• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.257-260
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• 2001

As today's network infrastructure continues to grow and DiffServ IP networks ate now available to provide various levels of flexible QoS services. DiffServ guarantees good scalability but shows dynamic QoS dependent on network traffic loads. Therefore, in this paper, we investigate the dynamics of DiffSev QoS and present analytical model to estimate the allowable traffic load under the given network conditions.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.23-37
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• 1997

In this paper, performance analysis of video traffic shaper for Motion Picture Experts Group (MPEG) video traffic in on ATM network are investigated. Traffic shaping for MPEG video traffic is proposed as a traffic control function in ATM networks. The proposed shaper smoothes video traffics by controling the output rate of the buffer, which is placed in an MPEG source, according to I,B,P frame sequences of MPEG. In performance analysis of an video traffic shaper, a periodic botch arrival model is suggested to describe cell streams in a frame of MPEG video traffic. The queueing model which has periodic independent botch arrival and periodic deterministic service time is used to obtain the cell loss ratio, the mean cell delay, and the measure of smoothing effect. Simulation results are used to validate this queueing model. The cell loss performance of ATM multiplexer is measured by simulation study with real MPEG-1 data. From the viewpoint of traffic load, the cell loss ratio is observed to be considerably high, which is considered to result from the burstiness of MPEG video traffic. As a result, it is shown that the shaping decreases cell loss ratio of multiplexer. The results of this paper can be employed to establish a basic guideline in the implementation of a traffic control scheme and the design of ATM multiplexer for MPEG video traffic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.215-217
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• 2005

There are two types of packet loss probabilities used in both the network layer and the physical layer within the wireless transmitter such as a queueing discard probability and transmission loss probability. We analyze these loss performances in order to guarantee Quality of Service (QoS) which is the basic of the future network. The queuing loss probability is caused by a maximum allowable delay time and the transmission loss probability is caused by a wireless channel error. These two types of packet loss probabilities are not easily analyzed due to recursive feedback which, originates as a result at a queueing delay and a number of retransmission attempts. We consider a wireless transmitter to a M/D/1 queueing model. We configurate the model to have a finite-size FIFO buffer in order to analyze the real-time traffic streams. Then we present the approaches used for evaluating the loss probabilities of this M/D/1/K queueing model. To analyze the two types of probabilities which have mutual feedbacks with each other, we drive the solutions recursively. The validity and accuracy of the analysis are confirmed by the computer simulation. From the following solutions, we suggest a minimum of 'a Maximum Allowable Delay Time' for real-time traffic in order to initially guarantee the QoS. Finally, we analyze the required service rate for each type utilizing real-time traffic and we apply our valuable analysis to a N-user's wireless network in order to get the fundamental information (types of supportable real-type traffics, types of supportable QoS, supportable maximum number of users) for network design.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.95-107
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• 2023

Purpose: The statistical analysis of point processes on linear networks is a recent area of research that studies processes of events happening randomly in space (or space-time) but with locations limited to reside on a linear network. For example, traffic accidents happen at random places that are limited to lying on a network of streets. This paper applies techniques developed for point processes on linear networks and the tools available in the R-package spatstat to estimate the intensity of traffic accidents in Leon County, Florida. Methods: The intensity of accidents on the linear network of streets is estimated using log-linear Poisson models which incorporate cubic basis spline (B-spline) terms which are functions of the x and y coordinates. The splines used equally-spaced knots. Ten different models are fit to the data using a variety of covariates. The models are compared with each other using an analysis of deviance for nested models. Results: We found all covariates contributed significantly to the model. AIC and BIC were used to select 9 as the number of knots. Additionally, covariates have different effects such as increasing the speed limit would decrease traffic accident intensity by 0.9794 but increasing the number of lanes would result in an increase in the intensity of traffic accidents by 1.086. Conclusion: Our analysis shows that if other conditions are held fixed, the number of accidents actually decreases on roads with higher speed limits. The software we currently use allows our models to contain only spatial covariates and does not permit the use of temporal or space-time covariates. We would like to extend our models to include such covariates which would allow us to include weather conditions or the presence of special events (football games or concerts) as covariates.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.117-134
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• 2004

Truck traffic accounts for a substantial fraction of the traffic stream in many regions and is often the source of localized traffic congestion, potential parking and safety problems. Truck trips tend to be ignored or treated superficially in travel demand models. It reduces the effectiveness and accuracy of travel demand forecasting and may result in misguided transportation policy and project decisions. This paper presents the development of speed-flow relationships with truck impacts based on CORSIM simulation results in order to enhance travel demand model by incorporating truck trips. The traditional BPR(Bureau of Public Road) function representing the speed-flow relationships for roadway facilities is modified to specifically include the impacts of truck traffics. A number of new speed-flow functions have been developed based on CORSIM simulation results for freeways and urban arterials.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4287-4306
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• 2016

Network resilience provides an effective way to overcome the problem of network failure and is crucial to Internet protocol (IP) network management. As one of the main challenges in network resilience, recovering from link failure is important to maintain the constancy of packets being transmitted. However, existing failure recovery approaches do not handle the traffic engineering problem (e.g., tuning the routing-protocol parameters to optimize the rerouting traffic flow), which may cause serious congestions. Moreover, as the lack of QoS (quality of service) restrictions may lead to invalid rerouting traffic, the QoS requirements (e.g., bandwidth and delay) should also be taken into account when recovering the failed links. In this paper, we first develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures, with which we can choose reliable backup paths (BPs). Then we construct a mathematical model for the failure recovery problem, which takes maximum rerouting traffic as the optimizing objective and the QoS requirements as the constraints. Moreover, we propose a heuristic algorithm for link failure recovery, which adopts the improved k shortest path algorithm to splice the single BP and supplies more protection resources for the links with higher priority. We also prove the correctness of the proposed algorithm. Moreover, the time and space complexity are also analyzed. Simulation results under NS2 show that the proposed algorithm improves the link failure recovery rate and increases the QoS satisfaction rate significantly.

• Smart Media Journal
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• v.13 no.6
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• pp.24-34
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• 2024

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.

• ETRI Journal
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• v.30 no.2
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• pp.205-215
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• 2008

Many control schemes have been proposed for flow-level traffic control. However, flow-level traffic control is implemented only in limited areas such as traffic monitoring and traffic control at edge nodes. No clear solution for end-to-end architecture has been proposed. Scalability and the lack of a business model are major problems for deploying end-to-end flow-level control architecture. This paper introduces an end-to-end transport architecture and a scalable control mechanism to support the various flow-level QoS requests from applications.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.323-329
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• 1998

In Pusan port, the studies, which analysis container cargo volumes by using forecasting methods and research about container logistics system, etc., have been continuously performed. But, in Pusan port, this study on an evaluation of traffic congestion has been scarcely performed until now. Especially, when changing and extending a berth, and constructing a new port, it is very important to examine this field. And it should be considered. Thus, this paper aims to analysis the effect of ship traffic condition in 2011, to evaluate marine traffic congestion, according to changing ship traffic volumes in Pusan port. To analysis it, we used simulation method and examined the results