The Severe Disaster Punishment Act had recently been established in order to promote safety and health (OSH) management system for severe accident prevention. OSH management system is primarily designed based on risk assessments; however, companies in industries have been experiencing difficulties in hazard identification and selecting proper measures for risk assessments and accident prevention. This study intended to introduce an accident analysis method based on epidemiological model in finding hazard and preventive measures. The accident analysis method employed in this study was proposed by the U.S. Department of Energy. To demonstrate the effectiveness of the accident analysis method, this study applied it to two accident cases occurred in construction and manufacturing industries. The application process and results of this study can be utilized in improving OSH management system and preventing severe accidents.

본 연구의 목적은 과거 12년(2010~2021년)간 발생한 상선의 충돌사고 668건을 조사하여 충돌의 원인요인을 조사하고 이를 통계 적으로 분석하여 항해사의 인적과실 예방 충돌회피(HEPCA) 모델을 제안하는 것이다. 중앙해양안전심판원의 통계연보 및 충돌사건 재결서 를 조사하여 상선의 충돌 원인요인 데이터를 수집하고 통계분석 도구인 SPSS를 이용하여 빈도분석을 수행하였다. 1단계 분석으로 상선 충 돌사고 668건을 대상으로 충돌원인을 분석하였고, 2단계 분석에서는 식별된 최대빈도 원인요인을 세부적으로 분석하였다. 분석결과, 충돌 원인은 항해사의 인적과실이 98 %인 것으로 식별되었으며, 빈도 높은 요인 순서는 경계소홀 〉항행법규위반 〉조선 부적절 순이었다. 경계 소홀의 원인 요인은 주로 상대선 초인 후 지속감시 소홀이었으며 상대선박의 존재를 인식하지 못한 원인은 주로 당직시간에 다른 작업을 한 요인이었다. 분석결과를 적용하여 인적과실 예방을 위한 HEPCA 모델을 제안하였고, 이를 재결서의 충돌사건에 적용해보았다. 본 연구결과는 해기사 교육기관 및 실무에서 항해사의 인적과실로 발생하는 충돌사고를 방지하기 위한 교육 자료로 활용이 가능할 것으로 기대된다.

The leading source of occupational fatalities is a portable ladder in Korea because it is widely used in industry as work platform. In order to reduce victims, it is necessary to establish preventive measures for the accidents caused by portable ladder. Therefore, this study statistically analyzed injury death by portable ladder for recent 10 years to investigate the accident characteristics. Next, to monitor wearing of safety helmet in real-time while working on a portable ladder, this study developed an object detection model based on the You Only Look Once(YOLO) architecture, which can accurately detect objects within a reasonable time. The model was trained on 6,023 images with/without ladders and safety helmets. The performance of the proposed detection model was 0.795 for F1 score and 0.843 for mean average precision. In addition, the proposed model processed at least 25 frames per second which make the model suitable for real-time application.

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.

PURPOSES: There are many recently constructed roundabouts in Jeollabuk-do province. This study analyzed how roundabouts reduce the risk of accidents and improve safety in the province. METHODS: This study analyzed safety improvement at roundabouts by using an accident prediction model that uses an Empirical Bayes method based on negative binomial distribution. RESULTS : The results of our analysis model showed that the total number of accidents decreased from 130 to 51. Roundabouts also decreased casualties; the number of casualties decreased from 7 to 0 and the seriously wounded from 87 to 16. The effectiveness of accident reduction as analyzed by the accident prediction model with the Empirical Bayes method was 60%. CONCLUSIONS : The construction of roundabouts can bring about a reduction in the number of accidents and casualties, and make intersections safer.

PURPOSES: The purpose of this study is to verify traffic accident injury severity factors for elderly drivers and the relative relationship of these factors. METHODS: To verify the complicated relationship among traffic accident injury severity factors, this study employed a structural equation model (SEM). To develop the SEM structure, only the severity of human injuries was considered; moreover, the observed variables were selected through confirmatory factor analysis (CFA). The number of fatalities, serious injuries, moderate injuries, and minor injuries were selected for observed variables of severity. For latent variables, the accident situation, environment, and vehicle and driver factors were respectively defined. Seven observed variables were selected among the latent variables. RESULTS: This study showed that the vehicle and driver factor was the most influential factor for accident severity among the latent factors. For the observed variable, the type of vehicle, type of accident, and status of day or night for each latent variable were the most relative observed variables for the accident severity factor. To verify the validity of the SEM, several model fitting methods, including , GFI, AGFI, CFI, and others, were applied, and the model produced meaningful results. CONCLUSIONS: Based on an analysis of results of traffic accident injury severity for elderly drivers, the vehicle and driver factor was the most influential one for injury severity. Therefore, education tailored to elderly drivers is needed to improve driving behavior of elderly driver.

