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The Uncertainty Analysis Of Rail Transit Investment

Posted on:2011-06-25Degree:MasterType:Thesis
Country:ChinaCandidate:G LiFull Text:PDF
GTID:2189360305960025Subject:Road and Railway Engineering
Abstract/Summary:PDF Full Text Request
With the rapid development of economic construction, China entered the urbanization and motorized stage of accelerated development, urban rail transit with its large capacity, high efficiency, low pollution and other advantages, many cities quickly become the first choice to solve traffic problems and based diversified development in China to form a subway, rapid rail, elevated light rail, monorail, etc.However, rail transit is a large-scale infrastructure projects, and its large amount of trade and investment specificity, making foreign and private capital into the rail transit industry, we must be careful to treat it, careful analysis of the project benefits and risks, which make the best decisions.Uncertainty analysis of investment projects improved the financial evaluation, investment decisions not only help to improve reliability, but also to understand the risks borne by the project, easy to put forward various response measures, the risk of investment projects to improve prevention capacity. Monte Carlo simulation to the uncertainty of investment projects at the same time all the random, through extensive simulation studies project economic evaluation of its impact.The paper on rail transit investment in the uncertain factors are analyzed, using the probability analysis method-Monte Carlo, Ningbo City, Metro Line 1 of a project as an example find the benchmark rate of return, Social Discount Rate, traffic, inflation rate of evaluation of the probability distribution, expected value, variance and standard deviation, also calculated after-tax net present value, internal rate of return and payback period of dynamic economic indicators such as the probability distribution for the rail transit investment decisions provide the basis.
Keywords/Search Tags:Rail transportation, Investment decisions, Uncertainty analysis, Monte Carlo simulation
PDF Full Text Request
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