• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.183-191
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• 2018

In this study a traffic volume dependent displacement estimation model for Gwangan Bridge was developed using bridge monitoring big data. Traffic volume data for four different vehicle types and the vertical displacement data in the central position of the Gwangan Bridge were used to develop and validate the estimation model. Two statistical estimation models were developed using multiple regression analysis (MRA) and principal component analysis (PCA). Estimation performance of those two models were compared with actual values. The results show that both the MRA and the PCA based models are successfully estimating the vertical displacement of Gwangan Bridge. Based on the results, it is concluded that the developed model can effectively be used to predict the traffic volume dependent displacement behavior of Gwangan Bridge.

• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.42-46
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• 2012

This paper analyses technical and economical conditions which activate the use of mobile communication spectrum not to limit the growth of mobile broadband service because of mobile big data traffic and proposes the method which activate the use of mobile communication spectrum. To activate new mobile communication spectrum the expenditure and income of investment should be balanced. The activation of new mobile communication spectrum to process mobile big data traffic depends on technical and economical conditions, internal and external factors of service provider. The investment expenditure is relate to CAPEX, OPEX which is internal factors of service provider and to spectrum price which is external factor of service. The investment income is relate to tariff system which is internal factors of service provider and to spectrum neutrality which is external factor of service provider. The activation of new mobile communication spectrum can be implemented when the investment expenditure and investment income meet the balance including the spectrum price in the investment expenditure and the tariff system which is able to extend network and the income based on traffic increase by external contents in the investment income.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.525-544
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• 2017

With the advent of the big data era, the interest in the development of land using traffic data has increased significantly. However, the current research on traffic big data lingers around organizing or calibrating the data only. In this research, a novel method for discovering the hidden values within the traffic data through data mining is proposed. Considering the fact that traffic data and network structures have similarities, network analysis algorithms are used to find valuable information in the actual traffic volume data. The PageRank and HITS algorithms are then employed to find the centralities. While conventional methods present centralities based on uncomplicated traffic volume data, the proposed method provides more reasonable centrality locations through network analysis. Since the centrality locations that we have found carry detailed spatiotemporal characteristics, such information can be used as an objective basis for making policy decisions.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.36-46
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• 2020

This study is designed to help customers use more comfortable airports by predicting congestion and congestion times by identifying the traffic routes of passengers in the airport building by day of the week and time by using Wi-Fi sensor collectors, one of the IoT technologies. Analysis of passenger traffic analysis data showed that the most congested time zones were from noon. to 2p.m. for all facilities, which could be used to improve major facilities. Regression analysis of factors affecting congestion found that self-check-in reduces congestion and check-in counters increases congestion. These findings will provide important implications for operations, including congestion management at airports.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.541-550
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• 2015

The importance of efficient enterprise network management has been emphasized continuously because of the rapid utilization of Internet in a limited resource environment. For the efficient network management, the management policy that reflects the characteristics of a specific network extracted from long-term traffic analysis is essential. However, the long-term traffic data could not be handled in the past and there was only simple analysis with the shot-term traffic data. However, as the big data analytics platforms are developed, the long-term traffic data can be analyzed easily. Recently, enterprise network resource efficiency through the long-term traffic analysis is required. In this paper, we propose the methods of collecting, storing and managing the long-term enterprise traffic data. We define several classification categories, and propose a novel network resource efficiency through the multidirectional statistical analysis of classified long-term traffic. The proposed method adopted to the campus network for the evaluation. The analysis results shows that, for the efficient enterprise network management, the QoS policy must be adopted in different rules that is tuned by time, space, and the purpose.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.267-268
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• 2022

Jeju coastal waters are limited to high-risk areas due to the passage of international cruise ships, passenger ships, with a large number of people and fishing boats, or to the jeju port and the jeju civilian-military combined port and near by seas, so a VTS system will be established along jeju and seogwipo coast. There is no accurate standard for determining the number of people required by the maritime traffic control center. Therefore, this study calculated the required operating personnel for control seats on the coast of jeju and seogwipo by using VTS big data to efficiently calculate the workload of maritime traffic control. It is judged that this study can be used basic data for research that sets the standard for calculating the control workload.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.111-114
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• 2018

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.153-154
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• 2023

대한민국의 교통 혼잡 비용은 2018년 기준 67조 원으로 국내총생산(GDP)의 3.6%를 차지하고 있다. 또한 국민 교통 고통지수는 매년 상승하고 있는 추세이다. 본 논문에서는 인구 밀집도가 가장 높은 서울시의 교통 혼잡 문제를 해결하기 위해 빅데이터 분석을 통한 효과적인 정책을 제공하고자 한다. 국가 표준 링크 아이디(LINK_ID)와 노드 아이디(NODE_ID)를 통해 위도 경도 데이터를 추출하고, 정체성이 높은 도로를 시각화해 추려진 특성과 공통점을 파악한다. 이를 토대로 정체성을 낮출 방안을 제공하고자 한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.100-112
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• 2021

With the advent of big data, traffic prediction has been developed based on historical data analysis methods, but this method deteriorates prediction performance when a traffic incident that has not been observed occurs. This study proposes a method that can compensate for the reduction in traffic prediction accuracy in traffic incidents situations by hybrid approach of machine learning and traffic simulation. The blind spots of the data-driven method are revealed when data patterns that have not been observed in the past are recognized. In this study, we tried to solve the problem by reinforcing historical data using traffic simulation. The proposed method performs machine learning-based traffic prediction and periodically compares the prediction result with real time traffic data to determine whether an incident occurs. When an incident is recognized, prediction is performed using the synthetic traffic data generated through simulation. The method proposed in this study was tested on an actual road section, and as a result of the experiment, it was confirmed that the error in predicting traffic state in incident situations was significantly reduced. The proposed traffic prediction method is expected to become a cornerstone for the advancement of traffic prediction.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.4
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• pp.120-130
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• 2019

In this study, based on the correlation between traffic volume data and vertical displacement data developed in previous research using the bridge maintenance big data of 2006, the vertical displacement estimation model using the traffic volume data of Gwangan Bridge for 10 years A comparison of the performance of the developed model with the current applicability is presented. The present applicability of the developed model is analyzed that the estimated displacement is similar to the actual displacement and that the displacement estimation performance of the model based on the structured regression analysis and the principal component analysis is not significantly different from each other. In conclusion, the vertical displacement estimation model using the traffic volume data developed by this study can be effectively used for the analysis of the behavior according to the traffic load of Gwangan Bridge.