항로표지는 해상교통의 안전을 도모하는 선박운항의 능률성을 향상시키기 위한 해양교통안전시설로서 국제적으로 IALA에서 관련 규정 및 권고사항에 따라 적용하고 있다. 우리나라는 항로표지 이용자에게 안정적인 서비스를 제공하기 위해 항로표지를 관리하고 있지만 항로 표지사고는 연평균 141건이 발생하고 있다. 항로표지사고는 관리자에게 기능을 복구시켜야 비계획 업무를 강요하고, 이용자에게는 심리적 불안 감을 유발하여 경제적인 손실이 발생한다. 본 연구에서는 항로표지사고로 인해 기능정지에서 복구까지 소요되는 관리자의 경제활동과 이용자의 불편함을 비용으로 정량화하기 위한 항로표지사고의 사회적비용(Social Cost) 평가모델을 개발하였다. 항로표지사고의 사회적비용 평가모델은 생산손실비용, 행정비용, 위험비용의 합으로 제안하였다.

In the shipping industry, it is well known that around 80 % or more of all marine accidents are caused fully or at least in part by human error. In this regard, the International Maritime Organization (IMO) stated that the study of human factors would be important for improving maritime safety. Consequently, the IMO adopted the Casualty Investigation Code, including guidelines to assist investigators in the implementation of the Code, to prevent similar accidents occurring again in the future. In this paper, a process of the human factors investigation is proposed to provide investigators with a guide for determining the occurrence sequence of marine accidents, to identify and classify human error-inducing underlying factors, and to develop safety actions that can manage the risk of marine accidents. Also, an application of these investigation procedures to a collision accident is provided as a case study This is done to verify the applicability of the proposed human factors investigation procedures. The proposed human factors investigation process provides a systematic approach and consists of 3 steps: ‘Step 1: collect data & determine occurrence sequence’ using the SHEL model and the cognitive process model; ‘Step 2: identify and classify underlying human factors’ using the Maritime-Human Factor Analysis and Classification System (M-HFACS) model; and ‘Step 3: develop safety actions,’ using the causal chains. The case study shows that the proposed human factors investigation process is capable of identifying the underlying factors and indeveloping safety actions to prevent similar accidents from occurring.

Traffic accidents increase with the increase of the vehicles in operation on the street. Especially big traffic accidents composed of over 3 killed or 20 injured accidents with the property damage become one of the serious problems to be solved in most of the cities. The purpose of this study is to build the discrimination model on big traffic accidents using the Quantification II theory for establishing the countermeasures to reduce the big traffic accidents. The results are summarized as follows. 1)The existing traffic accident related model could not explain the phenomena of the current traffic accident appropriately. 2) Based on the big traffic accident types vehicle-vehicle, vehicle-alone, vehicle-pedestrian and vehicle-train accident rates 73%, 20.5% 5.6% and two cases respectively. Based on the law violation types safety driving non-fulfillment center line invasion excess speed and signal disobedience were 48.8%, 38.1% 2.8% and 2.8% respectively. 3) Based on the law violation types major factors in big traffic accidents were road and environment, human, and vehicle in order. Those factors were vehicle, road and environment, and human in order based on types of injured driver’s death. 4) Based on the law violation types total hitting and correlation rates of the model were 53.57% and 0.97853. Based on the types of injured driver’s death total hitting and correlation rates of the model were also 71.4% and 0.59583.