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항만 도시 교통물류 안전 증진을 위한 실시간 기상 변화 및 항만 영향권 특성 별 사고 영향요인 분석에 관한 연구
박누리,박준영 한국물류과학기술학회 2024 물류과학기술연구 Vol.5 No.1
항만과 항만 인근 항만의 영향을 받는 도로는 대형 사고를 초래할 수 있는 화물차의 이동이 많기 때문에 교통안전에 각별한 주의가 필요하다. 따라서 사고 심각도를 낮출 수 있는 안전 관리 대책을 마련하기 위해 항만 도시의 도로 구간을 항만의 영향을 받는 정도에 따라 구분하고, 각 항만 영향권에서 사고 심각도에 영향을 미치는 요인을 도출해 안전 관리 전략을 수립할 필요가 있다. 본 연구에서는 항만 영향권에서 사고 심각도에 영향을 미치는 요인을 네 가지 머신러닝 기법을 통해 도출하고자 하였다. 모형 개발 후에는 가장 예측성능이 뛰어난 모형에 대하여 설명가능한 인공지능 기법을 통해 높은 사고 심각도에 영향을 미치는 요인을 도출하였다. 본 연구에서 도출된 결과를 활용하여 항만 지역의 사고 심각도 감소를 위한 정책 수립의 기초자료로 활용될 수 있을 것으로 기대된다. Port safety management should consider a variety of cargo shifting within trucks and containers, occurring at and near port areas. In particular, it is crucial for port safety management to consider not only incidents directly 'at-port' but also those in the surrounding 'near-port' areas, including the port influence area. This is significant because of the potential for high crash severity at near port areas, given the substantial truck traffic that could lead to large-scale crashes. Therefore, developing management strategies for port city safety requires identifying key risk factors that influence crash severity in each port area. During this process, because the key factors influencing crash severity may vary as one gets closer to the port center, it is essential to take into account the size of the port influence area. This study collected and matched both crash and weather data to consider various variables. Additionally, this study developed four machine learning-based crash severity models, including Naive Bayes Classification, Support Vector Machine, Extreme Gradient Boosting, and Light Gradient-Boosting Machine. Furthermore, the identification of key factors influencing high crash severity is determined through the application of an eXplainable Artificial Intelligence technique. It is expected that findings derived from this study can contribute to policy-making efforts aimed at enhancing traffic safety in the port area.
박누리,손정락 한국건강심리학회 2012 한국심리학회지 건강 Vol.17 No.3
Purpose of this study is to demonstrate severity of Binge Eating Disorder (BED) is different from that of general obesity and to examine the effects of Cognitive Behavioral Therapy (CBT) on BED-prone college students. To do this, the following scales were used: Binge Eating Scale, Rosenberg Self-Esteem Scale, Social Discomfort Scale, Barratt Impulsiveness Scale and Dutch Eating Behavior Questionnaire. Study I investigated pathological eating behaviors as well as psychological distress in three different groups: two obese groups with and without BED and a healthy control group. Ten participants for each group were selected: two obese groups among 50 obese women from a weight clinic in South Korea and the healthy control group with normal weight among a college population in South Korea. The findings showed that BED patients had more pathological eating behaviors and psychological distress than the other two groups. Most BED patients want weight-loss treatment when they seek help. However, the study suggests that treatment for BED should first be directed at the disordered eating and associated psychopathology rather than the obesity itself, even though BED patients are found in obese population. In Study II, 24 BED-prone college students among 600 college students were randomly assigned to CBT(eight weekly sessions during active treatment) or to no-treatment control group. At the end of the active treatment, binge eating was significantly reduced among those actively treated relative to those on no-treatment control group. Furthermore, CBT produced significant or at least marginally significant improvements in all psychological variables (self-esteem, impulsiveness, and social discomfort) relative to baseline, and they even improved more at the 6-week follow-up. The results support the efficacy of CBT as a preventive intervention for BED-prone college students. In spite of its several limitations, the present study clarifies the distinctiveness of BED from obesity in psychological factors along with pathological eating problems and recommends CBT as an effective treatment for BED-prone individuals.