• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.4 no.2
• /
• pp.1-11
• /
• 1984

This aims of this study is to analyse the correlationship between traffic accident s and traffic characteristic variables in at-grade intersections of urban area, to build up an accident forecasting model and to propose an evaluation method of hazardous at-grade intersections. The accident forecasting model is formulated by the use of residual indexes that is selected by principal component analysis and its statistical significance is tested by step-wise regression analysis. Effective countermeasures for safety can be established on the basis of identifying high accident intersections, because the validity of this model was examined and found to coincide with real world situations.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2019.05a
• /
• pp.45-46
• /
• 2019

Accident rate in domestic construction industry has been increased rapidly in every year. In particular, the rate of death has been shown very high compared with other industries. It means that safety activities performed by government is not effective in reducing the rate of accident. To solve these problems, the risk factors should be predicted in advance, controlled, monitored and managed from start of project to end of project. However, most studies have been conducted by using frequency of occurrence of accident and only listed the importance of risk. Therefore, the objective of this study is to provide basic material to develop risk quantifying model for human accidents on construction site in South Korea. In the future, it is expected to be used as a reference of study on developing safety mangement checklist in construction industry and model for forecasting accident.

• Journal of the Korean Society for Railway
• /
• v.20 no.4
• /
• pp.482-490
• /
• 2017

This study is a railway accident investigation statistic study with the purpose of prediction and classification of accident severity. Linear regression models have some difficulties in classifying accident severity, but a logistic regression model can be used to overcome the weaknesses of linear regression models. The logistic regression model is applied to escalator (E/S) accidents in all stations on 5~8 lines of the Seoul Metro, using data mining techniques such as logistic regression analysis. The forecasting variables of E/S accidents in urban railway stations are considered, such as passenger age, drinking, overall situation, behavior, and handrail grip. In the overall accuracy analysis, the logistic regression accuracy is explained 76.7%. According to the results of this analysis, it has been confirmed that the accuracy and the level of significance of the logistic regression analysis make it a useful data mining technique to establish an accident severity prediction model for urban railway casualty accidents.

• Journal of Korean Society of Transportation
• /
• v.29 no.6
• /
• pp.39-56
• /
• 2011

This study proposed a traffic accident prediction model developed based on administrative districts of Seoul. The model was to find the relationship between accident rates and the representative land usage of the districts (development density) - the higher the development density (building floor area) is, the higher the traffic accident rate is. The findings showed that traffic accident statistics differ from (1) residential building floor area, (2) commercial building floor area and (3) business building floor area.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2018.11a
• /
• pp.122-123
• /
• 2018

Although the disaster rate of the industry as a whole is on a downward trend, the disaster rate of the construction industry is on an ongoing trend. Therefore, in this study, we analyzed safety accident statistical data of the construction site over the past three years. As a result of the analysis, the incidence of disasters at small construction sites was very high. And the proportion of disaster occurred for workers who worked in less than 6 months even roughly 92.6%. In addition, as a result of analyzing the form of disaster occurrence, the crash was 34.1% and the fall was 15.1%. The analysis results of these construction safety accidents are to provide as a basic material for developing a policy that can prevent safety accidents and a safety accident prediction model.

• Journal of Korean Society of Transportation
• /
• v.29 no.4
• /
• pp.103-111
• /
• 2011

The speed is one of the significant factors affecting accident occurrence. In particular, freeway accidents are highly associated with the speed because vehicles travel on the freeway at higher speed leading to greater potential of severer injury. Efforts attempting to relating speed with accident occurrence have not been significantly made in Korea. The objective of this study is to model the relationship between speed and accident rate on freeways. Loop detector data and accident data obtained from a stretch of Kyungboo freeway during the recent five years, 2005-2009, were used to establish the model. Multiple linear regression analyses showed that median, minimum and standard deviation of speed were contributing variables in the model. The statistical significance identified by the analyses supports the feasibility of the model in evaluating various transportation policies and operations strategies in terms of traffic safety.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.55
• /
• pp.25-31
• /
• 2000

To make a better decision about when to shutdown a nuclear power plant, we build a decision model using influence diagrams. We proceed the analysis adopting a bayesian approach. Firstly, an accident arrival rate is assumed to be known and this assumption is relaxed later. We perform our analysis on the cases of exponential time to accidents, and gamma distribution for the arrival rate. An optimal shutdown time is obtained considering the trade-off between the costs incurred by an accident due to late shutdown and the possible loss of revenues due to the early shutdown. We also derive the upper bound of the failure rate where we may operate the plant.

• Journal of the Korean Institute of Gas
• /
• v.22 no.5
• /
• pp.107-113
• /
• 2018

While accident data are used to show alertness to accidents or to review similar cases, the analysis of nature of accident data its association with surrounding environment is very insufficient. Therefore, it is very necessary to demonstrate the possibility of an accident for a particular region by developing analysis techniques with the related accident data. The purpose of this study is to develop an analysis model and implement a system that produces regional accident probability based on historical weather information data and accident and reporting data. In other words, the system is designed and developed to create models by k-NN and decision tree algorithms with optional user-environment variables based on the probability between weather and accidents about many particular region of Korea. In the future, the models developed in this study are intended to be used to analyze and calculate the risk of a more narrow area.

• Journal of Korean Society of Water and Wastewater
• /
• v.25 no.4
• /
• pp.581-589
• /
• 2011

Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

• The Korean Journal of Applied Statistics
• /
• v.30 no.1
• /
• pp.181-193
• /
• 2017

The paper introduces bottom-up and optimal combination methods that can analyze and forecast hierarchical time series. These methods allow forecasts at lower levels to be summed consistently to upper levels without any ad-hoc adjustment. They can also potentially improve forecast performance in comparison to independent forecasts. We forecast regional traffic accident counts as time series data in order to identify efficiency gains from hierarchical forecasting. We observe that bottom-up or optimal combination methods are superior to independent methods in terms of forecast accuracy.