When a fire breaks out in a distribution center, it causes a lot of damage. And the most casualties are caused by Fire accidents. Therefore, training for fire prevention should be mandatory at the distribution center. Also, the contents of education should be different in room temperature warehouses and low temperature warehouses. Fire education in low-temperature warehouses should be more emphasized. This is because many fires occur in low-temperature warehouses. In this study, a study was conducted to determine the important order of training hours and contents for fire prevention education according to the type of distribution center. The importance of time and content for safety education in all types of warehouses did not differ significantly. It was first decided that safety prevention training should be conducted periodically in all types of warehouses

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

겨울철 국내 도로 결빙으로 인한 교통사고가 증가하는 추세를 보이고 있으며 2018년~2022년까지 총 4,609건의 결빙 교통사고가 발 생하였다. 결빙 교통사고의 치사율은 2.3으로 일반적인 교통사고와 비교하여 높은 치사율을 보이며 최근 5년(2018~2022)동안 결빙 교 통사고로 인하여 107명이 사망자와 7,728명의 부상자가 발생하였다. 현재 국토교통부에서 제시한 결빙 취약구간 평가기준표에 따라 결 빙 위험 구간을 지정하고 있으나, 해당 기준은 결빙의 주요 요인으로 고려되는 기상조건을 충분히 반영하지 못하고 있다. 도로 결빙은 노면온도가 0℃ 이하이며 노면에 수분이 공급될 때 형성되며 기온, 구름량, 풍속, 풍향, 상대습도, 강수량 등의 기상인자들이 복합적으 로 작용하여 결빙이 발생한다는 점을 고려하였을 때, 기상 특성은 도로 결빙의 주요 인자로 판단된다. 따라서 국내 결빙 취약구간 평 가기준의 개선이 필요하며 본 연구의 목적은 국내 결빙 교통사고 데이터를 분석하고 결빙이 형성되는 기상 조건을 구체화하는 것이다. 분석을 위한 데이터로 2018년~2022년까지 5년동안 발생한 결빙사고 사례와 기상청 방재기상관측소(AWS)에서 제공하는 기상 데이터 를 적용하였다. 이후, 박스도표, 확률밀도함수 등의 통계분석을 적용하여 결빙 형성 기상 조건을 구체화하였다. 이를 통하여 기존 결빙 취약구간 평가기준의 기상학적 개선 방향성을 제시할 수 있으며 더 나아가 도로 결빙 예측 로직 개발을 기대할 수 있다.

In recent years, deaths and missing people have continuously occurred due to man overboard (MOB) and suicide on passenger ships. However, due to the complex deck structure and enlargement of passenger ships, closed-circuit television (CCTV) coverage is limited, and it is difficult to prevent accidents for all passengers with limited crews. Therefore, a real-time system for detecting high-risk blind spots on passenger ships is needed through risk analysis. This study used a combination of the following three risk factors to calculate and evaluate the risk of MOB and suicide by deck area of the passenger ship: 1) distance away from guard rails, 2) the visibility of CCTV, and 3) ship operating conditions. Based on the survey from experts, risk scores of MOB and suicide accidents by deck area on a passenger ship were yielded.

PURPOSES : This study aims to understand the characteristics of accidents involving autonomous vehicles and derive the causes of accidents from road spatial information through autonomous vehicle accident reports. METHODS : For this study, autonomous vehicle accident reports collected and managed by the CA DMV were used as data sources. In addition, spatial characteristics and geometric data for accident locations were extracted by Google maps. Based on the collected data, the study conducted general statistics, text embedding, and cross-analysis to understand the overall characteristics of autonomous vehicle accidents and their relationship with road spatial features. RESULTS : The analysis results for characteristics of autonomous vehicle accidents, applying statistical analysis and text embedding techniques, reveal that the damages caused by autonomous vehicle accidents are often minor, and approximately half of the accidents are triggered by other vehicles. It is noteworthy that accidents where autonomous vehicles are at fault are not uncommon, and when the cause of the accident is within the autonomous vehicle, the accident risk can increase. The accident analysis results using spatial data showed that the severity of accidents increases when on-street parking is present, when dedicated lanes for bicycles and buses exist, and when bus stops are present. CONCLUSIONS : Through this study, geometric and spatial elements that appear to have an impact on autonomous driving systems have been identified. The findings of this study are expected to serve as foundational data for improving the safety of autonomous vehicle operations in the future.

