SOLAS에서는 국제 항해에 종사하는 총톤수 500톤 이상의 선박에 대하여 2018년 7월 1일 이후 도래하는 최초 검사까지 ECDIS 를 설치해야 한다고 규정하고 있다. 새로운 주요 항해 장비로 ECDIS가 탑재되면서 ECDIS 사용에 관련한 다양한 사고가 발생하고 있다. MAIB, BSU, BEAmer, DMAIB, DSB에서 발행한 12가지의 사고보고서에는 항해사의 운용 미숙과 ECDS 시스템의 사고 원인으로 분석하였고, 사고 원인과 관련된 단어들을 정량적으로 분석하기 위해 R-프로그램을 사용하여 텍스트를 분석하였다. 도출 빈도에 따른 단어의 중요도를 나타내기 위해 텍스트 마이닝 기법인 단어 구름, 단어 연관성, 단어 가중치의 방법을 사용하였다. 단어 구름은 사용된 단어들의 빈도 수를 구름 형태로 나타내는 방법으로써 N-gram 모델을 적용하였다. N-gram 모델 중 Uni-gram 분석 결과 ECDIS 단어, Bi-gram 분석 결과는 Safety Contour 단어의 사용 빈도가 가장 많았다. Bi-gram 분석을 기반으로 사고 원인 단어를 항해사와 ECDIS 시스템으로 구분하고, 연관된 단어들을 단어 연관성으로 나타내었다. 마지막으로 항해사와 ECDIS 시스템에 연관된 단어들을 단어 말뭉치로 구성한 후 단어 가중치를 적용하여 연도별 말뭉치 빈도 변화를 분석하였다. 추세선 그래프로 말뭉치 변화 경향을 분석한 결과, 항해사 말뭉치는 최근으로 올수록 감소하였으며 반대로 ECDIS 시스템 말뭉치는 점점 증가함을 나타내었다.

PURPOSES: The purpose of this study is to investigate factors that affect the severity of children’s traffic accidents using the ordered probit model, and to contribute to a safer road environment for children. METHODS: This study used children’s traffic accident data during the last four years in the Incheon Metropolitan area. At this point, to analyze only the direct damage caused to children, the analysis was made of accidents where the victim was under 13 years old. Data from a total of 1,110 accidents was collected. When the model was constructed, as it was judged that there could be a difference in factors affecting accident occurrence depending on the zone characteristics, the model was divided into school and non-school zones. RESULTS: The accident content (severity) is divided into four stages (fatal injury, serious injury, minor injury and injury report) to construct the order-typed probit model. For the analysis, 65 variables of 17 categories were included in the model. The statistical package STATA 13.1 was used to analyze the variables affecting the accident severity with a confidence level of 90% (α·=0.1). Consequently, a total of 15 variables were found to have a statistically significant effect on accident severity in a school zone. In contrast, a total of 22 variables were found to have a statistically significant effect on accident severity in non-school zones. Four variables (daytime, weekday, victim age, intersection) were significant in both models. CONCLUSIONS: Among the significant variables found in school zones, signal violation and type of vehicle (line bus, rent car, bus, business other vehicles) had a relatively greater effect on the accident severity than the other variables. In non-school zones, eight variables comprising daytime, head-on collision, crossing, over-speed, gender of victim (male), victim age, type of vehicle (construction machinery), driver age (50-59) were found to be significant variables. In conclusion, as well as eliminating factors that can lead to accident reductions, it is necessary to consider zone characteristics to reduce the severity of children’s accidents and promote children’s traffic safety.

