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Operation Organization Of Urban Rail Transit Based On The Passenger Flow Short-term Prediction

Posted on:2017-05-29Degree:MasterType:Thesis
Country:ChinaCandidate:Y ZhangFull Text:PDF
GTID:2272330503974656Subject:Traffic engineering
Abstract/Summary:PDF Full Text Request
With the development of social economy, our country’s city traffic congestion problem is increasingly serious, which can’t be solved by building new roads and broadening the path. As a new kind of large capacity, high efficiency, environmental protection type of transportation, Urban rail transit has a very important role to ease traffic congestion.In recent years, the development of urban rail transit in China is rapid, in 2015 China’s urban rail transit operation mileage increased by 334.68 km, making the total operating mileage of 3286.51 km.Attracts more and more urban rail transit passenger, the operation organization is also becoming difficult. The passenger volume forecasting is an important part of operation organization.As traditional traffic prediction with respect to the actual operation of a large error, therefore a high precision prediction model has great significance for the operating organizationUnder this background, the paper uses wavelet neural network prediction model, which bases on the unbalanced characteristics of the urban rail transit passenger volume, to forecast short-term section passenger volume, the test results show that prediction accuracy is higher and can be used as the basic data of urban rail transit operation. Based on the result of short-term section passenger volume forecast, develop a reasonable program of train formation thereby draw the train routing scheme and prioritization scheme is the emphasis of the paper. Meanwhile under conditions to ensure transport efficiency, improve service levels and reduce operating costs. Through the example analysis of xi ’an subway line 2, sums up the operation of passenger volume law of development, the existing operating organization mainly full-day driving program has been optimized,, the results show that the optimized scheme has improved transport efficiency and service level, and the quantity of using train fell by almost 19% meanwhile effectively save the operating costs.With UMTTDS simulation software testing finally the optimized scheme, proved its feasibility.
Keywords/Search Tags:urban rail transit, short-term passenger volume forecast, wavelet neural network, the operation optimization
PDF Full Text Request
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