• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.203-213
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• 2021

A vehicle crash occurs due to various factors such as the geometry of the road section, traffic, and driver characteristics. A safety performance function has been used in many studies to estimate the relationship between vehicle crash and road factors statistically. And depends on the purpose of the analysis, various characteristic variables have been used. And various characteristic variables have been used in the studies depending on the purpose of analysis. The existing domestic studies generally reflect the average characteristics of the sections by quantifying the traffic volume in macro aggregate units such as the ADT, but this has a limitation that it cannot reflect the real-time changing traffic characteristics. Therefore, the need for research on effective aggregation units that can flexibly reflect the characteristics of the traffic environment arises. In this paper, we develop a safety performance function that can reflect the traffic characteristics in detail with an aggregate unit for one hour in addition to the daily model used in the previous studies. As part of the present study, we also perform a comparison and evaluation between models. The safety performance function for daily and hourly units is developed using a negative binomial regression model with the number of accidents as a dependent variable. In addition, the optimal negative binomial regression model for each of the hourly and daily models was selected, and their prediction performances were compared. The model and evaluation results presented in this paper can be used to determine the risk factors for accidents in the highway section considering the dynamic characteristics. In addition, the model and evaluation results can also be used as the basis for evaluating the availability and transferability of the hourly model.

• Journal of Korean Society of Transportation
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• v.33 no.6
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• pp.575-583
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• 2015

The Korea Transport Institute (KOTI) builds the origin and destination(O-D) trip data with relatively smaller zone size such as Eup, Myeon, Dong administration unit districts in metropolitan area. Otherwise, O-D trip data was built by bigger size of traffic analysis zone(TAZ) such as Si, Gun, Gu administration unit districts for rural area. In some cases, it is needed to divide a zone into several sub-zones for rural area in order to analyze travel distribution pattern in detail for a certain highway and rail project. The study suggested a method to estimate O-D trips for sub-zones in the larger-size zones in rural area. Two different distribution models, direct demand model and gravity model, were calibrated for sub-zone's intra-zonal O-D trip pattern with metropolitan area O-D data which has smaller zone-size (sub-zone) data categorized by low, middle and high population density. The calibration results were compared between the two models. The gravity model with impedance function of power functional form was selected with better explanation for all groups in the metropolitan area. The adjusted $R^2$ was 0.7426, 0.6456 and 0.7194 for low, middle and high population density group, respectively. The suggested O-D trip estimating method is expected to produce enhanced trip patterns with sub-divided small zones.

• Journal of Korean Society of Transportation
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• v.32 no.4
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• pp.369-379
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• 2014

The Statistical regression model has been used to construct crash prediction models, despite its limitations in assuming data distribution and functional form. In response to the limitations associated with the statistical regression models, a few studies based on non-parametric methods such as neural networks have been proposed to develop crash prediction models. However, these models have a major limitation in that they work as black boxes, and therefore cannot be directly used to identify the relationships between crash frequency and crash factors. A genetic programming model can find a solution to a problem without any specified assumptions and remove the black box effect. Hence, this paper investigates the application of the genetic programming technique to develope the crash prediction model. The data collected from the Gyeongbu expressway during the past three years (2010-2012), were separated into straight and curve sections. The random forest technique was applied to select the important variables that affect crash occurrence. The genetic programming model was developed based on the variables that were selected by the random forest. To test the goodness of fit of the genetic programming model, the RMSE of each model was compared to that of the negative binomial regression model. The test results indicate that the goodness of fit of the genetic programming models is superior to that of the negative binomial models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.849-858
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• 2014

The mount of antecedent 5-day rainfall (P5) is usually used to determine the antecedent soil moisture condition for estimating effective rainfall using the NRCS-CN method. In order to re-establish the threshold of P5 considering basin characteristics, this study investigated the sensitivity of the threshold of P5 to effective rainfall by comparing the corresponding observed direct runoff. The overall results indicate that the direct runoff estimated using the re-establihed threshold of P5 has smaller mean error (RMSE of 27.3 mm) than those using the conventional threshold (RMSE of 35.2 mm). In addition, after evaluating the effectiveness of threshold of P5 using the improvement index, the threshold re-established in this study improved the ability to estimate the direct runoff by 30% on average. This study also suggested to employ regression models using topographic indices to re-establish the threshold for ungauged basins. When using the re-established threshold from the regression model, the RMSE decreased ranging from 0.4 mm to 15.1 mm and the efficiency index of Nash and Sutcliffe increased up to 0.33.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.557-564
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• 2019

The characteristics of the torque and torque ripple of Interior Permanent Magnet Synchronous Motor(IPMSM) are influenced by the size and position of the rotor magnet and the size of the stator slot. This paper deals with the optimal design method for improving torque and torque ripplerate for IPMSM using Response Surface Methodology(RSM). Two objective functions of torque output and torque ripple were derived from the sensitivity analysis by Plackett-Burmann(PB) for the characteristic variables affecting torque and torque ripple. Secondary characteristic variables were selected from the derived objective function and RSM secondary regression model function was estimated by the experiment schedule and analysis results according to the Central Composite Design (CCD). The reliability of the secondary regression model was verified using ANOVA table. The analysis according to the experimental schedule was verified by JMAG(Ver. 18.0) which is Finite Element Method(FEM) software. The torque output of IPMSM applied with final characteristic variables was increased torque output by 11.5 % and the torque ripplerate was reduced by 9.1 %.

