• Environmental and Resource Economics Review
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• v.16 no.3
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• pp.427-449
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• 2007

This study estimates the recreation benefits of rafting on the Hantan River. A choice experiment is conducted and the economic values of controlling water stream and water quality are estimated. Both the conditional logit and the multinomial pro bit models are estimated. This study rejects the IIA assumption of the conditional log it model and supports using a more flexible model such as the multinomial probit model.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.75-84
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• 2010

South Korea is seeking for a solution to the problems of traffic congestion and environment: the increase of bicycle use. However many people feel that using a bicycle is inconvenient. Therefore, we developed bicycle level of service model from the user's perspective so that the existing bicycle roads can be evaluated and improved. The purpose of this paper is to develop a bicycle level of service (LOS) model by considering user's satisfaction and multiple factors that affect bicycle LOS. Bicycle LOS criteria is estimated by applying an ordered probit model, which is suitable for research relating to choice. In addition, we determine the bicycle LOS using three-level(A-C) LOS structure from the user position considering the satisfaction level that people can distinguish clearly. The results show that the bicycle LOS is largely determined by the bicycle road width. Other factors are involved as well, including bicycle road type, the number of access and egress point on the bicycle road corridor, pedestrian volume, and frequency of meetings.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.108-117
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• 2009

The South Korean government is looking for a solution to the ever-growing problems of traffic congestion, and surging international oil prices: the use of the humble bicycle to get to places. However, Many people feel inconvenient using bicycle because of the insufficient bicycle infrastructure and lack of the safety and connectivity between existing pathways. In this study, bicycle level of service model using ordered probit model is developed considering safety, convenience, connectivity, and factors that affect bicycle LOS. The ordered probit model would be recommended for the research which relates in choice, preference and strength etc. Bicycle level of service criteria is calculated by applying this model reflecting bicyclist's point of view. The model which develops from this research which accomplishes a bicycle level of service evaluation and represent alternative solution to encourage bicyclist. It is believed that the proposed model would be greatly utilized in bicycle network planning, bicycle road and facility alternatives testing, projects funding priority.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.68-79
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• 2014

This paper analyzes the characteristics of trip chaining activities of elderly and explores temporal and spatial distribution. The research also estimates ordered probit model and binary logistic model to investigate various factors affecting trip chaining and mode choice patterns. We utilized household survey data for elderly conducted in 2006 and 2010 in Seoul metropolitan area. Research results indicate that trip chaining showed an increasing trend and simple trip chaining counts for more than 85%. GIS mapping expressed spatial distribution of trip departure and arrival areas, particularly showing regional changes in job-related trips. We also found that more factors influence trip chaining in 2010, compared with 2006, and travel cost is more sensitive than travel time in determining travel mode. The research contributes to establish transportation policies based on travel behavior of elderly in a upcoming super-aged society.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.127-135
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• 2009

Most traffic information that drivers receive while driving are stored in their short-term memory and disappear within a few seconds. Contemporary modeling approaches using a dummy variable can't fully explain this phenomenon. As such, this study proposes to use utility functions of real-time in-vehicle traffic safety information (IVTSI), analyzing its safety impacts based on empirical data from an on-site driving experiment at signalized intersection approach with a limited visibility. For this, a driving stability evaluation model is developed based on driver's driving speed choice, applying an ordered probit model. To estimate the specified utility functions, the model simultaneously accounts for various factors, such as traffic operation, geometry, road environment, and driver's characteristics. The results show three significant facts. First, a normal density function (exponential function) is appropriate to explain the utility of IVTSI proposed under study over time. Second, the IVTSI remains in driver's short-term memory for up to nearly 22 second after provision, decreasing over time. Three, IVTSI provision appears more important than the geometry factor but less than the traffic operation factor.

• Journal of Environmental Policy
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• v.6 no.1
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• pp.83-109
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• 2007

This study evaluates citizen's willingness-to-pay for the benefits from improved water quality of the Taehwa river in Ulsan, Korea, using a contingent valuation method with double-bounded dichotomous choice. The estimation results of the bivariate probit model shows the amounts of willingness-to-pay are monthly 3,458.5 Korean Won per household and yearly 14,760 million Korean Won for total households in Ulsan, Korea. These estimates are equivalent to the social values of improved water quality of the Taehwa river. This study also tests the inter-dependence between two answers, which may occur in the responses of the questions for the double-bounded dichotomous choice, and all the null hypotheses on the inter-dependence are rejected in this study.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.199-211
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• 2008

This study deals with the side right-angle collisions of 4-legged signalized intersections in Cheongju. The goals are to analyze the characteristics of accidents and to find out the accident factors that affect severity using ordered probit model. In pursuing the above, the study uses the data of 580 side right-angle collisions occurred at the 181 intersections(2004-2005). The analyses show that more accidents were occurred in the nighttime and in going straight. The main cause was analyzed to be the red-light violation. Also, the main results of modeling are the following, First, the likelihood ratio index is 0.094 and t-ratio values that explain goodness of fit are significant. Second, minor road traffic volumes, minor road lanes, major road left-turn lanes, major road left-turn signal, major road yellow signal time, cross angle, major and minor road speed limits are significant factors affecting crash severities at signalized intersections.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.13-22
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• 2018

Buses, one of the representative public transportation modes, are divided into a vareity of service types according to the purpose of operation, operating distance, and management agencies. Although bus-involved crashes may cause large amount of damage due to the higher number of passengers boarded on a bus, prior research has little focused on crash severity according to bus service types. This study aims to investigate factors influencing crash severity in bus-involved crashes and to present policy implications to reduce crash severity by bus service type. To do this, bus-involved crash data from the Traffic Accident Analysis System (TAAS) during five-year period are used. Ordered probit models for three types of bus service, i.e., city bus, suburban and express buses, and charter buses, are estimated to analyze the factors of accident severity. The results show that there are significant differences of factors affecting crash severity among the types of bus services while speed and road surface influence all the types of buses. In case of local buses, time of day, roadway alignment, and installation of a traffic signal are found to be statistically significant factors. Seat belt and road class have significant effects on injury severity of the intercity and express buses. Chartered buses have time of day, driving experience, seatbelt, traffic signal, and day of week as the significant factors. The results of this study are expected to contribute to the reduction of the crash severity by each bus service type.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.917-923
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• 2018

In recent, technological advancement including a vehicle black box (VBB) has led to reducing such underreporting issues and errors of crash data. The objective of this study is to analyze the injury severity of cyclists on taxi-to-two wheeler crashes based on the accurate crash data collected from the VBB in taxi. This study defined the two wheelers as bicycle and motorcycle. To perform this study, we used the VBB data collected from taxis operating in Incheon, South Korea for a two-year period (2010-2011). An ordered probit model was applied to analyze the injury severity in crashes. As a result, new injury severity factors were found: increase of the crash speed of taxi, damage of crash-involved vehicles (i.e., taxi and/or two wheeler), not standing of cyclists after crash, and second or third impact of cyclists after first crash.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.367-382
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• 2018

Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.