• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.2
• /
• pp.19-27
• /
• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• Journal of Communications and Networks
• /
• v.18 no.3
• /
• pp.513-522
• /
• 2016

We propose a mechanism for monitoring load in quality of service (QoS)-enabled wireless networks and show how it can be used for network management as well as for dynamic pricing. Mobile network traffic, especially video, has grown exponentially over the last few years and it is anticipated that this trend will continue into the future. Driving factors include the availability of new affordable, smart devices, such as smart-phones and tablets, together with the expectation of high quality user experience for video as one would obtain at home. Although new technologies such as long term evolution (LTE) are expected to help satisfy this demand, the fact is that several other mechanisms will be needed to manage overload and congestion in the network. Therefore, the efficient management of the expected huge data traffic demands is critical if operators are to maintain acceptable service quality while making a profit. In the current work, we address this issue by first investigating how the network load can be accurately monitored and then we show how this load metric can then be used to provide creative pricing plans. In addition, we describe its applications to features like traffic offloading and user satisfaction tracking.

• Journal of Korean Society of Transportation
• /
• v.33 no.3
• /
• pp.256-267
• /
• 2015

The intelligent transportation systems allow us to have valuable opportunities for collecting wide-area coverage traffic data. The significant efforts have been made in many countries to provide the reliable traffic conditions information such as travel time. This study analyzes the impacts of the fog weather conditions on the traffic stream. Also, a strategy for predicting the long-term traffic speeds is developed under foggy weather conditions. The results show that the average of speed reductions are 2.92kph and 5.36kph under the slight and heavy fog respectively. The best prediction performance is achieved when the previous 45 pattern cases data is used, and the 14.11% of mean absolute percentage error(MAPE) is obtained. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.5
• /
• pp.1-18
• /
• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.

• Journal of Digital Contents Society
• /
• v.5 no.1
• /
• pp.7-11
• /
• 2004

There are many problems that must solve to construct next generation high-speed communication network. Among these, item that must consider basically is characteristics analysis of traffic that nows to network Traffic characteristics of many Internet services that is offered present have shown that network traffic exhibits at a wide range of scals-self-similarity. Self-similarity is expressed by long term dependency, this is contradictory concept with Poisson model that have relativity short term dependency. Therefore, first of all, for design and dimensioning of next generation communication network, traffic model that are reflected burstiness and self-similarity is required. Here self-similarity can be characterized by Hurst parameter. In this paper, the calculation equation is derived considering queueing delay and self-similarity of data traffic art compared with simulation results.

• Journal of Korean Institute of Industrial Engineers
• /
• v.37 no.4
• /
• pp.297-303
• /
• 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 Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.633-636
• /
• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• ETRI Journal
• /
• v.26 no.5
• /
• pp.391-396
• /
• 2004

In this paper, we propose a packet scheduling discipline called packet loss fair scheduling, in which the packet loss of each user from different real-time traffic is fairly distributed according to the quality of service requirements. We consider an orthogonal frequency division multiple access (OFDMA) system. The basic frame structure of the system is for the downlink in a cellular packet network, where the time axis is divided into a finite number of slots within a frame, and the frequency axis is segmented into subchannels that consist of multiple subcarriers. In addition, to compensate for fast and slow channel variation, we employ a link adaptation technique such as adaptive modulation and coding. From the simulation results, our proposed packet scheduling scheme can support QoS differentiations while guaranteeing short-term fairness as well as long-term fairness for various real-time traffic.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.16 no.4
• /
• pp.54-62
• /
• 2008

International organizations related to air transport such as ICAO, IATA, ACI are forecasting that the number of passenger will grow about 4.4% annually up to 2015. Therefore, the innovation of given system technology and operation procedure is required in global scale to cope with the increase of air traffic demand. CNS/ATM infrastructure based on satellite is considered to play key role in order to solve the problems due to the dramatic increase of air traffic demand over the world. Free flight concept in the air transport operation has been proved with CNS/ATM infrastructure especially in USA and Europe. Therefore, it is necessary to develop key technologies to overcome technology gap and to secure international competitiveness in Korea. ADS-B is an important issue, and new element technologies should be considered as essential items which were shown in Capstone project. Nowadays, the free flight concept is combined to Air Transport Road Map such as NextGen project in USA, SESAR in Europe. In this process, free flight is included in the concepts such as ATM(Air Traffic Management), aviation security and safety, environmental protection and economy development, wide area weather variable reduction service, information integration and application between the related authorities (civil/military) etc. The purpose of research is to establish mid-term and long-term infrastructure plan and strategy for free flight realization in Korea. The analysis of action target and equipment construction status, phase construction plan of infrastructure has been performed by considering mid-term and long-term free flight plans of USA and Europe.

• ETRI Journal
• /
• v.46 no.3
• /
• pp.379-391
• /
• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.