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실시간 철도안전 의사결정 지원시스템의 사고사례 적용

Train accident Applies to Real-time railway safety decision support system

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초록

As one-day commuting is becoming possible across the country thanks to the development of modes of transportation, convenience has improved. Due to automation and increasing scale, however, a possibility of a disastrous traffic accident arising from a small defect cannot be ruled out. In this respect, all the social systems are in constant operation to ensure a safe and convenient life of people. Nevertheless, the risk of accident exists anywhere and anytime, so a safer and more efficient system needs to be introduced. In addition, there are many factors in an accident depending on the characteristics of the accident site, so a broader examination is needed with a multi-faceted perspective. The real-time railway safety decision-making support system is a system that detects various factors causing an accident in advance and helps the user to make a decision more efficiently and operate more safely. By applying accident cases to the system, this study intends to verify that a similar railway accident could be prevented.

Abstract

As one-day commuting is becoming possible across the country thanks to the development of modes of transportation, convenience has improved. Due to automation and increasing scale, however, a possibility of a disastrous traffic accident arising from a small defect cannot be ruled out. In this respect, all the social systems are in constant operation to ensure a safe and convenient life of people. Nevertheless, the risk of accident exists anywhere and anytime, so a safer and more efficient system needs to be introduced. In addition, there are many factors in an accident depending on the characteristics of the accident site, so a broader examination is needed with a multi-faceted perspective. The real-time railway safety decision-making support system is a system that detects various factors causing an accident in advance and helps the user to make a decision more efficiently and operate more safely. By applying accident cases to the system, this study intends to verify that a similar railway accident could be prevented.

참고문헌 (6)

  1. Eurostat Statistics Explained, "Railway safety statistics", ~ (2016)
  2. 한국철도공사, "제52회 2014 철도통계연보", 1066~1069 (2015)
  3. Paola PELLEGRINI, "Real time railway traffic management modeling track-circuits", HAL archives-ouvertes, 1~12 (2013)
  4. 송현진, "철도 빅데이터 기반의 선로이상 위험예측", 대한설비학회 특별세션, ~ (2016)
  5. Byeong Ho Cho, "A BDD Methodology for the Calculation of Large Fault Trees", ART2016 poster section, ~ (2016)
  6. , 2014

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