• Journal of Korea Port Economic Association
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• v.23 no.1
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• pp.1-18
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• 2007

The purpose of this study is to examine the effect of exchange rate volatility on Trading Volume of Container of Korea, and to induce policy implication in the contex of GARCH and regression model. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply impulse response functions and variance decomposition to the structural model to estimate dynamic short run behavior of variables. The major empirical results of the study show that the increase in exchange rate volatility exerts a significant negative effect on Trading Volume of Container in long run. The results Granger causality based on an error correction model indicate that uni-directional causality between trading volume of container and exchange rate volatility is detected. This study applies impulse response function and variance decompositions to get additional information regarding the Trading Volume of Container to shocks in exchange rate volatility. The results indicate that the impact of exchange rate volatility on Trading Volume of Container is negative and converges on a stable negative equilibrium in short-run. Th exchange rate volatility have a large impact on variance of Trading Volume of Container, the effect of exchange rate volatility is small in very short run but become larger with time. We can infer policy suggestion as follows; we must make a stable policy of exchange rate to get more Trading Volume of Container

• Management & Information Systems Review
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• v.28 no.3
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• pp.1-23
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• 2009

Prior bankruptcy studies have established that bankrupt firm's pre-filing financial ratios are different from those of healthy firms or of randomly selected going concerns. However, they may not be sufficiently different from the financial ratios of other firms in financial distress to allow the development of a ratio-based model that predicts bankruptcy with reasonable accuracy. As the result, in the multiple discriminant model, independent variables divided firms into bankrupt firms and healthy firms are retained earnings to total asset, receivable turnover, net income to sales, financial expenses, inventory turnover, owner's equity to total asset, cash flow to current liability, and current asset to current liability. Moreover four variables Retained earnings to total asset, net income to sales, total asset turnover, owner's equity to total asset indicate that these valuables classify bankrupt firms and distress firms. On the other hand, Owner's Equity to borrowed capital, Ordinary income to Net Sales, Operating Income to Total Asset, Total Asset Turnover and Inventory Turnover are selected to predict bankruptcy possibility in the Logistic regression model.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.422-422
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• 2012

지하수개발 이용의 허가시 지하수 양수로 인한 주변지역에 미치는 영향을 조사하여 지하수의 고갈과 오염을 예측하고 이를 사전에 방지함으로써 지하수의 보전과 합리적인 이용을 도모하고자 지하수영향조사제도가 시행되어 왔다. 특히 하천구역의 경계로부터 300미터 내의 지역에서 지하수를 개발 이용하는 경우에는 지하수영향조사서를 첨부하여 국토해양부장관과 미리 협의하도록 되어있고, 이 때 지하수개발 이용이 하천의 수량에 영향을 미친다고 인정하는 경우에는 취수량 취수 기간의 제한 및 취수 금지 등을 요청할 수 있다. 그러나, 하천인근의 지하수 양수가 하천수에 미치는 영향을 정량적으로 평가할 수 있는 기법이 마련되어있지 않아 실무적으로 지하수영향조사 및 허가 절차상 어려움을 겪고 있다. 따라서 본 연구에서는 지하수 이용에 따른 하천수량 변화를 예측할 수 있는 간편 상관관계식을 지표수-지하수 통합모의 결과를 이용하여 유도 제시하였다. 지표수-지하수 통합모의를 위해서 SWAT-MODFLOW 결합모형을 적용하였고, 두 개의 시험유역에 대해 가상의 양수정 설치에 따른 하천수량 변화량을 평가하는 시나리오 분석을 수행하였다. 상관관계는 다중회귀분석을 통해서 하천수 감소량을 지하수 양수량, 하천과 양수정 이격거리, 대수층 및 하천바닥의 수리전도특성, 강수량 등의 함수로 나타내었다.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.83-92
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• 2015

Concerning the national health, it is important to develop an accurate prediction method of atmospheric particulate matter (PM) because being exposed to such fine dust can trigger not only respiratory diseases as well as dermatoses, ophthalmopathies and cardiovascular diseases. The National Institute of Environmental Research (NIER) employs a decision tree to predict bad weather days with a high PM concentration. However, the decision tree method (even with the inherent unstableness) cannot be a suitable model to predict bad weather days which represent only 4% of the entire data. In this paper, while presenting the inaccuracy and inappropriateness of the method used by the NIER, we present the utility of a new prediction model which adopts boosting with quantile loss functions. We evaluate the performance of the new method over various ${\tau}$-value's and justify the proposed method through comparison.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.