| On December 28,2019,the world’s first smart high-speed rail,the BeijingZhangjiakou smart high-speed rail,was put into operation,opening a precedent for smart high-speed rail.As a new member of the high-speed rail family,smart high-speed rail has many operating requirements,high technical standards,and complex operating conditions,resulting in its operating costs much higher than the existing high-speed rail.Therefore,the study of its operating costs,especially the study of operating costs in the feasibility stage of the project,is of great significance to line selection and preventive operation cost control,and is conducive to promoting the sound development of smart high-speed rail,and better play the vanguard role of smart highspeed rail in leading smart transport.The paper takes the operating cost of railway projects such as smart high-speed rail as the research object,and uses the full life cycle operating time of smart highspeed rail as the time axis to study the operating cost in the feasibility phase of the project.Through searching,analyzing,and collecting the influencing factors of smart high-speed rail operating costs,establish an indicator system of influencing factors of smart high-speed rail operating costs.Use the completed operation of intercity,highspeed rail and other railway engineering information to establish a basic information database of smart high-speed rail operating costs.In view of the fact that in the process of sorting and collecting information,the selection of indicators for the operating costs of smart high-speed rail is inevitably redundant and related.The paper uses SPSS software to verify the validity and reliability of the influencing factor indicators,verify the reliability and validity of the selected indicators,improve the accuracy of the influencing factor indicators,and reduce the errors caused by the influencing factor indicators.And use rough set attribute reduction theory to reduce the influencing factors of smart high-speed rail operating costs,and obtain the key indicators that affect the operating cost of smart high-speed rail,eliminate indicators that have little impact,and achieve the purpose of predicting operating costs with fewer key indicators,which is conducive to reducing forecasts operation cost workload,improve work efficiency.When predicting the operating costs of smart high-speed rail,the paper does not have enough information to obtain in terms of influencing factor indicators and case selection given that the development of smart high-speed rail has just started.Accordingly,the existing high-speed rail data is used as the basis for the estimation of the operating cost of the smart high-speed rail.In order to reduce the deviation caused by the existing high-speed rail indicators and data on the smart highspeed rail operating cost estimation,and improve the accuracy of the estimation.In terms of the estimation time division,the smart high-speed rail operating cost estimation is divided into two stages:20 years before the operation time and 80 years after the operation time,the estimation error is reduced by the method of near precision and far roughness;in the selection of estimation method,two algorithms of BP neural network and fuzzy inference are selected.Use the advantages of these two algorithms in data processing to reduce the bias caused by the estimation.One is that in the first 20 years of operation,based on the current situation of my country’s high-speed rail development in the past two decades,sufficient case data can be obtained,similar cases can be screened using cosine similarity to meet the estimation requirements of BP neural network,and the BP neural network estimation model of smart high-speed rail operating costs can be established.The second is that 80 years after operation,it is impossible to obtain the case data and influencing factor indicators that meet the conditions.Use the RS reduction indicators and expert experience to infer the influencing factors of the smart high-speed rail operating cost,and use fuzzy inference to estimate the operating cost of the smart high-speed rail.A rough set attribute index reduction(RS)-fuzzy inference system(FIS)estimation model of smart high-speed rail operating costs is established.And establish an smart high-speed rail operating cost query system,and achieve design goals through computer software.Finally,an empirical analysis of the estimation model is carried out with the JX smart high-speed rail. |
