
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    • 1983
  • This paper deals with the problem of optimally controlling traffic flows in urban transportation traffic networks. For this, a nonlinear discrete-time model of urban traffic network is first suggested in order to handle the phenomenon of traffic flows such as oversaturatedness and/or undersaturatedness. Then an optimal control problem is formulated and a hierarchical optimization technique is applied, which is based upon a prediction-type two-level method of Hirvonen and Hakkala.

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A Study of Sensitivity Analysis and Traffic Performance by Competition in the Ad hoc Network (애드혹(Ad hoc) 네트워크에서 경쟁에 의한 트래픽성능 및 민감성 분석)

  • Cho, Hyang-Duck;Kim, Woo-Shik
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.47 no.3
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    • pp.39-49
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    • 2010
  • Ad hoc network is infrastructureless network, that the network topology is configured by each node. Each node capacity device function namely, accomplishes a network control and a function of management because not being Network device of the exclusively. Ad hoc networks what kind of node join or leave the network topology to facilitate the expansion of arbitrary topology, to the case which is the distance whose traffic is distant the place must overtake through the route of intermediate nodes, like these facts give an effect to performance fluctuation. Consequently, each node in order to maintain traffic of oneself stably there is a necessity which will consider traffic and congestion control from the node which traffic of a condition and oneself of traffic of the circumference and the route which compose the network will overtake through. This paper assumes the path of the band with a finite resource, and path traffic to occupy the competition and its impact on transmission performance of these competing arguments to configure the model factor to analyse the performance impact on the results presented. Like this result with the fact that from the study, it will contribute in network management policy and the technique.

A Dynamic Priority-based QoS Control Scheme for Wireless Mobile Networks

  • Kang, Moon-Sik
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.57-60
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    • 2005
  • In this paper, a dynamic priority-based QoS (DPQoS) provision scheme is proposed for the required QoS from one end of the network to the other in wireless mobile networks. The DPQoS model is used to meet diversity multimedia traffic requirements. This model is come up with a framework for the wireless network of which consists of a core-IP network and also a number of wireless access networks. For the true end-to-end QoS, it is required that the core network is able to support the required QoS for the wireless users. This paper shows a solution to optimize the performance for different traffic classes according to the traffic characteristics. The performance of the proposed scheme is evaluated at delay aspects such as delay and throughput.

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Performance Analysis of ABR Congestion Control Algorithm using Self-Similar Traffic

  • Kim, Dong-Il;Jin, Sung-Ho
    • Journal of information and communication convergence engineering
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    • v.2 no.1
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    • pp.15-21
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    • 2004
  • One of the most important issues in designing a network and realizing a service is dealing with traffic characteristics. Recent experimental research on LAN, WAN, and VBR traffic properties has highlighted that real traffic specificities can not be displayed because the current models based on the Poisson assumption under estimate the long range dependency of network traffic and self-similar peculiarities. Therefore, a new approach using self-similarity characteristics as a real traffic model was recently developed. In This paper we discusses the definition of self-similarity traffic. Moreover, real traffic was collected and we generated self-similar data traffic like real traffic to background load. On the existing ABR congestion control algorithm transmission throughput with the representative ERICA, EPRCA and NIST switch algorithm show the efficient reaction about the burst traffic.

Traffic Flow Prediction with Spatio-Temporal Information Fusion using Graph Neural Networks

  • Huijuan Ding;Giseop Noh
    • International journal of advanced smart convergence
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    • v.12 no.4
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    • pp.88-97
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    • 2023
  • Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

Development of Traffic Congestion Prediction Module Using Vehicle Detection System for Intelligent Transportation System (ITS를 위한 차량검지시스템을 기반으로 한 교통 정체 예측 모듈 개발)

  • Sin, Won-Sik;Oh, Se-Do;Kim, Young-Jin
    • IE interfaces
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    • v.23 no.4
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    • pp.349-356
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    • 2010
  • The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.

Models for Internet Traffic Sharing in Computer Network

  • Alrusaini, Othman A.;Shafie, Emad A.;Elgabbani, Badreldin O.S.
    • International Journal of Computer Science & Network Security
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    • v.21 no.8
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    • pp.28-34
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    • 2021
  • Internet Service Providers (ISPs) constantly endeavor to resolve network congestion, in order to provide fast and cheap services to the customers. This study suggests two models based on Markov chain, using three and four access attempts to complete the call. It involves a comparative study of four models to check the relationship between Internet Access sharing traffic, and the possibility of network jamming. The first model is a Markov chain, based on call-by-call attempt, whereas the second is based on two attempts. Models III&IV suggested by the authors are based on the assumption of three and four attempts. The assessment reveals that sometimes by increasing the number of attempts for the same operator, the chances for the customers to complete the call, is also increased due to blocking probabilities. Three and four attempts express the actual relationship between traffic sharing and blocking probability based on Markov using MATLAB tools with initial probability values. The study reflects shouting results compared to I&II models using one and two attempts. The success ratio of the first model is 84.5%, and that of the second is 90.6% to complete the call, whereas models using three and four attempts have 94.95% and 95.12% respectively to complete the call.

Network Routing by Traffic Prediction on Time Series Models (시계열 모형의 트래픽 예측에 기반한 네트워크 라우팅)

  • Jung, Sang-Joon;Chung, Youn-Ky;Kim, Chong-Gun
    • Journal of KIISE:Information Networking
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    • v.32 no.4
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    • pp.433-442
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    • 2005
  • An increase In traffic has a large Influence on the performance of a total network. Therefore, traffic management has become an important issue of network management. In this paper, we propose a new routing algorithm that attempts to analyze network conditions using time series prediction models and to propose predictive optimal routing decisions. Traffic congestion is assumed when the predicting result is bigger than the permitted bandwidth. By collecting traffic in real network, the predictable model is obtained when it minimizes statistical errors. In order to predict network traffic based on time series models, we assume that models satisfy a stationary assumption. The stationary assumption can be evaluated by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). We can obtain the result of these two functions when it satisfies the stationary assumption. We modify routing oaths by predicting traffic in order to avoid traffic congestion through experiments. As a result, Predicting traffic and balancing load by modifying paths allows us to avoid path congestion and increase network performance.

The Relation of CLR and Blocking Probability for CBR Traffic in the Wireless ATM Access Network

  • Lee, Ha-Cheol;Lee, Byung-Seub
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.27 no.11C
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    • pp.1158-1163
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    • 2002
  • In this paper it is focused on the relation between CLR (Cell Loss Ratio) and blocking probability, GoS(Grade of Services) parameters in the wireless ATM (Asynchronous Transfer Mode) access network which consists of access node and wireless channel. Traffic model of wireless ATM access network is based on the cell scale, burst scale and call connection level. The CLR equation due to buffer overflow for wireless access node is derived for CBR (Constant Bit Rate) traffic. The CLR equation due to random bit errors and burst errors for wireless channel is derived. Using the CLR equation for both access node and wireless channel, the CLR equation of wireless ATM access network is derived. The relation between access network CLR and blocking probability is analyzed for CBR traffic.