PURPOSES : The intents of the study are to identify the accident factors and to demonstrate the potentials of tobit model as a tool to study the number of accidents on arterial roads segments. METHODS : This paper uses a tobit regression as a methodology to analyze the factors affecting the number of accidents. In pursuing the above goal, this study gives particular attentions to analyzing the data of 2,446 accidents (1,610 in major arterial roads and 836 in minor arterial roads) occurred on arterial roads in 2007 to 2010. RESULTS : First, 3 accident models which were classified by total arterial roads, major arterial roads and minor arterial roads, and were all statistically significant were developed. Second, the exclusive right-turn lane as common variable, and the number of accident, traffic volume, number of lanes, link length, rate of median, number of entrances, number of pedestrian crossings, number of curves, number of bus stops and exclusive left-turn as specific variables of the models were selected. Finally, the paired sample t-test could not be rejected the null hypotheses of three types of models. CONCLUSIONS : Using data from vehicle accidents on arterial roads, the estimation results show that many factors related to roadway geometrics and traffic characteristics significantly affect to the number of accidents.

고령화가 진행될수록 고령운전자의 수 역시 증가될 것으로 예상되어 향후 고령운전자에 의한 교통사고는 급증할 것으로 판단된다. 본 연구는 고속도로 교통사고 발생시 고령층과 비고령층의 구분에 따른 교통사고 특성을 분석하였다. 분석결과 비고령층에 비해 고령층에 작용하는 영향요인이 다르게 나타남을 알 수 있었다. 로짓모형을 통해 비고령층과 고령층의 Odds Ratio를 분석하여 사고영향요인에 따른 차이점을 알아보았으며 고령운전자의 사고 분석 모형을 개발하였다. 비고령층에 비해 곡선구간 및 절토구간, 노면의 습기상태일 때 고령운전자의 사고위험이 높은 것으로 나타났다.

본 연구는 도로기하구조 요인과 교통사고간의 관계를 규명하기 위하여 CART분석을 이용하여 전국의 4차로 국도를 대상으로 교통사고예측모형을 개발하고, 다중회귀모형, 확률회귀모형과 CART분석모형을 비교 분석하여 개발한 모형의 적합도를 검증하였다. 연구결과로는 첫째, 변수간의 복합적인 상호관계를 설명할 수 있는 CART분석을 이용하여 국도의 교통사고 예측모형을 개발하고 도로기하구조 요인에 따라 표준교통사고율을 의미하는 교통사고발생도표를 제시하였다. 둘째, CART분석모형에 근거하여 교통사고 발생률에 큰 영향을 미치는 도로기하구조 요인이 구간거리(km), 횡단보도폭(m), 횡단길어깨(m), 교통량 순으로 나타났다. 셋째, CART분석모형의 적합도 검증결과, CART분석모형이 실제교통사고율을 타 모형에 비해 전반적으로 잘 묘사하고 있었으나, 각 모형별로 교통사고율의 크기에 따라 교통사고율이 비교적 낮은 구간에서는 다중회귀모형이, 평균이상의 교통사고율을 나타내는 구간에서는 포아송 회귀모형의 예측력이 높았으며, CART분석모형은 교통사고율의 크기와 상관없이 우수한 예측력을 보였다. 넷째, 도출된 교통사고발생도표는 도로기하구조 조건에 따른 표준교통사고율을 제시해주기 때문에 도로설계 시에 안전한 기하구조 설계요소 선정기준을 제시 할 뿐만 아니라, 교통사고 잦은 지점개선사업추진 시 사업의 우선순위를 판단할 수 있는 기준을 제시하는 등 정책적 활용도가 매우 높을 것으로 판단된다.

The present study has investigated the patterns and the causes of safety -accidents on the accident-data in semiconductor Industries through near miss report the cases in the advanced companies. The ratio of incomplete actions to incomplete state was 4 to 6 as the cases of accidents in semiconductor industries in the respect of Human-ware, Hard- ware, Environment-ware and System-ware. The ratio of Human to machine in the attributes of semiconductor accident was 4 to 1. The study also investigated correlation among the system related to production, accident, losses and time. In semiconductor industry, we found that pattern of safety-accident analysis is organized potential, interaction, complexity, medium. Therefore, this study find out that semiconductor model consists of organization, individual, task, machine, environment and system.

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.