본 연구는 교통사고 심각도와 관련된 중요변수를 찾고 이들 변수를 바탕으로 신경망, Decision Tree, 로지스틱 회귀분석을 이용하여 사고 심각도 분류 예측모형을 추정하였다. 다수의 범주형 변수로 이루어진 교통사고 통계원표상의 설명변수 들로부터 사고 심각도 변화에 영향력 있는 변수 선택을 위하여 독립성 검정을 위한 x² test 와 Decision Tree를 이용하였고, 선택된 변수들은 신경망과 로지스틱 회귀분석의 기초로 이용되었다. 분석결과 세가지기법간에 분류정확도에는 유의한 차이가 없는 것으로 나타났다. 그러나 Decision Tree가 설명변수 선택능력과 분석수행시간, 사고 심각도 결정요인 식별의 용이함 측면에서 범주형 종속변수인 사고 심각도의 분석에 적합한 것으로 보이며 사고 심각도에는 보호장구가 가장 큰 영향을 미치는 것으로 재입증되었다.
Purpose: Certain places in Seoul such as Shinchon, Hongdae, and Gangnam, often suffer from sudden overflow of mobile population which can cause serious safety problems. This study suggests the application of spatial CUSUM control chart in monitoring areal population mobility data which is recently provided by Seoul metropolitan government.
Methods: Monitoring series of standardized local Moran’s I enables one to detect spatio-temporal out-of-control status based on the accumulation of past patterns. Moreover, we visualize such pattern map for more intuitive comprehension of the phenomenon. As a case study, we have analyzed the female mobility population aged 25 to 29 appeared in 51 Jipgyegu near Hongik university on fridays from January, 2017 to June, 2018. They are validated by exploring related articles and through local due diligence.
Results: The results of the analysis provide insights in figuring out if the change of the mobility population is short-term by particular incident or long-term by spatial alteration, which allows strategic approach in constructing response system. Specific case near popular downtown near Hongik University has shown that newly opened hotels, shops of global sports brand and franchise bookstores have attracted young female population.
Conclusion: We expect that the results of our study contribute to planning effective distribution of administrative resources to prepare against drastic increase in floating population. Furthermore, it can be useful in commercial area analysis and age/gender specific marketing strategy for companies.
주식시장에서 KOSPI200지수의 상승 또는 하락으로 분류 및 예측하는 정보는 선물 및 옵션시장에서 포토폴리오를 설계할때 의사결정을 위해 중요한 기준이 된다. 경제지표인 시계열 패턴들의 향후 추세는 가장 최근의 경제패턴에 매우 종속적이기 때문에 최근의 패턴들을 가장 우선적으로 학습해야 할 필요가 있다. 본 논문에서는 시계열분석, 신경회로망, 그리고 다양한 분야에서 각광을 받고 있는 SVM(Support Vector Machine)과 Fuzzy SVM 모형의 분류 및 예측성능을 비교하였다. 특히 학습 DB에 따라 시계열성 속성을 갖는 퍼지소속함수에 가장 적합한 차원을 제시함으로서 Fuzzy SVM이 우수함을 입증하였다.
In many cases, the result of a road traffic accident can be described with more than one response variable. Nonetheless, most of the existing road accident data analysis deal with only one response variable and try to explain why it occurs. In this paper, we train association rules for a set of three response variables (severity levels of accidents, body area of att ack, and injury mode) conditional on personal, environmental and behavioral
aspects of accident. The results derived at 5% support and 50% confidence from the 1996 data of three police stations in Korea
are as follows : (1) Fatal accident (death) of fractal head's bone often occurs when a highschool graduate in his thirties has a car
alone accident in a densely populated city in a clean night while driving a non-commercial passenger car in normal condition on the
straight pavement of no signal. (2) Major injury accident with a head shock likely occurs when a man with protection device is
involved in car-to-car accident in a city while driving a non-commercial car on the straight road. (3) Minor injury with neck
dislocation sprain oft en occurs when a highschool graduate in his thirties, commercial passenger car is involved in a car-to-car
accident on the dry straight road with 13-15m width without a traffic signal in a clean night. We expect that these rules can
contribute to effective safety practice in Korea.
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