메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
팽정광 (서울산업대학교 철도경영정책학과) 김시곤 (서울산업대학교 철도경영정책학과) 박민규 (서울산업대학교 철도경영정책학과) 강승필 (서울대학교 건설환경공학부)
저널정보
대한안전경영과학회 대한안전경영과학회지 대한안전경영과학회지 제11권 제4호
발행연도
2009.1
수록면
201 - 211 (11page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
The types and quantities of Hazmat and Hazmat transportation are gradually increasing, keeping pace with industrialization and urbanization. At present the safety management for Hazmat transportation only considers reducing accident probability, but even when an accident involving Hazmat-carrying vehicles occurs, that is not regarded as a Hazmat-related accident if the Hazmats do not leak out from the containers carrying them. Thus the methods to reduce risk (Risk=Probability$\times$Consequence) have to be developed by incorporating accident probability and consequence. By using Geographic Information System (GIS), a technical method is invented and is automatically able to evaluate the consequence by different types of Hazmat. Thus this study analyzed the degree of risk on the links classified by the Hazmat transport pathways. In order to mitigate the degree of risk, a method of 7-step risk management on Hazmat transportation in railway industries can be suggested. (1st step: building up GIS DB, 2nd step: calculating accident probability on each link, 3rd step: calculating consequence by Hazmat types, 4th step: determination of risk, 5th step: analysis of alternative plans for mitigating the risk, 6th: measure of effectiveness against each alternative, and 7th step: action plans to be weak probability and consequence by the range recommended from ALARP). In conclusion, those 7 steps are used as a standardization method of optimum transportation routing. And to increase the efficiency of optimum transportation routing, optional route can be revise by verification.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0