PURPOSES : The purpose of this study is to statistically analyze the meteorological factors that contribute to the formation of road surface icing based on actual cases of icing accidents and provide directions for improving icing evaluation criteria. METHODS : In this study, we collected cases of domestic road icing accidents by searching news articles with the keyword ‘icing collision accidents’. Subsequently, we determined the latitude, longitude, and altitude of accident locations using satellite map service. We applied the Inverse Distance Weighting (IDW) method and temperature lapse rate to estimate meteorological data at each location. Finally, statistical analysis was conducted for temperature, humidity, and precipitation occurrence using probability density functions. RESULTS : As a result, road icing accident data points with identifiable location coordinates were collected. Among these, temperature, humidity, and precipitation occurrence from Automated Weather Stations (AWS) data were selected for analysis. During the process of correcting meteorological factors using the Inverse Distance Weighting (IDW) method, the optimal Weighting Exponent (p) that minimizes the error was determined and applied. The results showed that accidents occurring in the morning indicated the highest accident occurrence rate. The average temperature at the time of the accidents was -1.4°C, with a humidity level of 85.1%. Precipitation was observed at the time of the accident in 19 cases. CONCLUSIONS : Icing on pavement can occur not only under extreme weather conditions but also under typical meteorological conditions. Typically, icing can occur when the relative humidity is above 70%. Accordingly, for future improvements in the evaluation criteria for icing-prone areas by the Ministry of Land, Infrastructure and Transport, it is possible to incorporate the temperature and humidity ranges that generally lead to icing, taking into account climate characteristics.

This study analyzed the factors affecting the effectiveness of the Chemical Hazard Risk Management (CHARM). A survey was conducted on 104 learners who participated in the Risk Assessment training course at Occupational Safety and Health Training Institute. Through a self-administered questionnaire, the effect of personal characteristics, corporate characteristics, and safety and health level of the company on the effectiveness of chemical risk assessment was investigated. As a result of statistical analysis, the safety and health level of the company had a positive (+) effect on the effectiveness of Chemical Hazard Risk Management(CHARM), but personal characteristics and corporate characteristics had no relation to it. This study can be used as basic data for further research related to chemical risk assessment in workplaces.

PURPOSES : This study aims to analyze the causes of pedestrian traffic accidents on community roads. METHODS : This study collected variables affecting pedestrian traffic accidents on community roads based on field surveys and analyzed them using negative binomial regression and zero-inflated negative binomial regression models. RESULTS : Model analysis results showed that the negative binomial regression model is more suitable than the zero-inflation negative binomial regression model. Additionally, the segment length (m), pedestrian volume (persons/15 min), traffic volume (numbers/15 min.), the extent of illegal parking, pedestrian-vehicle conflict ratio, and one-way traffic (one: residential, two: commercial) were found to influence pedestrian traffic accidents on community roads. Model fitness indicators, comparing actual values with predicted values, showed an MPB of 1.54, MAD of 2.57, and RMSE of 7.03. CONCLUSIONS : This study quantified the factors contributing to pedestrian traffic accidents on community roads by considering both static and dynamic elements. Instead of uniformly implementing measures, such as pedestrian priority zones and facility improvements on community roads, developing diverse strategies that consider various dynamic factors should be considered.

PURPOSES : This study investigates the factors affecting extra-long tunnel accidents by integrating data on tunnel geometry, traffic flow, and traffic accidents and derives the underlying implications to mitigate the severity of accidents. METHODS : Two processes centered on three key data points (tunnel geometry, traffic flow, and traffic accidents) were used in this study. The first is to analyze the spatial characteristics of extra-long tunnel traffic accidents and categorize them from multiple perspectives. The other was to investigate the factors affecting extra-long tunnel traffic accidents using the equivalent property-damage-only (EPDO) of individual accidents and the aforementioned data as the dependent and independent variables, respectively, by employing an ordered logistic regression model. RESULTS : Gyeonggi-do, Gyeongsangnam-do, and Gangwon-do are three metropolitan municipalities that have a significant number of extra-long tunnel accidents; Busan and Seoul have the most extra-long tunnel accidents, accounting for 23.2% (422 accidents) and 18.6% (339 accidents) of the 1,821 accidents that occurred from 2007 to 2020, respectively. In addition, approximately 70% of extra-long tunnel traffic accidents occurred along tunnels with lengths of less than 2 km, and Seoul and Busan accounted for over 60% of the top 20 extra-long tunnels with accidents. Most importantly, the Hwangryeong (down) tunnel in Busan experienced the most extra-long tunnel traffic accidents, with 77 accidents occurring during the same period. As a result of the ordered logistic regression modeling with EPDO and multiple independent variables, the significant factors affecting the severity of extra-long tunnel traffic accidents were determined to be road type (freeway, local route, and metropolitan city road), traffic flow (speed), accident time (year, summer, weekend, and afternoon), accident type (rear end), traffic law violations (safe distance violation and center line violation), and offending vehicles (van, sedan, and truck). CONCLUSIONS : Based on these results, the following measures and implications for mitigating the severity of extra-long tunnel traffic accidents must be considered: upgrading the emergency response level of all road types to that of freeways and actively promoting techniques for regulating high-speed vehicles approaching and traversing within extra-long tunnels are necessary. In addition, the emergency response and preparation system should be reinforced, particularly when the damage from extra-long tunnel traffic accidents is more serious, such as during the summer, weekends, and afternoons. Finally, traffic law violations such as safe distance and centerline violations in extra-long tunnels should be prohibited.

