• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.149-155
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• 2007

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.47-55
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• 2005

In recent years, the rapid growth of vehicles have increased traffic crashes. Since they can cause the economic losses and have put the life qualify in danger, there should be numerous efforts to reduce traffic crashes. To reduce traffic crashes, this research seeks to improve the safety of intersections by analysing causations of injury severity with Ordered Probability Model. This research applied the Ordered Probit Model, which assumes that ${\epsilon}_i$(random error) is normally distributed, for model calibration and used $p^2$ (likelihood ratio) and $x^2$ (Chi-square) for model selection. The results show that minor road traffic, heavy vehicle rates, major and minor right-turn rates, presence of lightings, speed limits, instructive line for left-turn traffic are significant factors affecting crash severities at signalized intersections.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.193-198
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• 2008

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.43-54
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• 2011

After implementing "Semi-public management system of intra-city bus", the burden of financial aid for unprofitable routes is on the increase in Busan metro city. It becomes a heavy burden on the local finance, which needs to be resolved for improving the intra-bus system. The rainfall is one of the factors influencing the demands for intra-bus in urban transportation. Motivated by this fact, this study investigates the impact of rainfall on the intra-city bus demand. Actual bus users are surveyed on their patterns and recognition of using the bus according to the amount of rainfall. A rainfall forecast model using ordered probit model is presented, and the elasticity of the intra-city bus utilization to the amount of rainfall is also analyzed. The resulting findings could be applied to promote the use of intra-city buses and also be utilized as basic data for other studies to improve the intra-city bus system.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.363-376
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• 2012

Excessive zeroes are often observed in ordinal categorical response variables. An ordinary ordered Probit model is not appropriate for zero-inflated data especially when there are many different sources of generating 0 observations. In this paper, we apply a two-stage zero-inflated ordered Probit (ZIOP) model which incorporate the zero-flated nature of data, propose a Bayesian analysis of a ZIOP model, and apply the method to alcohol consumption data collected by the National Bureau of Statistics, Korea. In the first stage of a ZIOP model, a Probit model is introduced to divide the non-drinkers into genuine non-drinkers who do not participate in drinking due to personal beliefs or permanent health problems and potential drinkers who did not drink at the time of the survey but have the potential to become drinkers. In the second stage, an ordered probit model is applied to drinkers that consists of zero-consumption potential drinkers and positive consumption drinkers. The analysis results show that about 30% of non-drinkers are genuine non-drinkers and hence the Korean alcohol consumption data has the feature of zero-inflated data. A study on the marginal effect of each explanatory variable shows that certain explanatory variables have effects on the genuine non-drinkers and potential drinkers in opposite directions, which may not be detected by an ordered Probit model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.391-398
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• 2019

In general, truck-involved crashes increase severity in terms of both injury level and crash impact level. Recently, although the frequency and fatality of truck-involved crashes in Korea are rising, their associative studies are very limited. Therefore, the objective of this study is to identify critical factors influencing on injury severity of truck-involved crashes on Korean freeway system. To carry out this objective, this study uses an ordered probit model (OPM) based on a 6-year crash dataset from 2012 to 2017. From the analysis, eight variables were found to have a great effect on injury severity: older driver, crash speed, rear-end collision, number of vehicles involved, drowsy driving, nighttime (0:00 to 6:00) driving, overturn or rollover, and vehicle's fire after crash. However, injury severity was less severe in crashes under snowy condition and crashes to traffic facilities (i.e., crash alone).

• 한국도로학회:학술대회논문집
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• 2011.03a
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• pp.77-84
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• 2011

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• 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.