【摘要】：隨著我國高速鐵路技術(shù)的飛速發(fā)展，安全這一主題在高速鐵路運(yùn)營管理中越來越突出。2012年起，我國鐵路全面引入基于風(fēng)險的安全管理機(jī)制，對適用于我國高速鐵路運(yùn)營現(xiàn)狀的風(fēng)險分析方法的研究也成為熱門課題。 目前，鐵路運(yùn)輸從人-機(jī)-環(huán)境-管理等方面采用了先進(jìn)可靠的新技術(shù)確保運(yùn)營安全，尤其是高速鐵路集成了諸多領(lǐng)域的現(xiàn)代高新技術(shù)，因此事故發(fā)生概率小，但由于列車速度的大大提高，一旦發(fā)生就會造成極大損失。這種特性即為低頻率—高損失特性，，體現(xiàn)在數(shù)據(jù)上就是損失數(shù)據(jù)不服從正態(tài)分布，而是具有明顯的厚尾特征。 本論文引進(jìn)極值理論中的POT模型，能夠針對鐵路重大以上事故（尤其是高速鐵路事故）這種低頻率—高損失的特性進(jìn)行風(fēng)險估計與分析，在選定一個合理的較大閾值μ的情況下，針對數(shù)據(jù)樣本中超過閾值的數(shù)據(jù)，擬合得出損失分布函數(shù)，并在給定的置信水平之下計算得出風(fēng)險價值VaR P，從而對事故損失做出估計。 本論文在調(diào)研分析風(fēng)險管理理論在國內(nèi)外的發(fā)展現(xiàn)狀，以及風(fēng)險管理基本概念、模型與方法的基礎(chǔ)上，研究了風(fēng)險管理技術(shù)應(yīng)用于中國高速鐵路運(yùn)營安全的基本方法，給出適用于我國高速鐵路運(yùn)營安全的風(fēng)險分析的一般流程和模型。并結(jié)合研究課題“中國高速鐵路運(yùn)營安全風(fēng)險分析和控制”，對目前高速鐵路運(yùn)營安全風(fēng)險進(jìn)行了簡要分析。 作為重點(diǎn)研究內(nèi)容，本論文在研究風(fēng)險分析數(shù)學(xué)理論的基礎(chǔ)上，首次提出了高速鐵路事故低頻率—高損失特性的理念，并提出了高速鐵路基于低頻率—高損失特性的高速鐵路運(yùn)營安全風(fēng)險分析模型，即POT-GPD方法模型。 基于POT-GPD模型，以運(yùn)營數(shù)據(jù)為基礎(chǔ)，分別對某運(yùn)營單位客車脫軌事故造成的經(jīng)濟(jì)損失和人員死亡的歷史數(shù)據(jù)、某運(yùn)營單位貨車脫軌事故造成的經(jīng)濟(jì)損失數(shù)據(jù)進(jìn)行數(shù)值模擬，分別選取不同的閾值，計算得到3個數(shù)據(jù)樣本各自的損失分布函數(shù)，并分別給定置信水平P=95%和P=99%，計算得到風(fēng)險價值VaR P值。 最后，本論文通過計算得到的風(fēng)險價值，結(jié)合對樣本數(shù)據(jù)的分析，對閾值μ的選取的合理性進(jìn)行檢驗。針對不同的閾值μ，通過樣本數(shù)據(jù)分別進(jìn)行擬合，分析閾值選取的合理性，并對閾值μ的選取做出建議。
[Abstract]:With the rapid development of high-speed railway technology in China, the theme of safety is becoming more and more prominent in high-speed railway operation and management. Since 2012, China Railway has fully introduced risk-based safety management mechanism. The research on the risk analysis method which is suitable for the operation of high-speed railway in China has also become a hot topic. At present, railway transportation adopts advanced and reliable new technology in man-machine-environment management to ensure operation safety, especially high-speed railway integrates modern high and new technology in many fields, so the probability of accidents is small. However, due to the great increase of train speed, once it happens, it will cause great losses. This property is characterized by low frequency and high loss, which is reflected in the fact that the loss data is not obedient to normal distribution, but has obvious characteristics of thick tail. In this paper, the POT model of extreme value theory is introduced, which can estimate and analyze the risk of the low frequency-high loss characteristic of the major railway accidents (especially high-speed railway accidents). In the case of a reasonable larger threshold 渭, the loss distribution function is obtained by fitting the data exceeding the threshold value in the data sample, and the risk value VaR P is calculated under a given confidence level, and the accident loss is estimated. Based on the investigation and analysis of the development of risk management theory at home and abroad, as well as the basic concepts, models and methods of risk management, this paper studies the basic methods of applying risk management technology to the operation safety of high-speed railway in China. The general process and model of risk analysis for high-speed railway operation safety in China are presented. Combined with the research topic "risk analysis and control of high speed railway operation safety in China", this paper briefly analyzes the operational safety risk of high speed railway at present. On the basis of studying the mathematical theory of risk analysis, the concept of low frequency-high loss characteristics of high-speed railway accidents is put forward for the first time. Based on the characteristics of low frequency and high loss, a risk analysis model of high speed railway operation safety, called POT-GPD method, is proposed. Based on POT-GPD model and operation data, the economic loss and death caused by train derailment in a certain operating unit and the economic loss data caused by derailment of freight car in a certain operating unit are simulated numerically respectively. The loss distribution functions of the three data samples are calculated by selecting different thresholds, and the VaR P value of the risk value is calculated by giving the confidence level PQ 95% and PU 99% respectively. Finally, this paper tests the rationality of the selection of threshold 渭 by calculating the risk value and analyzing the sample data. According to different threshold 渭, the rationality of threshold selection is analyzed by fitting the sample data, and suggestions are made on the selection of threshold 渭.
【學(xué)位授予單位】：中國鐵道科學(xué)研究院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：U298

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