• Journal of Navigation and Port Research
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• v.35 no.4
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• pp.281-287
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• 2011

According to the Marine Traffic Safety Law, revised in 2009, Marine Traffic Safety Audit is introduced to secure the safe navigation, to prevent the marine accident and to maximize the efficiency of the port. In this audit system, marine traffic safety assessment is the most important scheme because the primary purpose of the audit system is to identify potential risk elements affecting safe navigation. Even though the reliability of audit result depends on the selection of assessment models, there are no independent assessment models for Korean coastal waters and most of models used in Korea currently are developed by foreign countries. Therefore, the development of the independent assessment model for Korean coastal water is required. This study, prior to the development of independent assessment model, aims to provide a basic data by comparing two foreign assessment models in Ulsan port area with marine accident statistics data.

• Journal of the Korean Society of Safety
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• v.29 no.1
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• pp.54-58
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• 2014

This study deals with the accidents of circular intersections in Korea. The goal is to develop the accident models for 94 circular intersections. In pursuing the above, this study gives particular attentions to collecting the data of geometric structure and accidents, and comparatively analyzing such the models as Poisson and NB regression and multiple regression model using SPSS 17.0 and LIMDEP 3.0. The main results are as follows. First, the negative binomial model among various models was analyzed to be the most appropriate. Second, 3 independent variables was adopted in the model, and these variables was analyzed to have a positive relation to the accident rate. Finally, the reduced width of circulatory roadway, removal of the parking lot within circulatory roadway and appropriate levels of approach lane were required to improve the safety of circular intersection.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.103-110
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• 2011

This study deals with the traffic accidents by type. The objectives are to analyze the characteristics of 2 accident types, and to develop the models by type. In pursuing the above, this paper gives particular attentions to testing the differences between by type two groups, and developing the models (Poisson and negative binomial regressions) using the data of domestic circular intersections. The main results are as follows. First, the number of accidents in vehicle vehicle was analyzed to account for about 73.41% of total and to be higher than vehicle people. Second, two Poisson models and two negative binomial models which were all statistically significant were developed using vehicle people accidents and vehicle vehicle accidents as dependant variables. Finally, the traffic volume as common variable was selected in the models, and right-turn slip lane, speed hump, the number of driveways, the number of pedestrian crossings as specific variables of the models were selected.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.8
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• pp.721-725
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• 2016

This paper reviews the analysis of a given scenario according to the Hybrid Model, and why accident causation models are necessary in casualty investigations. The given scenario has been analyzed according to the Hybrid Model using its main five components, fallible decisions, line management, psychological precursors to unsafe acts, unsafe acts, and inadequate defenses. In addition, the differences between the SHEL and the Hybrid Model, and the importance of a safety barrier during an accident investigation, are shown in this paper. One unit of SHEL can be linked with another unit of SHEL. However, it cannot be used for the analysis of an accident. Therefore, we must use an accident causation model, which can be a Hybrid Model. This can explain the "How" and "Why" of accident, so it is a suitable model for analyzing them. During an accident investigation, the reason we focus on a safety barrier is to create another safety barrier or to change an existing safety barrier if that barrier fails. Hence, the paper shows how a sea accident can be investigated, and we propose a preventive way of avoiding the accident through combining the methods of different models for the future.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.130-135
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• 2017

This study deal with the traffic accident. The purpose of this study is to develop the accident density models reflecting the transportation and socioeconomic characteristics based on 230 zones of Korea. In this study, The models which are tested to be statistically significant are developed through multiple linear regression analysis. The main research results are as follows. First, in the transportation-based model, road length, avenue ratio, number of intersections and tunnels are analyzed to be positive to the model, however, school zone is analyzed to be negative to the model. Second, in the socioeconomic-based model, population density, transportation vulnerable ratio, children and truck ratio are analyzed to be positive to the model. Finally, in the integrated models, road ratio, population density, transportation vulnerable ratio, children ratio, truck ratio and number of companies are analyzed to be positive, however, school zone is analyzed to be negative to the model. This results could be expected to give good implications to accident-reduction policy-making.

• International Journal of Highway Engineering
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• v.15 no.2
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• pp.131-138
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• 2013

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.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.190-198
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• 2009

This study shows the developing process of the risk assessment models for railway casualty accidents. To evaluate the risks of these accidents, the hazardous events and the hazardous factors were identified by the review of the accident history and engineering interpretation of the accident behavior. The frequency of each hazardous event was evaluated from the historical accident data and structured expert judgments by using the Fault Tree Analysis (FTA) technique. In addition, to assess the severity of each hazardous event, the ETA (Event Tree Analysis) technique and other safety techniques were applied. The risk assessment models developed can be effectively utilized in defining the risk reduction measures in connection with the option analysis.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.19-29
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• 2008

The main purpose of this study is to introduce Hauer's(2004) approach that overcomes current accident prediction models' limitation and to apply this approach to Korean situation using fatal accident data on motorways. After developing accident prediction models according to this approach, it is found that AADT and vertical grade could improve fitness of the model, whereas a radius of roads is not related to the number of accidents. The advantage of Hauer's approach is to reduce possibility to eliminate critical variables and to keep uncritical variables when we consider many variables to develop accident prediction models.