• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.265-266
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• 2022

전통적으로 선박교통관제서비스는 정보제공(INS), 항행지원(NAS), 교통관리(TOS)로 구분되어 왔으나, 최근 IMO 결의서 A.1158의 개정을 통해 선박교통관제서비스의 목적을 선박 항해에 안전하지 않은 상황을 선제적으로 모니터링하고 통제하는 것으로 규정하고 있다. 이를 위해 기존의 VTS 서비스 용어들을 모두 삭제하였으며, IALA에서도 관제사의 의사결정도구에 대한 개정을 논의하고 있는 상황이다. 이에 본 논문에서는 빅데이터 기반의 관제사 의사결정도구를 제안하였으며, 적용 가능성을 검토하였다. 제안하는 방법은 관제사의 주관적인 판단과 단순한 규칙에 의존하던 기존의 관제 방법과는 달리 데이터를 기반으로 하는 객관적인 관제 기준을 제시할 수 있으며, 이러한 방법이 실제 관제 현장에 적용되어 해양사고를 방지하고, 항만의 운영 효율을 향상시킬 수 있기를 기대한다.

• Journal of Industrial Convergence
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• v.17 no.4
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• pp.53-58
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• 2019

It is important to collect and analyze the data from the traffic accident analysis system and the National Statistical Office to reduce the traffic accident rate of the elderly, who are the weakest. In particular, it is more important to analyze the data in areas where the elderly population is large and where accidents occur frequently. This paper visualizes and analyzes the data of elderly traffic accidents that occurred in recent 5 years in the area where many elderly people live in Buyeo-gun. The elderly traffic accident type, accident area, and location data of the elderly can be useful for the improvement measures and related decision making to reduce the elderly traffic accidents.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.887-892
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• 2023

Vessel traffic service(VTS) centers are equipped with RADAR, AIS(Automatic Identification System), weather sensors, and VHF(Very High Frequency). VTS operators use this equipment to observe the movement of ships operating in the VTS area and provide information. The VTS data generated by these various devices is highly valuable for analyzing maritime traffic situation. However, owing to a lack of compatibility between system manufacturers or policy issues, they are often not systematically managed. Therefore, we developed the VTS Bigdata Platform that could efficiently collect, store, and manage control data collected by the VTS, and this paper describes its design and implementation. A microservice architecture was applied to secure operational stability that was one of the important issues in the development of the platform. In addition, the performance of the platform could be improved by dualizing the storage for real-time navigation information. The implemented system was tested using real maritime data to check its performance, identify additional improvements, and consider its feasibility in a real VTS environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.378-381
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• 2016

정보기술과 디지털 경제의 확산으로 대규모의 데이터가 생산되는 정보화시대에서 빅데이터의 중요성이 강조되고 있으며 다양한 분야에서 이를 응용하고 있다. 빅 데이터 분석 도구인 R은 통계 기반의 정보 분석을 가능하게 하는 언어와 환경이다. 본 논문에서는 R을 이용하여 미디어에 나타난 부산 교통 관련 빅데이터를 분석한다. 다양한 미디어에서 부산 교통 관련 데이터를 수집하고 어떠한 텍스트가 분포되어 있는지 빈도 조사를 수행한다.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.130-145
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• 2021

As part of the Smart Seoul policy, the importance of using big urban data is being highlighted. Furthermore interest in the impact of transportation-related urban environmental factors such as PM10 and noise on citizen's quality of life is steadily increasing. This study established the integrated DB by matching IoT big data with transportation data, including traffic volume and speed in the microscopic Spatio-temporal scope. This data analyzed the impact of a spatial unit in the road-effect zone on environmental risk level. In addition, spatial units with similar characteristics of road traffic and environmental factors were clustered. The results of this study can provide the basis for systematically establishing environmental risk management of urban spatial units such as PM10 or PM2.5 hot-spot and noise hot-spot.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.379-384
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• 2013

For effective ship management in VTS(Vessel Traffic Service), it needs to assess the external force acting on ship. Big data in maritime traffic can be roughly categorized into two groups. One is the traffic information including ship's particulars. The other is the external force information e.g., wind, sea wave, tidal current. This paper proposes the method to assess the external force acting on ship using big data in maritime traffic. To approach Big data in maritime traffic, we propose the Waterway External Force Code(WEF code) which consist of wind, wave, tidal and current information, Speed Over the Water(SOW) of each ship, weather information. As a results, the external force acting a navigating ship is estimated.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.297-298
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• 2014

최근 사회 전반적으로 빅데이터가 주목 받고 있다. 기존 대중교통 안내 어플리케이션의 경우 현재 교통정보를 기준으로 추천하기 때문에 실제로는 최적의 경로가 아닌 경로가 추천될 수 있다. 본 논문에서는 빅데이터 분석을 통한 실시간 최적 교통 경로 안내 시스템을 설계하고 구현한다. 설계한 시스템은 과거 교통 정보를 분석하여 각 경로들의 교통상황을 예측하여 경로 이동 계획을 설정해준다. 또한 중간에 교통상황이 급변하여 경로를 수정해야할 필요가 있을 때 사용자에게 알림을 주고 그에 대한 조치를 취할 수 있도록 지원한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• Transportation Technology and Policy
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• v.13 no.1
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• pp.23-30
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• 2016