• Journal of Urban Science
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• v.7 no.1
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• pp.45-54
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• 2018

The purpose of this study is to find the factors that reduce prediction error in traffic volume using highway traffic volume data. The ARIMA model was used to predict the day, and it was confirmed that weekday and weekly characteristics were distinguished by prediction error. The forecasting results showed that weekday characteristics were prominent on Tuesdays, Wednesdays, and Thursdays, and forecast errors including MAPE and MAE on Sunday were about 15% points and about 10 points higher than weekday characteristics. Also, on Friday, the forecast error was high on weekdays, similar to Sunday's forecast error, unlike Tuesday, Wednesday, and Thursday, which had weekday characteristics. Therefore, when forecasting the time series belonging to Friday, it should be regarded as a weekly characteristic having characteristics similar to weekend rather than considering as weekday.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.99-102
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• 2001

It has well known that the congestion of traffic and it's distribution. There are very important problems in the traffic control systems. In this paper, we will purpose an ITFS(Intelligent Traffic Forecasting System) which can determine the car classes and transport them to ITS(Intelligent Traffic control System). The system will be used the Inductive Loop Detector(ILD)and the Fuzzy logic and shown the effectiveness by the computer simulation.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1235-1243
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• 2015

Increased data volume in the ICT area has increased the importance of forecasting accuracy for internet traffic. Forecasting results may have paper plans for traffic management and control. In this paper, we propose combined forecasts based on several time series models such as Seasonal ARIMA and Taylor's adjusted Holt-Winters and Fractional ARIMA(FARIMA). In combined forecasting methods, we use simple-combined method, MSE based method (Armstrong, 2001), Ordinary Least Squares (OLS) method and Equality Restricted Least Squares (ERLS) method. The results show that the Seasonal ARIMA model outperforms in 3 hours ahead forecasts and that combined forecasts outperform in longer periods.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.576-583
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• 2010

Wireless Internet is the network which provides wireless access in order to serve the Internet connections and data communication through the mobile handsets. To get efficient wireless access to the Internet, we need the WAP (Wireless Application Protocol) gateway that performs protocol translation and contents conversion between two different networks. We need the capacity dimensioning of the WAP gateway system in order to provide the wireless Internet service stably and cost-effectively. We also need the traffic engineering methods including traffic modelling and forecasting for the economical facility investment. The existing method of WAP gateway capacity dimensioning was intuitive and qualitative. But in this paper, we deal with the capacity dimensioning analytically and quantitatively on the basis of WAP traffic description parameters and traffic forecasting method.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.889-896
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• 2005

While a year and a half has been passed since the launch of KTX service, traffic volume of Gyung-bu High Speed Railway is still much lower than the forecasted value. This situation has been badly affecting not only Korail's financial status but also KRNA's general railway construction projects as general public responds negatively to such projects as New Ho-nam Line Construction. This paper outlines traffic volume forecasting methodologies applied to construction of Gyung-bu High Speed Railway, identifies major causes of forecasting deviations. and finally extracts problems through comparison between the forecasted results and actual traffic volume.

• The Journal of Information Technology
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• v.5 no.4
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• pp.173-183
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• 2002

In this paper, we propose an efficient forecasting methodology of the mid and long-term frequency demand in Korea. The methodology consists of the following three steps: classification of basic service group, calculation of effective traffic, and frequency forecasting. Based on the previous studies, we classify the services into wide area mobile, short range radio, fixed wireless access and digital video broadcasting in the step of the classification of basic service group. For the calculation of effective traffic, we use the measures of erlang and bps. The step of the calculation of effective traffic classifies the user and basic application, and evaluates the effective traffic. Finally, in the step of frequency forecasting, different methodology will be proposed for each service group.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.297-303
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• 2011

Major ports in Northeastern Asia engage in fierce competition to attract transshipment traffic volume. Existing time series analyses for analyzing port competition relationships examine the types of competition and relations through the signs of coefficients in cointegration equations using the transshipment traffic volume results. However, there are cases for which analyzing competing relationships is not possible based on the results of the transshipment traffic volume data differences and limitations in the forecasting of traffic volume. Accordingly, we used the Lotka-Volterra (L-V) model,also known as the ecosystem competitive relation model, to analyze port competition relations for the long-term forecast of South Korean transshipment traffic volume.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5256-5262
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• 2014

Recently, Distributed Denial of Service (DDoS) attacks, such as spreading malicious code, cyber-terrorism, have occurred in government agencies, the press and the financial sector. DDoS attacks are the simplest Internet-based infringement attacks techniques that have fatal consequences. DDoS attacks have caused bandwidth consumption at the network layer. These attacks are difficult to detect defend against because the attack packets are not significantly different from normal traffic. Abnormal traffic is threatening the stability of the network. Therefore, the abnormal traffic by generating indications will need to be detected in advance. This study examined the abnormal traffic detection technique using a forecasting model-based trend model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.73-80
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• 2018

In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems.