• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• Korean Journal of Occupational Health Nursing
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• v.18 no.1
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• pp.98-105
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• 2009

Purpose: This study was to analyze the current preventive measures on alcohol-involved accident and the factors that affect such preventive measures in the construction industry. Method: The survey was administered to examine how the preventive measures on alcohol-involved accident were executed using the data of '2005 Occupational Safety and Health Survey' conducted in 2005 by the Occupational Safety and Health Research Institute. For this study, we analyzed 944 work places in the construction industry. Result: The preventive measures on alcohol-involved accident were being executed in 62.1% of construction companies. As for the number of actual preventive measures on alcohol-involved accident, work places in Jeolla-do executed 2.63 times more frequently than those in Chungcheong-do, and work places with the Occupational Safety and Health Conference executed 2.22 times more frequently than those without such a measure. Conclusion: Joining the Occupational Safety and Health Conference was to be one of the most influential ways of preventive measures on alcohol-involved accident in the construction industry. Accordingly, if workers and employers look for active measures and administer them through the Occupational Safety and Health Conference, it will reduce alcohol-involved accident in the construction industry and contribute to the better preventive measures on alcohol-involved accident in the construction and other industries.

• Korean Journal of Occupational Health Nursing
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• v.9 no.1
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• pp.18-29
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• 2000

Back injury is frequent in industry workers and is a common cause of productivity loss. It has been reported that the insured of industrial accident insurance tend to stay in hospital longer than that of other types of insurance. The purpose of this study was to identify factors affecting the length of hospital stay for the treatment of back injury in the workers under industrial accident insurance. The results of this study help insurers develop reasonable industrial accident insurance policy for back injury claims and prevention strategies of work-related back injury. A total of 2,949 patients whose industrial accident insurance claim has been approved for the treatment of work-related back injury from January to December 1999 were included in this study. Relationship between the length of hospital stay and characteristics of patient, work place, back injury, and hospital were assessed using ANOVA, t-test, simple linear regression and multiple resgression. The major findings of this study are as follows : 1. The average length of hospital stay(LOS) was 91.82 days, respectively. 2. Characteristics of Patient LOS of male patients was longer than that of female patients, there was positive correlation between age and LOS and between average wage and LOS. Working period was negatively correlated with LOS. Distance from resident to hospital was positively correlated with LOS and LOS was significantly different dependign on type of duty. 3. Characteristics of Work Place LOS was significantly different depending on types of industry and geographical region of work place. Size of work place was positively correlated with LOS. 4. Characteristics of Back Injury Occupational back pain required shorter LOS compared with back injury due to electric shock. Number of concomitant illnesses and severity of disability were positively correlated with LOS. 5. Characteristics of Hospital Patients treated in community hospitals required significantly longer LOS. Treatment in hospitals with rehabilitation program required decreased LOS. This was more prominent as number of physicians specialized in rehabilitation. 6. Multiple regression analysis revealed that distance form resident to hospital, geographical region of work place, size of work place, number of concomitant illnesses, severity of disability, and type of hospital were factors affecting LOS.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.45-50
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• 2020

The number of elevators in Korea has surpassed 700,000 units in 2019, which is the 8th in the world by number of installed units and 3rd in the world by new units. The word 'lift' is a representative word, and the category includes elevators, escalators, dumb waiters, and moving walks. Those who live in the city will experience using elevators once or twice a day, and these elevators are becoming an indispensable means of transportation when using high-rise apartments or subways. However, such a convenient elevator also has a lot of risks that threaten the safety of the user and actually cause many accidents every year. In particular, escalators (including moving walks), which account for as little as 5% of all elevators, account for 70% of all elevator accidents. According to Heinrich's chain of thought theory, accidents are caused by a combination of factors, which are divided into five stages: Stage 1: Genetic Factors and Social Environment, Stage 2: Individual Defects, Stage 3: Unsafe behavior and Unsafe conditions, Stage 4: Accident, Stage 5: Injury. Heinrich said that three of these five phases, unsafe behavior and unsafe conditions, require safety management and efforts to prevent accidents. In escalator accidents, the analysis of accident cases that have occurred so far will be related to unsafe behaviors and unsafe conditions, and the effective management of these causes of accidents will enable safer and more convenient use of escalators. This study analyzed accident cases of elevator users, focusing on escalator accidents over the last 10 years (2010 ~ 2019), and safety management to prevent safety accidents of elevator users by analyzing the behavior of actual users and questionnaires of experts in related fields. The method was studied.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2467-2471
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• 2008

Railway accident analysis results show that accidents cased by human factors are not decreasing, whereas H/W related accidents are steadily decreasing. For the efficient management of human factors, many expertise on design, conditions, safety culture and staffing are required. But current safety management activities on safety critical works are focused on training, due to the limited resource and information. In order to establish railway human factors management requirements, human factors management status on all train operating companies are analysed in this study.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.7-16
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• 2000

This paper is focused on verifying time-space repetition of the highway accident and finding the their causes and deterrents. We classify all months into several seasonal groups, develop the model for each seasonal group and analyze the results of these models for Joong-bu highway. The existence of seasonal effect is verified by the analysis or self-organizing map and the accident indices. Agglomerative hierarchical cluster analysis which is used to decide the seasonal groups in accordance with accident patterns, winter group, spring-fall group. and summer group. The accident features of winter group are that the accident rate is high but the severity rate is low. while those of summer group are that the accident rate is low but the severity rate is high. Also, the regression model which is developed to identify the accident Pattern or each seasonal group represents that the season-related factors, such as the amount of rainfall, the amount of snowfall, days of rainfall, days of snowfall etc. are strongly related to the accident pattern of evert seasonal group and among these factors the traffic volume, amount of rainfall. the amount of snowfall and days of freezing importantly affect the local accident Pattern. So, seasonal effect should be considered to the identification of high-risk road section. the development of descriptive and Predictive accident model, the resource allocation model of accident in order to make safety management plan efficient.

• Proceedings of the Safety Management and Science Conference
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• 2003.11a
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• pp.75-80
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• 2003

The objectives of this study is to achieved by the use of the conceptual approach and accident data bases to develop statistical accident analysis, effectiveness values, comparison analysis of statistical models to determine which variables are significantly related to accidents, human factor, and hazard factor analysis, all of which were used in the railroad crossing. The result from this approach applicable to the railroad crossing where systematic safety management criteria have been considered.

• Journal of the Korean Society of Safety
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• v.20 no.2 s.70
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• pp.91-97
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• 2005

The purpose of this study is to provide basic data for establishment of prevention counterplan against construction accidents in preparation for variation of climate conditions. In order to execution of this study, it was analyzed relations of climate factors and cases of construction accident occurred construction sites. In occurrence of construction accidents inducing death upon variation of Climate factors, precipitation and wind velocity were not related directly to construction accidents inducing death. On the other hand, the more temperature and humidity are high, the more construction accidents inducing death occurred. Especially, when temperature and humidity are above $24^{\circ}C,\;70\%$ respectively, field managers must pay attention to safety management of construction sites.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.138-141
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• 2015

The South Korean construction industry has shown a high proportion of industrial accidents (approximately 28% of whole injuries) and the continuously increasing accident rate. Although many safety research emphasized that the 3E (Enforcement, Education, and Engineering) approach is a potential solution to enhance workplace safety, there should be benefits to consider psychological (i.e., Emotional) effects on the safety performance since most construction works are human-oriented. Thus, understanding construction workers' psychological conditions can be a priority. This research studied the relationships between psychological conditions-which cover stress, personal temperament, emotional disturbance, and drinking habit-and specific safety-related factors including safety motivation and knowledge, and safety performance of individual workers at a construction site. This study conducted a survey of 430 respondents and analyzed the data with the multiple linear regressions. The results imply persistence, trait anxiety, and problem-focused coping style are the critical factors that should be controlled for enhancing jobsite safety. Finally, the research outcomes could be applied to build a strategic safety management plan for a construction manager.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.