• Journal of the Korean Data and Information Science Society
• /
• v.24 no.1
• /
• pp.125-133
• /
• 2013

In this paper, penalized binary logistic regression models are employed as statistical models for determining the discharge of 668 patients with a chief complaint of dyspnea based on 11 blood tests results. Specifically, the ridge model based on $L^2$ penalty and the Lasso model based on $L^1$ penalty are considered in this paper. In the comparison of prediction accuracy, our models are compared with the logistic regression models with all 11 explanatory variables and the selected variables by variable selection method. The results show that the prediction accuracy of the ridge logistic regression model is the best among 4 models based on 10-fold cross-validation.

• Journal of Korean Society of Transportation
• /
• v.33 no.1
• /
• pp.81-89
• /
• 2015

Crashes that occur at tollgates have different characteristics compared to those of the mainline on expressways in terms of crash cause, crash type, and vehicle type. Due to this fact, the safety performance function (SPF) focused on the expressway tollgates, apart from the mainline, should be developed. The aim of this study is, therefore, to identify the influential factors and develope a SPF for crashes at tollgates. Firstly, we established independent variables affecting crashes at tollgates through literature review and descriptive statistical analysis. Based on these variables, two negative binomial regression models with different form of independent variables were developed and goodness-of-fits of each model were compared. According to the results, the number of crashes increases i) as AADT, Hi-pass rate, and heavy vehicle rate increase, ii) as average lane width decreases, iii) on the mainline tollgate type. The safety performance function developed in this study could be applied to select hot-spots for expressway tollgates.

• Journal of The Korean Society For Urban Railway
• /
• v.6 no.4
• /
• pp.373-381
• /
• 2018

The floating population that is index to figure out dynamic activities in urban area will be important in urban railway planning, but it is not useful because it is collected by posterior method. This study aims to investigate factors influencing floating population. The floating population data that was collected in Seoul for a month in December 2013 is used as dependent variable, and the negative binomial regression analysis is used in modelling. The number of households, number of employees, number of subway stations, and number of bus lines variables are statistically significant in predicting floating population.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.1
• /
• pp.1-16
• /
• 2022

Recently, e-scooters have been attracting attention as eco-friendly modes of transportation in cities due to an increasing interest in the environment. Accordingly, various studies on usage behavior are being conducted, but studies that reflect individual travel attitudes are insufficient. Therefore, this study surveyed commuters in Seoul and analyzed respondents' traveling attitudes through factor analysis. It also built a binary logistic regression model for the intention to use shared e-scooters to determine how individual travel behaviors are affected. In particular, the model results showed that age, the main mode of transportation (car), walking time to the bus stop, and four travel attitude variables (disutility of travel, preference to self-drive, internet/smartphone friendliness, and willingness to pay extra money for services) significantly affected the intention to use shared e-scooters. This study is expected to be used as basic data, with aspect to travel behavior, for the efficient operation and use of shared e-scooters in the future.

• Journal of Korean Society of Transportation
• /
• v.28 no.2
• /
• pp.151-159
• /
• 2010

This paper presents a research result that was performed to develop a more accurate freeway crash prediction model than existing models. While the existing crash models only focus on developing crash relationships associated with highway geometric conditions found on a short section of a crash site, this research applies a different approach considering the upstream highway geometric conditions as well. Theoretically, crashes occur while motorists are in motion, and particularly at freeways vehicle speed at one specific point is very sensitive to upstream geometric conditions. Therefore, this is a reasonable approach. To form the analysis data base, this research gathers the geometric conditions of the West Seaside Freeway 269.3 km and six years crash data ranging 2003-2008 for these freeway sections. As a result, it is found that crashes fit well into Negative Binomial Distribution, and, based on the developed model, total number of crashes is inversely proportional to highway curve length and radius. Contrarily, crash occurrences are proportional to tangent length. This result is different from existing crash study results, and it seems to be resulted from this research assumption that a crash is influenced greatly by upstream geometric conditions. Also, this research provides the expected effects on crash occurrences of the length of downgrade sections, speed camera placements, and the on- and off- ramp presences. It is expected that this research result is useful for doing more reasonable highway designs and safety audit analysis, and applying the same research approach to national roads and other major roads in urban areas is recommended.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.1
• /
• pp.9-18
• /
• 2016

In the Heckman-Meyers model, which is frequently referred by IAA, Swiss Solvency Test, EU Solvency II, the assumption of parameter distribution is key factor. While in theory Bayesian analysis somewhat reflects parameter uncertainty using prior distribution, it is often the case where both Heckman-Meyers and Bayesian are necessary to better manage the parameter uncertainty. Therefore, this paper proposes the use of Bayesian H-M CRM, a combination of Heckman-Meyers model and Bayesian, and analyzes its efficiency.