This research carried out a study on the job characteristics of the skipper of the coastal composite fishing vessels in order to find a way to prevent the ship collision caused by the highest human error among the marine casualty of fishing boats. Video observation was used as the research method in which six CCD cameras were installed on the vessel to collect image data and data extracted from the image were analyzed to derive the results of the functional activity of skipper according to the fishing operation process of experimental fishing vessel. The results are as follows. The working process of the experimental fishing vessel consisted of navigation for fishing ground, setting line, waiting for hauling line, hauling line and navigation to homeport. In these processes, the skipper was performing watchkeeping in the wheelhouse in which he carried out a single task, a dual task that performed two tasks simultaneously, and a triple task that performed two or more tasks simultaneously. In addition, one of the risk factors causing the collision was a no watchkeeping in the wheelhouse for navigating for fishing ground, waiting for hauling line, and hauling line at 25.4%, 64.6% and 0.3%, respectively among the marine casualty while drowsiness caused 1.2% of the marine casualty in navigating for fishing ground. Concurrent tasks that simultaneously perform two or more tasks that can overlook any other important duties while carrying out watchkeeping in the wheelhouse include 51.3% of navigation for fishing ground, 81.9% of setting line, 19.0% of waiting for hauling line, 87.9% of hauling, and 88.7% of navigation to homeport. The above concurrent tasks yielded an average of 66.1%. Experimental fishing vessels are required to focus on ship handling operations related to fishery operations, and the skipper is assigned more activities and attention to fishery related tasks. Therefore, it is considered desirable to build a collision prevention system that is appropriate to the characteristics of the skipper’s work, escaping from transferring the responsibility of ship collision to the skipper completely.

최근 해상 교통 환경의 변화로 인해 해군함정 사고가 지속적으로 발생하고 있으며, 특히 2017년 미 해군 구축함 사고로 인해 심각한 인명피해가 발생하였다. 이에 본 연구에서는 해군 교육 자료, 해군함정 사고 재결서, 미구축함 사고 분석 보고서의 해군함정 사고사례를 이용해 해군함정 사고를 분석하고 시나리오를 구축하였다. 이를 위해 조사한 자료를 중심으로 함정 사고 현황을 파악하고 사고사례를 분석하였다. 사고재현 절차에 따라 사고사례 17건을 재현하고 해군함정 사고 시나리오를 구축하였다. 해군함정 사고 17건의 CPA, TCPA 및 PARK 모델 위험도 분석 결과, 충돌위험은 평균적으로 사고발생 5~6분 전을 기준으로 증가하는 것으로 분석되었다. 본 연구의 결과를 해군 사례 교육 및 시뮬레이션 교육의 시나리오 기초자료로 제공하여 해양사고 예방에 기여하고자 한다.

해양사고는 주로 충돌과 좌초에 의한 것으로 인적과실이 원인이다. 한국형 e-Navigation 사업은 인적과실에 의한 해양사고 저감 및 초고속 해상무선통신(LTE-Maritime)의 구축 방안을 마련하고 있다. 그에 따른 사고취약선박 모니터링 지원 서비스(SV10)는 육상에서 수집한 선박의 위치정보를 기반으로 선박의 충돌·좌초 위험도를 평가하여 선박이 위험상황을 인식할 수 있도록 정보를 제공하고 해양사고 발생 시 육상에서 조기 대응할 수 있도록 실시간 선박 위치 및 위험상황 정보를 유관기관과 공유하는 서비스이다. 본 연구에서는 중앙해양안전심판원에서 제공된 해양사고 통계연보 및 재결서를 분석하여 SV10 서비스로 저감될 수 있는 해양사고 비율을 확인하였다.

