• Journal of the Society of Disaster Information
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• 제9권1호
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• pp.30-49
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• 2013

In this research we developed a new traffic accident forecasting model on the basis of land use. A new traffic accident forecasting model by type was developed based on market segmentation and further introduction of variables that may reflect characteristics of various regions using Classification and Regression Tree Method. From the results of analysis, activities variables such as the registered population, commuters as well as road size, traffic accidents causing facilities being the subjects of activities were derived as variables explaining traffic accidents.

• Safety and Health at Work
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• 제11권4호
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• pp.443-449
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• 2020

Background: The study tries to show the effect of Occupational Health and Safety (OHS) legislation implemented via plan-do-check-act methodology on accident and sickness absenteeism. Methods: The data for the study gathered via a questionnaire from a large-sized organization operates in production and maintenance of passenger coaches in February-March 2019 in Turkey. The data analyzed via structural equation model analysis. Results: The results showed that there are statistically meaningful relationships between OHS protective measures, training and informing of employees, and employee participation and accident and sickness absenteeism. In addition, a meaningful relationship between internal control and accident and sickness absenteeism was determined. Statistically meaningful relationships between emergency measures, and health surveillance and internal control, and accident and sickness absenteeism could not be determined. Conclusion: It is concluded that the actions implemented by organizations to reduce absenteeism should be as per OHS legislation.

• Korean Journal of Human Ecology
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• 제13권5호
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• pp.741-749
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• 2004

The purpose of this study was parents' perception on young children's safety life, safety accident, and safety education and provided basic data of administrating parent education for young children's safety. Subjects of this study were 620 parents (310 fathers and 310 mothers) of young children attending at four public kindergartens and two day care centers located in C city and D county. The results of the study were as follows: First, regarding parents' perception on young children's safety life, parents thought that their perception and attitude would mostly affect young children' safety life. Second, with regard to parents' perception on safety accident, half of parents experienced such safety accident as accident during play, traffic accident, accident in dangerous places, accident from dangerous matters, accident in sport activities, fire, and electric shock. Third, most parents looked upon safety education as very important one, and fathers were more satisfied with the safety education administered by kindergartens. Fourth, with regard to parents' perception on parent education for young children's safety, most parents thought that parent education for safety would be necessary. They ranked traffic and play accidents as the most important contents of safety education.

• International Journal of Highway Engineering
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• 제17권6호
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• pp.75-83
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• 2015

PURPOSES : This study deals with the traffic accidents classified by the traffic analysis zone. The purpose is to develop the accident density models by using zonal traffic and socioeconomic data. METHODS : The traffic accident density models are developed through multiple linear regression analysis. In this study, three multiple linear models were developed. The dependent variable was traffic accident density, which is a measure of the relative distribution of traffic accidents. The independent variables were various traffic and socioeconomic variables. CONCLUSIONS : Three traffic accident density models were developed, and all models were statistically significant. Road length, trip production volume, intersections, van ratio, and number of vehicles per person in the transportation-based model were analyzed to be positive to the accident. Residential and commercial area ratio and transportation vulnerability ratio obtained using the socioeconomic-based model were found to affect the accident. The major arterial road ratio, trip production volume, intersection, van ratio, commercial ratio, and number of companies in the integrated model were also found to be related to the accident.

• Journal of the Ergonomics Society of Korea
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• 제35권3호
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• pp.155-163
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• 2016

Objective: This study aims to draw the characteristics of occupational accidents occurred in cabling work, and assess accident severity based on occupational injury data. Background: Accident factors and accident risk are different by the place of work in cabling work. Field managers require information on accident prevention that can be easily understood by workers. However, there has been a lack of studies that focus on cabling work in Korea. Method: This study classifies 450 injured persons caused in cabling work by process, and analyzes the characteristics of occupational injuries from the aspects of age, work experience and accident type. This study also analyzes accident frequency and severity of injury. Results: Results show that preparing/finishing (33.3%) was the most common type of cabling process in injuries, followed by maintenance (28.4%), routing/income (23.1%) and wiring/installation (15.1%) process. The critical incidents in the level of risk management were falls from height in the routing/incoming process, and falls from height in the maintenance process. And, incidents ranked as 'High' level of risk management were slips and trips, fall from height and vehicle incident in the preparing/finishing process, and fall from height in the wiring/installation process. Conclusion and Application: The relative frequency of accident and its severity by working process serve as important information for accident prevention, and are critical for determining priorities in preventive measures.

• International conference on construction engineering and project management
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• 제17권3호
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• pp.87-97
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• 2018

The traffic accident reports that are generated by the Traffic Broadcasting Networks(TBN) are unstructured data. It, however, has the value as some sort of real-time traffic information generated by the viewpoint of the drives and/or pedestrians that were on the roads, the time and spots, not the offender or the victim who caused the traffic accidents. However, the traffic accident reports, which are big data, were not applied to traffic accident analysis and traffic related research commonly. This study adopting text-mining technique was able to provide a clue for utilizing it for the impacts of traffic accidents. Seven years of traffic reports were grasped by this analysis. By analyzing the reports, it was possible to identify the road names, accident spot names, time, and to identify factors that have the greatest influence on other drivers due to traffic accidents. Authors plan to combine unstructured accident data with traffic reports for further study.

• Journal of Korean Society of Disaster and Security
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• 제16권1호
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• pp.71-80
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• 2023

Fuel cells are low-carbon power sources that can expand distributed energy system and electric vehicle charging infrastructure when installing fuel cells in gas stations. In order to ensure safety for fuel cells in gas stations, quantitative risk assessments were conducted after deriving accident scenarios based on accident data of domestic and foreign gas stations and fuel cells. It calculates the expected extent of damage from fire and explosion that can occur in reality, not the worst accident scenario, and analyzes the damage impact. The separation distance of more than 9.0 m from a dispenser, 15.5 m from a car under refueling, 4.1 m from the ventilation pipe, 1.1 m from the gas adjustment device prevent the severe damage caused by the expected accident. This study result can be used to deploy fuel cells in gas stations and establish safety measures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.138-141
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• 2015

K-water operates many Water Treatment Plants(WTP) to supply clean water to people. There are automation process control equipments collecting data at each step in WTP. The data collected is big enough to 370,000Tag/min from the K-water Water Treatment Plants. In the past, this big data was not important, we focused on the operating water purification process using the data. Currently, we increased the importance of attention to take advantage of Big Data. The research about the accident reduction and efficiency improvement in WTP are ongoing by data collection and analysis. In this paper, we analyzed the flow rate, power and pressure obtained in the accident case in WTP. We researched the methods for accident prediction and reduction.

• Fire Science and Engineering
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• 제33권6호
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• pp.72-79
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• 2019

Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.