• Title/Summary/Keyword: 이분산성의 영과잉 포아송 모형

Search Result 2, Processing Time 0.018 seconds

Hurdle Model for Longitudinal Zero-Inflated Count Data Analysis (영과잉 경시적 가산자료 분석을 위한 허들모형)

  • Jin, Iktae;Lee, Keunbaik
    • The Korean Journal of Applied Statistics
    • /
    • v.27 no.6
    • /
    • pp.923-932
    • /
    • 2014
  • The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

Safety Impacts of Red Light Enforcement on Signalized Intersections (교차로 신호위반 단속카메라 설치가 차량사고에 미치는 영향)

  • Lee, Sang Hyuk;Lee, Yong Doo;Do, Myung Sik
    • Journal of Korean Society of Transportation
    • /
    • v.30 no.6
    • /
    • pp.93-102
    • /
    • 2012
  • The frequency and severity of traffic accidents related to signalized intersections in urban areas have been more serious than those in both arterial segments and crosswalks. Especially, traffic accidents involved with injuries and fatalities have caused by traffic signal violations within intersections. Therefore, many countries including Korea have installed the red light enforcement camera (RLE) to reduce traffic accidents associated with the traffic signal violation. Meanwhile, many methodologies have been studied in terms of safety impacts estimation of red light enforcement, which, however, cannot be easy to conduct. In this study, safety impacts was estimated for intersections of Chicago downtown area using SPF models and EB approach. As a result, for all crash types and target traffic accident types such as "angle", "rear end", "sideswipe in the same and other directions", "turn", and "head on", fatal crashes were reduced by 26% and 38%. However, RLE may increase property-demage-only-crashes by 3.23% and 1.16%, respectively.