• Korean Journal of Construction Engineering and Management
• /
• v.15 no.1
• /
• pp.111-121
• /
• 2014

The Clean Development Mechanism (CDM) allows New & Renewable Energy projects to make additional income by selling CER's, which represent the amount of Green House Gases(GHG) that is reduced in the project. However, forward contracts used to hedge fluctuating market prices does not allow projects to sell CER's at a premium. As an alternate approach to maximize CER revenue, CER's are modeled as a 'real option', in which CER's are sold only above the desired sales price. Using the Binomial Option Pricing model, the resultant lattices are used to determine whether to sell, defer or abandon the option at individual nodes. Overlaying Pascal's Triangle on the lattices also enabled the calculation of the annual probabilities for deferring CER sales without incurring downside losses. Application to an actual Landfill Gas project showed increased overall NPV, and that CER sales could be deferred at a maximum of 2 years. The proposed framework allows transparency in the analysis and provides valuable and strategical information when making investment decisions related to CER sales of CDM projects.

• Journal of Korean Society of Transportation
• /
• v.29 no.5
• /
• pp.7-17
• /
• 2011

Traffic problems caused by drunk drivers have been steadily raised from the past. Even though the previous researches have focused on the development of countermeasures for preventing drunk driving, the number of drivers violating the DUI (Driving-Under-Influence) regulation is still increasing. Many studies seek countermeasures for preventing drunk driving by comparing the differences between general and drunk drivers. However, few researches have investigated focusing only on the characteristics of drunk drivers. It is well known that characteristics of general drivers are different from those of drunk drivers, and also habitual drunk drivers have different characteristics from non-habitual drunk drivers. Motivated by this fact, only the drivers who have violated DUI regulation are considered in the analysis. This study primarily aims to provide alternative solutions for reducing habitual drunk drivers who are highly inclined to do drunk driving repeatedly. For the analysis, various types of variables potentially effecting drunk driving behavior were investigated, and then truncated count data models were developed to analyze the effects of the variables selected on drunk driving. The results showed that 1) a truncated negative binomial model is better fitted to the data; and 2) five variables including experiential learning, the lack of self-control, self-reflection, the fear of crackdown, and the level of dependence on vehicles were found to be statistically significant.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.6
• /
• pp.887-894
• /
• 2018

An electric vehicle is emerging as an alternative to the response of global climate change and sustainability. However, an Electric vehicle has not been popular due to the constraints such as its price or technical limitations. In order to analyze the effect of purchasing electric vehicles, this study conducted a binary logistic regression model that demonstrates the relation between purchasing and influencing variables. Variables which have high correlation were excluded from the model through the correlation analysis to prevent multicollinearity. Socio-economic variables such as the number of owned vehicles, sex, ages are not significant. On the other hand, Variables related to prices, charging and policy are found to have a significant to effect on the purchase of electric vehicles. In accordance with the model estimated result, it seems to be necessary to improve the charging incentives, or to provide electric car information and to expand opportunities for experience electric vehicles. The result is also expected to be helpful for spreading electric vehicles and formulating policies.

• The Korean Journal of Applied Statistics
• /
• v.31 no.4
• /
• pp.475-484
• /
• 2018

Gamma generalized linear models are useful for non-negative and skewed responses. However, these models have received less attention than Poisson and binomial generalized linear models. In particular, hypothesis testing for the significance of regression coefficients has not been thoroughly studied. In this paper we assess the performance of various test statistics for gamma generalized linear models based on numerical studies. Our results show that the likelihood ratio test and F-type test are generally recommended and that the partial deviance test should be avoided in practice.