• 산업경영시스템학회지
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• 제41권4호
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• pp.171-178
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• 2018

Recently, the continuing operation of nuclear power plants has become a major controversial issue in Korea. Whether to continue to operate nuclear power plants is a matter to be determined considering many factors including social and political factors as well as economic factors. But in this paper we concentrate only on the economic factors to make an optimum decision on operating nuclear power plants. Decisions should be based on forecasts of plant accident risks and large and small accident data from power plants. We outline the structure of a decision model that incorporate accident risks. We formulate to decide whether to shutdown permanently, shutdown temporarily for maintenance, or to operate one period of time and then periodically repeat the analysis and decision process with additional information about new costs and risks. The forecasting model to predict nuclear power plant accidents is incorporated for an improved decision making. First, we build a one-period decision model and extend this theory to a multi-period model. In this paper we utilize influence diagrams as well as decision trees for modeling. And bayesian statistical approach is utilized. Many of the parameter values in this model may be set fairly subjective by decision makers. Once the parameter values have been determined, the model will be able to present the optimal decision according to that value.

• 대한교통학회지
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• 제30권2호
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• pp.101-116
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• 2012

사고예측모형은 장래 계획 노선이나 다른 노선에 적용되어 사고를 예측하거나 안전개선사업 및 교통정책의 평가 등에 활용된다. 본 연구에서는 고속도로 본선에 대해 이러한 용도로 활용될 수 있는 사고예측모형을 구축하고자 한다. 또한 자료 구축이 용이한 변수를 선정하여 모형을 쉽게 활용할 수 있도록 하는 것을 기본 목표로 하였다. 모형은 종속변수를 사고건수와 사상자발생사고건수로 하여 사고모형과 사상자발생사고모형을 각각 구축하였다. 모형에 적용된 확률구조는 음이항 분포와 포아송 분포이며, 추정에 의해 적합한 모형을 선별하였다. 국내 고속도로중 주요한 9개 노선을 선정하였고, 시간적으로는 2003~2007년까지 5개년도 자료를 활용하였다. 모형의 설명변수는 교통류 특성을 나타내는 교통량 등의 예측 가능한 변수와 기하구조 요인 등을 적용하였다. 최우추정법에 의한 추정 결과 사고모형의 경우 구간길이, 일교통량, 버스비율, 곡선구간수가 유의한 변수로 추정되었으며 사상자발생사고모형에서는 구간 길이와 일교통량, 버스비율이 유의한 변수로 추정되었다. 모형의 공간적 시간적 전이 가능성을 확인하기 위해 우도비 검정을 수행한 결과, 사고모형은 6차로 이상이나 4차로의 교통류 및 기하구조 특성을 가지는 도로로의 전이가 가능하였다, 반면 사상자발생 사고모형은 모든 도로와 시간대로의 전이가 가능하여, 모형의 활용도가 높게 나타났다. 결과적으로 본 연구에서 구축된 모형은 다른 노선과 장래 계획, 정책 평가 등에 다양하게 활용될 수 있을 것이다.

• 디지털융복합연구
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• 제15권12호
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• pp.313-321
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• 2017

국내의 교통사고는 지속적으로 증가하고 있다. 현재 1차 사고를 방지하기 위한 IOT 기술들이 적용되고 있지만 2차 사고방지를 위한 실질적인 대안은 제시되고 있지 않다. 단지 일반적인 지침으로 권고하고 있는 상황이다. 본 논문에서는 기존의 통신 기술을 활용하여 2차사고 방지를 위한 제안 모델 및 시뮬레이션을 구현하고 있다. 1차사고 시 운전자의 시야 확보가 가능한 상황에서는 안전거리를 확보하여 2차 사고를 예방 할 수 있지만 터널 및 산악 지형 또는 차량 통행이 많은 곡선도로와 같이 운전자의 시야확보가 어려운 지역에서는 1차사고 후 2차 사고의 확률이 점점 증가되고 있는 추세이다. 이에 본 논문에서는 1차 사고는 센서기술을 활용한 정보로 사고 판단을 하고 V2V 또는 V2I 통신을 통한 2차사고 방지를 구현하였다. 시뮬레이션을 통해 운전자의 시야가 확보되는 경우의 2차 사고는 제안 모델과 차이가 없었으나 시야가 확보되지 않고 통신이 단절된 곳에서는 제안모델이 적용된 결과 3%~7% 사고율이 감소하였다.