PURPOSES: This study aimed to analyze traffic accidents at circular intersections, and discuss accident reduction strategies based on land use and vehicle type. METHODS : Traffic accident data from 2010 to 2014 were collected from the “traffic accident analysis system”(TAAS) data set of the Road Traffic Authority. To develop the accident rate model, a multiple linear regression model was used. Explanatory variables such as geometry and traffic volume were used to develop the models. RESULTS: The main results of the study are as follows. First, it was found that the null hypotheses that land use and vehicle type do not affect the accident rate should be rejected. Second, 16 accident rate models, which are statistically significant (with high R2 values), were developed. Finally, the area of the central island, number of speed humps, entry lane width, circulatory roadway width, bus stops, and pedestrian crossings were analyzed to determine their effect on accidents according to the type of land use and vehicle. CONCLUSIONS: Through the developed accident rate models, it was revealed that the accident factors at circular intersections changed depending on land use and vehicle type. Thus, selecting the appropriate location of bus stops for trucks, widening entry lanes for cars, and installing splitter islands and optimal lighting for motorcycles were determined to be important for reducing the accident rate. Additionally, the evaluation showed that commercial and mixed land use had a weaker effect on accidents than residential land use.

PURPOSES: Traffic cameras have been installed to reduce traffic accidents. The effectiveness of traffic cameras has been proved by dozens of studies, but recently questions over its effectiveness have been raised by a series of studies. In this study, the effectiveness of traffic cameras was analyzed with a focus on different road environments. METHODS : The effectiveness of the traffic cameras was analyzed by extracting the occurence frequency before and after camera installation. The effect of reduction was analyzed comprehensively considering the installation position, monitoring direction, and surrounding environment of traffic cameras. RESULTS : The result of this study is as follows. First, the installation of cameras in an area with relatively low accidental traffic was more effective. Secondly, the effect of camera installation on car-to-pedestrian collisions was better than that of car-to-car collisions. Thirdly, accidents tended to occur more frequently when cameras were installed in front of the accident-prone owing to the negative spill-over effect. CONCLUSIONS: The result can be used to guide placement of traffic cameras. Moreover, the installation of cameras with consideration of the road environment is expected to contribute to a reduction in traffic fatalities.

자율주행자동차 기술은 급속도로 발전하고 있어 그 상용화가 멀지 않 은 것으로 여겨지고 있다. 많은 기업이 2030년 이후에는 완전한 자율주 행자동차를 보급하는 것을 목표로 그 기술개발과 안전성확보에 속도를 높이고 있다. 자율주행자동차가 도입된다면 현재 발생하는 교통사고 중 90%에 가까운 비중을 차지하는 운전자의 부주의나 운전미숙, 음주운전 등의 과실로 인한 교통사고는 최소화 될 것이고 또한 고령자나 장애인 등 교통약자들이 조금 더 편리하게 이동의 자유를 누릴 수 있는 등의 긍 정적인 면이 기대되고 있지만 훨씬 고도화되는 자동차기술의 안전성 문 제라든가, 인터넷으로 연결되는 자동차 시스템에 대한 보안문제와 같은 새로운 위험 또한 존재하고 있고, 또한 사람이 직접 운행에 개입하지 않 는 자율주행자동차의 특성 상 사람의 운행지배가 배제되는 경우도 발생 할 수 있으므로 사고가 발생할 경우 과연 누가 운행자로서 책임을 지는 지 등 현행 책임 법제가 이러한 변화를 충분히 해결할 수 있는지가 문제 될 수밖에 없다. 이러한 문제의식에 기초하여 자율주행자동차가 상용화된 이후의 자동 차 사고 관련 책임 부담에 관하여 현행 법 제도 특히, 자동차손해배상보 장법과 제조물책임법의 적용 시 문제점을 검토하고, 변화하는 자동차 관 련 사고 관련 문제를 고려한 개선방안을 모색하여 보고자 한다.

Among the industrial disasters caused by drop, ladder related drop disasters are occurred the mostly. The victims are occurred continuously. This study analyzed current situation of industrial disasters for recent 10 years(2005~2014) and compared・analyzed statistics of death disasters of ladder by workplace scale, age, occupation, employment type, working content, scarred area, etc. in detail. This study suggested direction of safety standard modification for ladder that is an original cause material of many drops, direction of safety training strengthening of small-scaled workplace, and safety model based on disaster statistics and should contribute to reduction of disaster rate for ladder working.