Risk Assessment Approaches With Uncertain Decision Preferences And The Application In Urban Rail Transit

With the acceleration of urbanization in China,this process promotes the development maturity of urban rail transit network.Accordingly,this change leads to a more complex urban rail transit system,in which the system components are function connected.It also increases the probability of potential risk and/or accident in the urban rail transit system operation process.Therefore,in order to enrich the current risk analysis approaches for urban rail transit operation,it is beneficial to develop the risk identification and assessment methods under uncertain environment,and explore the application of these methods for risk analysis in urban rail transit operation.In such case,this thesis first proposes some risk identification and assessment methods under uncertain environment by considering the limitations of extant risk identification and assessment theories for urban rail transit operation.And then,these methods are utilized to address the risk analysis problem in urban rail transit operation system.The specific research contents of this thesis are as follows:(1)In order to solve the risk identification problem under uncertain environment,a risk identification method is established based on Acci Map model and fuzzy interpretive structure model.First,Acci Map model is used to identify the main risk factors of system the internal logical relationship among these factors.Then,the triangular fuzzy number and interpretative structure model are introduced to measure the correlation between each pair of risk factors.Next,the MICMAC(Matrix of Cross Impact Multiplications Applied to Classification)method is utilized to identify the key risk factors.Finally,an illustrative example is conducted to demonstrate the validity and rationality of the proposed model.(2)To the risk analysis problem with interactive risk factors under uncertain environment,the Fine-Kinney based risk assessment approach is proposed by integrating the Choquet integral and MULTIMOORA(Multiple Multi-objective Optimization by Ration Analysis)method.In this approach,the triangular fuzzy numbers are used to represent uncertain risk assessment information.Then,an extended Choquet integral based risk information aggregation method with preference degree of fuzzy number is put forward,in which the properties of proposed information aggregation method are verified.Next,an improved MULTIMOORA method based on Choquet integral is constructed to determine risk priority ranking order.Finally,the proposed risk priority ranking method is incorporated into the Fine-Kinney approach to address uncertain risk analysis problem with interactive risk factors.After that,sensitivity analysis and comparative analysis are led to verify the applicability and rationality of the proposed method.(3)In order to cope with risk assessment problem with random and fuzzy preferences,a developed FMEA model based risk assessment approach is proposed,in which the cloud model and GLDS(Gained and Lost Dominance Score)model are incorporated.First,the normal cloud model is applied to depict the random and fuzzy uncertain risk preferences of decision makers.Then,a weighted averaging operator with Shapley index and Choquet integral is proposed to aggregate the risk information from experts considering the interactions between experts' preference.Next,a risk priority calculation method based on extended GLDS model is constructed.After that,the proposed risk priority calculation method is combined with FMEA model to address risk analysis problem with random and fuzzy risk preferences information.Finally,an illustrative example is presented to demonstrate the applicability and reliability of the proposed method.(4)The risk assessment approaches based on prospect theory and generalized TODIM method are proposed for the purpose of dealing with risk analysis problem considering the influence of decision makers' risk preferences.First,two different risk priority calculation models are proposed by using prospect theory and generalized TODIM method,in which the reference dependence and loss aversion effects are taken into account,respectively.Then,the proposed risk priority calculation models are introduced to FMEA for addressing risk assessment problem considering decision makers' risk preferences under uncertain environment.Finally,the illustrative examples are selected to illustrate the proposed method,and the comparative analysis is performed to test its rationality and effectiveness.(5)In order to test the applicability and validity of proposed risk identification and assessment approaches,the application of these approaches in operational risk analysis of urban rail transit is conducted,in which the Nanjing metro operation system is selected as an example.First,the developed Acci Map model is utilized to identify the key risk factors based on the risk analysis of Nanjing metro operation system.Then,the cloud model and extended generalized TODIM method based FMEA model are applied to determine risk priorities of key risk factors in Nanjing metro operation system.Finally,according to the risk priority ranking orders of key risk factors,some risk management and control measures are proposed.In this thesis,the operation risk analysis in urban rail transit is selected to test the validity of the proposed risk identification and assessment methods for uncertain system.Theoretically,the proposed uncertain risk assessment methods not only can enrich the existing risk analysis theory and method,but also can improve the performance of the Fine-Kinney and FMEA based risk assessment tools in literature.In practical applications,the proposed risk identification and assessment methods for uncertain system provide a new means and tool for solving the risk analysis problem in urban rail transit operation.