• 대한교통학회지
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• 제25권5호
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• pp.135-148
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• 2007

교통사고의 특성과 사고에 대한 지속시간 사이의 관계에 대한 이해는 사고의 효과적인 대응과 사고에 의한 혼잡을 감소시키는데 핵심 요소가 된다. 때문에 본 연구의 목적은 AFT metric 모형을 적용한 사고 지속시간을 분석하는 것이다. 비록 로그 로지스틱 및 로그 정규 AFT 모형이 통계적 이론과 기존 연구 사례를 기반으로 선정되었으나, 로그 로지스틱 모형이 보다 우수하게 추정되었다. AFT 모형은 예측 목적으로도 널리 사용되기 때문에, 추정된 모형은 사고 발생시 사고 관련 기본 정보 접수 즉시 고속도에서의 사고 지속시간 예측에 사용될 수 있다. 결과적으로, 예측된 사고 지속시간 정보는 사고를 처리하기 위한 제반 의사 결정에 도움을 줄 뿐 아니라 교통 혼잡의 감소 및 추가 사상자의 감소로 그 효과가 이어질 것으로 판단된다.

• 국제학술발표논문집
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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.

• 대한교통학회지
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• 제12권1호
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• pp.5-24
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• 1994

The purpose of this study is to develop an accident hazard index model in order to be used for the evaluation of regional traffic safety and to develop a driver violation index model in order to identify the primary causes of traffic accidents. The accident hazard index model was developed considering the accident rates based on population and the vehicle registration. The driver violation index model was developed considering the accident rates of each item of driver violation. Using the models developed in this study, it is identified that in the provincial level analysis the degree of the traffic safety of Chungbuk, Chungnam, and Kyungbuk Province are evaluated to be low. In the county level analysis of Kyungnam Province, the degree of the traffic safety of Yangsan, Euirung, Haman, Sachun and Tongyung County are evaluated to be low. Also, it is found that the major driver violations causing accidents in the nation are driving by unlicensed drives, improper passing, and improper railroad crossing : in Kyungnam Province, improper passing is the most driver violation.

• 한국안전학회지
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• 제30권2호
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• pp.70-76
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• 2015

This study deals with the traffic accident of circular intersections. The purpose of the study is to develop the accident models by traffic violation type. In pursuing the above, this study gives particular attention to analyzing various factors that influence traffic accident and developing such the optimal models as Poisson and Negative binomial regression models. The main results are the followings. First, 4 negative binomial models which were statistically significant were developed. This was because the over-dispersion coefficients had a value greater than 1.96. Second, the common variables in these models were not adopted. The specific variables by model were analyzed to be traffic volume, conflicting ratio, number of circulatory lane, width of circulatory lane, number of traffic island by access road, number of reduction facility, feature of central island and crosswalk.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2001년도 추계학술대회 논문집
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• pp.429-434
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• 2001

Rail crossings pose special safety concerns for modern railroad operation with faster trains. More than ninety percent of train operation-related accidents occurs on at-grade crossings. Surest countermeasure for this safety hazard is to eliminate at-grade crossings by constructing over/under pass or by closing them. These eliminations usually require substantial amount of investment and/or heavy public protest from those affected by them. Thorough and objective analysis are usually required, and valid accident prediction models are essential to the process. This paper developed an accident prediction model for Korean at-grade crossings. The model utilized many important factors such as guide personnel, highway traffic, train frequency, train sight distance, and number of tracks. Developed model was validated with actual accident data.

• International Journal of Computer Science & Network Security
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• 제23권8호
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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.

• 한국안전학회지
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• 제30권1호
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• pp.104-110
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• 2015

This study deals with the roundabout accidents. The goal of this study is to develop the sideswipe accident models at roundabout. In the pursuing the above, this study gives particular attentions to collecting the data of geometric structure and accidents of 54 roundabouts in Korea and developing the Poisson and negative binomial regression models. The main results are as follows. First, sideswipe accident is analyzed to be the highest frequency that is 39.5% of total accident data. Second, Poisson models which is statistically significant is developed. Finally, traffic volume per approach($X_1$), number of circulatory roadway($X_3$), operation of parking lot($X_4$) and width of circulatory roadway($X_6$) are adopted as the common variables. This study might be expected to give some implications to the accident research on the roundabout.