由於軌道運輸車統屬大系統層級，透過資訊收集、處理與共享，使各單元整合成整體系統，各系統中各個構成單元與構成事件組成關係結構，此即安全結構形式，而系統整體安全評估，需藉由安全結構關係、與組成單元的安全性加以分
析。時間必是影響軌道運輸安全的重要因素，許多安全事件均具有依時行為(Time-Behavior) 特性，因此傳統可靠性結構或事故分析屬靜態分析，只能反映某一時間點之系統安全狀態。馬可夫模式因具有時間與狀態變化兩因子，不但能充分反映安全事件的依時行為或序列之相依狀況，又能在任何時間點與狀態之下，推斷下一步的狀態與機率。因此馬可夫模式可說是少數優良的動態安全分析方法之一。本文將應用馬可夫模式並結合故障樹分析法的優點，以台織平交道安全監測系統為例，進行實證分析。結果顯示本文所提的馬可夫組合模式確實優於現有單一安全分析方法，應可作為國內軌道運輸營運機構安全分析之參考。 The time-dependent behavior of Markov model becomes more and more promising Generally, devices of a system may break down gradually due to complex interactions among components. A combinatorial approach by using conventional Fault Tree Analysis with Markov model may provide beneficial results in the research field of railway safety. The results of the research shown as follows: Firstly, the safety of Intelligent Level Crossing Safety Surveillance System is better than existing level crossings. Secondly, the difference of accident rate before 2.35 thousands hours between Intelligent Level Crossing Safety Surveillance System and existing level crossings is very significant. Thirdly, the Intelligent Level Crossing Safety Surveillance System will be more durable than existing level crossings. Finally, the failure of communication devices will obviously influence safety of whole system.
關聯:
中華民國運輸學會第二十屆學術論文研討會論文集 (第五冊)=Proceeding of the 20th annual conference for the Chinese institute of transportation v.5，頁1503-1528