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

The potential impact of hypothetical accidents that occur during the immediate and deferred dismantling of the Kori Unit 1 steam generator has been comprehensively evaluated. The evaluation includes determining the inventory of radionuclides in the Steam Generator based on surface contamination measurements, assuming a rate of release for each accident scenario, and applying external and internal exposure dose coefficients to assess the effects of radionuclides on human health. The evaluation also includes calculating the atmospheric dispersion factor using the PAVAN code and analyzing three years of meteorological data from Kori NPP to determine the degree of diffusion of radionuclides in the atmosphere. Overall, the effective dose for residents living in the Exclusion Area Boundary (EAB) of Kori NPP is predicted, an it is found that the maximum level of the dose is 0.034% compared to the annual dose limit of 1 mSv for the general public. This implies that the potential impact of hypothetical accidents on human health discussed above is within acceptable limits.

The preliminary study was conducted on the use of navigation instruments and programs conducted in the previous demonstration and experiment of the training ship SAEBADA, real-time sharing and accuracy of land and sea information, and the development of real-time effective information transmission and reception and management system programs. Based on this, this study used the training ship CHAMBADA, which is similar in length to the tonnage of the fishing boat. The purpose is to present errors in ship situation-specific information data, alarm systems for marine information, and land and ship transmission and reception programs collected using intelligent information management systems to find out whether they can be applied and used in fishing boats. It plans to conduct research on direct and indirect safety and ship’s stability when the intelligent information management system operates in real time in the operation of ships considering the characteristics of small fishing boats and the characteristics of fishing.

This study aims to understand the current status of exemptions for traffic accidents during the return of emergency vehicles and to provide suggestions for improvement. A survey was conducted on 3,500 firefighters to investigate the perception of traffic accidents during the return of emergency vehicles, and responses from 505 participants were analyzed. Based On the demographic characteristics and perception of the participants, frequency analysis and variance analysis were used as research methods to analyze basic statistics and the current situation. The results showed that firefighters have concerns and anxieties about traffic accidents during the return of emergency vehicles, and the need for applying exemptions and enacting explicit legal provisions was statistically confirmed. Based on these results, we suggest a policy for exemptions to improve the preparation for re-deployment and to alleviate the concerns and anxieties of firefighters.

Ensuring the safe arrival of delivery cargo at its intended destination is of utmost importance. Truck drivers play a crucial role in guaranteeing the secure delivery of cargo without any mishaps. However, there are various factors that may lead to delayed arrival of trucks at their destination, such as late departures or prolonged loading operations. The timely departure of cargo transportation is contingent upon several variables, including the driver's experience, cargo volume, and loading time. If the transportation commencement is delayed, it may increase the risk of accidents due to an elevated operating speed. Consequently, we conducted a study to investigate the correlation between cargo loading time, cargo volume, driving experience, and the likelihood of accidents. Our findings indicate that both cargo volume and driver experience can impact the likelihood of vehicle accidents. Furthermore, all factors can have an interactive effect on the occurrence of accidents. However, extending the loading time may mitigate the impact on the likelihood of accidents.

PURPOSES : This study aims to conduct a sensitivity analysis to determine the major factors affecting traffic accidents involving elderly pedestrians. METHODS : In this study, a regression tree model was built based on a non-parametric statistical model using data on traffic accidents involving elderly pedestrians. Using this model, we analyzed the degree of change in the probability of pedestrian fatalities. RESULTS : Results of the model analysis show that the first major factor combination affecting traffic accidents involving elderly pedestrians is speeding, night time, and road markers. The second combination is night time and arterial roads (national and local highways). The last combination that may lead to such accidents is heavy vehicles and federally funded local highways. CONCLUSIONS : Preventive measures, such as speed control, proper lighting, median strips, designation of pedestrian protection zones, and guidance of detours, are necessary to manage high-risk combinations causing accidents of the elderly.

After the Fukushima nuclear power plant accident in 2011, interest in technology for evaluating residents’ exposure to effluents generated from nuclear power plants at the time of the accident has increased. KHNP has developed the S-REDAP program and is using it to evaluate radiation dose and recommend resident protection measures in the event of a nuclear power plant emergency. Its main functions are source term evaluation, atmospheric diffusion evaluation, radiation dose evaluation, etc. Based on these evaluations, resident protection measures are evaluated. In Japan, evaluation is conducted through a program called SPEEDI-MP (System for Prediction of Environmental Emergency Dose Information Multi-model Package) created by JAEA (Japan Atomic Energy Agency). Similar to S-REDAP, the program also evaluates effluents emitted from nuclear facilities through source term evaluation and atmospheric diffusion factor evaluation. In JAEA, through a program using SPEEDI-MP, the source term evaluation was performed in collaboration with NSC (Nuclear Safety Commission) in the event of the Fukushima nuclear plant accident, and dose evaluation in Japan was performed 2 months as an atmospheric diffusion factor using meteorological data for 2 days. Through comparative analysis of evaluation data from Japan, improvements to the current program be derived.