• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.15 no.1
• /
• pp.1-15
• /
• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• Journal of Environmental Policy
• /
• v.2 no.1
• /
• pp.77-86
• /
• 2003

Particle-size distributions (PSDs) are widely used for the estimation of soil hydraulic properties. The objective of this study was to select the best model among the nine PSD models with different underlying assumptions, by using a variety of Korean soils. The Fredlund model with four parameters, the logistic growth curve, and Weibull distribution model showed the highest performance compared to the other models with the majority of soils studied. It was interesting to find that the logistic growth function with no fitting parameters showed a great fitting performance.

• Journal of Navigation and Port Research
• /
• v.29 no.10 s.106
• /
• pp.907-913
• /
• 2005

The rapid growth of economy in china leads to concentrate throughput volume of World to North East Asia. Ports in North East Asia have to cope with rapid changes in environments. In order for a port to compete in the global market, it must provide proper productivity and promptly services of port. The system of information hold a key in port-logistic industries. Most ports make every effort to set up and operate the system of Information. Busan port already set up the system of port-logistic information such as Port-MIS, KROIS. But it is not activated yet because of problems of documentation duplication and system connectivity and so on. The purpose of this research is to make a model which can activate the system of port-logistic information. In addition, We suggest a definite and feasible model by group and types of port-logistic industry.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.29 no.1
• /
• pp.369-375
• /
• 2005

The rapid growth of economy in china leads to concentrate throughput volume of World to North East Asia. Ports in North East Asia have to cope with rapid changes in environments. In order for a port to compete in the global market, it must provide proper productivity and promptly services of port. The system of information hold a key in port-logistic industries. Most ports make every effort to set up and operate the system of Information. Busan port already set up the system of port-logistic information such as Port-Mis, KROIS. But it is not activate yet because of problems of security and certification and so on. The purpose of this paper is to make Model which can activate the system of port-logistic information. In addition to, We suggest definite and feasible model by group and types of port-logistic industry.

• The Korean Journal of Applied Statistics
• /
• v.21 no.4
• /
• pp.571-580
• /
• 2008

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.

• Journal of Digital Convergence
• /
• v.19 no.4
• /
• pp.25-31
• /
• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• International Area Studies Review
• /
• v.14 no.2
• /
• pp.3-23
• /
• 2010

The purpose of the study is to estimate value of the Eulsukdo that is attracting lots of birds. Eulsukdo became one of the most famous eco-tourism destinations worldwide and environmental restoration work is progressing with enormous budget. The input of the budget by policy judgement basically can be justified when the benefit excesses the cost in social aspect. Eulsukdo has external effect as cultural tourism resource but it is difficult to estimate the value in market. The study is to estimate the value of Eulsukdo through the single-bounded dichotomous CVM(Contingent Valuation Methods). According to analysis results, the mean WTP(Willingness to Pay) and the truncated mean WTP are estimated at 5,240 and 3,374 won in the log-normal model, and 5,888 and 3,232 won in the log-logistic model respectively. The annual total benefits value based on the truncated mean WTP is estimated at 3,870 million won in the log-normal model and 4,040 million won in log-logistic model. The result of this study will provide useful guide to policy makers and developers who fully realize the value of public goods.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2008.10a
• /
• pp.324-328
• /
• 2008

금융산업에서 고객의 이탈비율은 기대수익에 영향을 미친다는 점에서 예측이 필요한 부분이며 최근 들어 정확한 예측을 통한 비용관리가 이루어지면서 고객 이탈을 예측하는 것이 중요한 문제로 떠오르고 있다. 그러나 보험 고객 데이터가 대용량이고 불균형한 출력 값을 갖는 특성으로 인해 기존의 방법으로 예측 모델을 만드는 것이 적합하지 않다. 본 연구에서는 대용량 데이터를 처리하는 데 효과적으로 알려져 있는 Trust-region Newton method를 적용한 로지스틱 회귀분석을 통해 이탈고객을 예측하는 것을 주된 연구로 하며, 불균형한 데이터에서의 예측정확도를 높이기 위해 Oversampling, Clustering, Boosting 등을 이용하여 고객 데이터에 적합한 이탈 고객 예측 모형을 제시하고자 한다.

• Journal of the Korean Society of Safety
• /
• v.25 no.4
• /
• pp.90-95
• /
• 2010

This study deals with the accident model of arterial link section in Cheongju. The objective is to develop the accident model of arterial link section using the logistic regression. In pursuing the above, the study uses the 258 accident data occurred at the 322 arterial link section. The main results are as follows. First, Nagellerke $R^2$ of developed accident model is analyzed to be 0.309 and t-values of variable that explains goodness of fit are evaluated to be significant. Second, the variables adopted in the model are AADT, the number of exit and entry. These variables are all analyzed to be statistically significant. Finally, the analysis of correct classification rate shows that the total accident of correct classification rate is analyzed to be 72.7% at the arterial link section.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.37 no.2
• /
• pp.113-125
• /
• 2012

The logistic model and the Bass model have diverse names and formulae in diffusion theory. This diversity makes users or readers confused while it also contributes to the flexibility of modeling. The method of handling the integration constant, which is generated in process of deriving the closed form solution of the differential equation for a diffusion model, results in two different 'actual' models. We rename the actual four models and propose the usage of the models with respect to the purpose of model applications. The application purpose would be the explanation of historical diffusion pattern or the forecasting of future demand. Empirical validation with 86 historical diffusion data shows that misuse of the models can draw improper conclusions for the explanation of historical diffusion pattern.