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Research On Planning Methods For Multi-Hierarchy Transit Network

Posted on:2009-06-20Degree:MasterType:Thesis
Country:ChinaCandidate:Y H ZhuFull Text:PDF
GTID:2132360242494232Subject:Transportation planning and management
Abstract/Summary:PDF Full Text Request
Reasonable and effective transit network is the basic of the preferred public transportation-oriented development. However, there is lack of a well-being network with clear responsibilities in most of cities in China presently, which results in low-speed transit operation. Therefore, a reasonable and high-quality transit network should be achieved by including multi-hierarchy lines and different standards.The definition and advantages of multi-hierarchy transit are introduced, compared with traditional single-mode transit. According to basic principles for network distribution, this paper obtains optimization objects and routes-searching methods with consideration of respective characteristics of multi-hierarchy transit lines.Meanwhile, a network planning method is proposed with the idea of"researching respectively and optimizing as a whole". Then this paper focuses on a case study on comprehensive transit lines distribution.Estimations and adjustment are necessary for initial planning scenario. With respect to the change of transit accessibility and mode choice due to transit network's adjustment, this paper considers transit accessibility as an input factor, and addresses modal split and OD matrix based on BP neural network. With the help of Cube, volumes in each line are obtained. Then a serial of estimations are carried out so as to adjust unreasonable lines. Consequently, this paper realizes the circulation among modal split, volume assignment, estimation and adjustment.Taking Fangshan district in Beijing as an example, this paper proposes a multi-hierarchy transit planning scenario, based on current transit situation. The estimation result shows the planning scenario is reasonable and practicable.
Keywords/Search Tags:transit planning, network adjustment, multi-hierarchy transit, BP neural network, transit assignment
PDF Full Text Request
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