• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.379-393
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• 2006

In this paper, we propose a method to summarize the monthly traffic amount for analyzing network load in mobile communication system. We used the traffic data obtained from a domestic telecommunication company. Based on the statistical properties of the traffic data, we devise an efficient method to summarize monthly traffic amount.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.307-313
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• 2011

In this paper, we compare performance of three models. The Holt-Winters, FARIMA and ARGARCH models, are used in predicting internet traffic data for analysis of traffic characteristics. We first introduce the time series models and apply them to real traffic data to forecast. Finally, we examine which model is the most suitable for explaining the long memory, the characteristics of the traffic material, and compare the respective prediction performance of the models.

• Asia pacific journal of information systems
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• v.26 no.3
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.661-668
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• 2015

Them traffic accidents have been increased every year due to increasing of vehicles numbers as well as the gravitation of the population. The carelessness of drivers, many road weather factors have a great influence on the traffic accidents. Especially, the number of traffic accident is governed by precipitation, visibility, humidity, cloud amounts and temperature. The purpose of this paper is to analyse the effect of road weather factors on traffic accident. We use the data of traffic accident, AWS weather factors (precipitation, existence of rainfall, temperature, wind speed), time zone and day of the week in 2013. We did statistical analysis using logistic regression analysis and decision tree analysis. These prediction models may be used to predict the traffic accident according to the weather condition.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.261-270
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• 2002

Recently, the demand of the Intelligent Transportation System(ITS) has been increased to a large extent, and a real-time traffic information service based on the internet system became very important. When ITS companies carry out real-time traffic services, they find some traffic data missing, and use the conventional method of reconstructing missing values by calculating average time trend. However, the method is found unsatisfactory, so that we develop a new method based the spatial and spatial-temporal models. A cross-validation technique shows that the spatial-temporal model outperforms the others.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1177-1190
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• 2009

The traffic operating speed is one of important information to measure a road capacity. When we supply the information of the road of high traffic by using navigation, offering the present traffic information and the forecasted future information are the outstanding functions to serve the more accurate expected times and intervals. In this study, we proposed the traffic speed forecasting model using the accumulated traffic speed data of the road and highway and forecasted the average speed for each the road and high interval and each time interval using Fourier transformation and time series regression model with trigonometrical function. We also propose the proper method of missing data imputation and treatment for the outliers to raise an accuracy of the traffic speed forecasting and the speed grouping method for which data have similar traffic speed pattern to increase an efficiency of analysis.

• Communications of the Korean Mathematical Society
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• v.13 no.3
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• pp.635-642
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• 1998

When an integrated communication system is congested, we may reserve some spaces for non-realtime traffic by discarding a part of realtime traffic. That is sensible because realtime traffic is insensitive to a few losses. Several discarding schemes have been developed including Separate Queue (SQ). Under such schemes, the block loss distribution, i.e., the distribution of the number of losses within a given block which consists of successive data of a type, is important. We derive the block loss distribution of the SQ scheme and modifies the SQ scheme with a threshold.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1037-1044
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• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• Convergence Security Journal
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• v.6 no.4
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• pp.49-57
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• 2006

Firewall to block a network access of unauthorized IP system and IDS (Intrusion Detection System) to detect malicious code pattern to be known consisted the main current of the information security system at the past. But, with rapid growth the diffusion speed and damage of malicious code like the worm, study of the unknown attack traffic is processed actively. One of such method is detection technique using traffic statistics information on the network viewpoint not to be an individual system. But, it is very difficult but to reserve traffic raw data or statistics information. Therefore, we present extraction technique of a network traffic Raw data and a statistics information like the time series. Also, We confirm the validity of a mixing traffic and show the evidence which is suitable to the experiment.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1053-1061
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• 2013

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.