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        검색결과 2

        1.
        2014.08 KCI 등재 구독 인증기관 무료, 개인회원 유료
        PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.
        4,000원
        2.
        2017.03 KCI 등재 서비스 종료(열람 제한)
        In this study, the intra-seasonal fluctuation (ISF) of wintertime temperature change in East Asia was classified by a cluster analysis of complete linkage. A ISF of temperature change was defined as a difference of synthesized harmonics (1 to 36 harmonic) of daily temperature averaged for 30 years (1951~1980, 1981~2010). Eight clusters were gained from the ISF curves of 96 stations in East Asia. Regions of the cluster C, G and A1 seem to be affected by the Siberian High (SH) center, whereas the cluster A1, A2, D, B and F by the SH main pathways. Regions of the cluster E are apart from the SH main pathways and appear to be in the area of influence of other factors. Wintertime temperatures in Northwest China (clusters C, G) and Northeast China (cluster A1) were increased very largely. In most clusters, around late January there were less warming periods than the winter mean of the mean ISF of the clusters, before and after this time there were more warming periods than the winter mean.