• Title/Summary/Keyword: 프로빗 모형

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Analysis of Intra-city Bus Demand during Rainfall Using Ordered Probit Model (순서형 프로빗 모형을 이용한 강우시 시내버스 이용수요의 변동분석)

  • Jeong, Heon-Yeong;Song, Geum-Yeong;Kim, Gwang-Uk
    • 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.

Undecided inference using bivariate probit models (이변량 프로빗모형을 이용한 미결정자 추론)

  • Hong, Chong-Sun;Jung, Mi-Yang
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.6
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    • pp.1017-1028
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    • 2011
  • When it is not easy to decide the credit scoring for some loan applicants, credit evaluation is postponded and reserve to ask a specialist for further evaluation of undecided applicants. This undecided inference is one of problems that happen to most statistical models including the biostatistics and sportal statistics as well as credit evaluation area. In this work, the undecided inference is regarded as a missing data mechanism under the assumption of MNAR, and use the bivariate probit model which is one of sample selection models. Two undecided inference methods are proposed: one is to make use of characteristic variables to represent the state for decided applicants, and the other is that more accurate and additional informations are collected and apply these new variables. With an illustrated example, misclassification error rates for undecided and overall applicants are obtainded and compared according to various characteristic variables, undecided intervals, and thresholds. It is found that misclassification error rates could be reduced when the undecided interval is increased and more accurate information is put to model, since more accurate situation of decided applications are reflected in the bivariate probit model.

Injury Severity Analysis of Truck-involved Crashes on Korean Freeway Systems using an Ordered Probit Model (순서형 프로빗 모형을 적용한 고속도로 화물차 사고 심각도)

  • Kang, Chanmo;Chung, Younshik;Chang, Yoo Jin
    • 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).

메타분석에서 그룹화 임의효과 모형의 베이지안 해석

  • 정윤식;정호진
    • The Korean Journal of Applied Statistics
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    • v.13 no.1
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    • pp.81-96
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    • 2000
  • 본 논문은 의학분야에서 주로 사용되는 메타분석 중 그룹화 임의효과 모형(grouped random effects model)을 프라빗 연결함수(probit link function)를 이용하여 베이즈적 관점에서 연구하였다. 이때 프라빗 함수를 강요하기 위해 잠재변수를 정의하였고, 사전 분포를 달리한 세가지 모형을 고려하였다. 주어진 세가지 모형들에게서 적합한 모형 선택을 위하여 베이즈 인자(Bayes factor, BF)와 유사베이즈 인자(pseudo-Bayes factor, PsBF)를 이용하였다. 깁스샘플러와 메트로폴리스 알고리즘을 이용하여 베이지안 계산상의 어려움을 해결하였다. 예로써, 새로운 간질약에 대한 효과를 조사하기 위하여 앞에서 제시된 방법으로 해석하였다.

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Subscription to the Internet Telephony : An Econometric Analysis of Stated-Preference Data (인터넷전화 가입의향분석: Stated-Preference 자료의 계량경제학적 분석)

  • Kim, Ho
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2005.05a
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    • pp.814-821
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    • 2005
  • 본 연구에서는 인터넷전화의 잠재 가입자를 대상으로 이들의 전화 사용 행태 및 사회-인구 통계적 특성 검토하여 이를 인터넷전화의 가입 확률로 연결하는 모형을 개발한다. 이때 가입 예측모형은 설문결과에 이산 프로빗 모형을 적용함으로써 구축하였다. 모형의 결과에 따르면 인터넷전화서비스 가입에 통계적으로 유의한 영향을 미치는 변수는 월 전화요금, 월 전화사용회수, 채택하고 있는 전화요금제, 현재 가입 중인 전화회사, 가구 의사결정자의 나이 등인 것으로 분석되었다. 또한 위의 각 요인의 부호는 관련 분야의 전문가들의 일반적인 견해 및 상식과 일치하는 것으로 나타났다.

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Determinants of High Risk Drinking in Korea (한국 사회의 고위험 음주 결정요인에 관한 연구: 중도 절단 이변량 프로빗 모형의 적용)

  • Chung Woojin
    • Korea journal of population studies
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    • v.26 no.2
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    • pp.91-110
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    • 2003
  • This study analyzed data from 1997 Korea's Behavioral Risk Factor Surveillance System Survey collected through telephone questionings based on the multi-stage stratified random sampling. We categorized respondents into those who had ever drunk an alcoholic beverage in the last month and those who didn't and, referring to the World Health Organization's guideline, the former group were further categorized into low risk drinking group and high risk drinking group. Employing bivariate probit regression analyses with censoring on independent variables such as preferred type of alcoholic beverage, the number of types of beverages consumed, age, marital status, education, occupation, residential area, current smoking, body mass index and stress suggested (1) that those who prefer soju are more likely to involve high risk drinking than those who and prefer the other alcoholic beverages (2) that those who are relatively older, who live without a partner, who have jobs, who. are vulnerable to stress, or who enjoy more than one type of beverage are more likely to be exposed to high risk drinking than the others.

An Exploratory Study on User Characteristics of Social Media: From the Perspective of Consumer Innovativeness (소셜미디어 이용자 특성에 대한 탐색적 연구: 소비자혁신성을 중심으로)

  • Shin, Hyunchul;Kim, Yongwon;Kim, Yongkyu
    • Journal of Digital Convergence
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    • v.18 no.10
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    • pp.195-206
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    • 2020
  • This study aims to analyze the effect of consumer characteristics such as consumer innovativeness on using popular social media in Korea. Social media usage is estimated by probit and multinomial probit model with user characteristics using Korea media panel data of 2019. According to the analysis, users with hedonoc innovativeness are likely to use social media, while users with cognitive innovativeness are not likely to use it. Regarding individual social media usage, functional innovativeness increases the probability of using Kakaostory, and hedonic innovativeness increases the likelihood of using Instagram. However, cognitive innovativeness decreases the probability of using Kakaosotry and Naver Band. This study gives insights into finding out specific social media for marketing certain products with innovativeness. In future research, it may be worthwhile to analyze under the assumption that a social media user is using several social media simultaneously.

Comparison of Methodologies for Characterizing Pedestrian-Vehicle Collisions (보행자-차량 충돌사고 특성분석 방법론 비교 연구)

  • Choi, Saerona;Jeong, Eunbi;Oh, Cheol
    • 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.

Development of the Bicycle Level of Service Model from the User's Perspective (이용자 중심의 자전거도로 서비스수준 모형 개발)

  • Gang, Gyeong-U;Lee, Gyeo-Ra
    • 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.