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Urban Expressway Networks Scale Calculation Methods

Posted on:2014-10-28Degree:MasterType:Thesis
Country:ChinaCandidate:D PuFull Text:PDF
GTID:2262330401973170Subject:Traffic and Transportation Engineering
Abstract/Summary:PDF Full Text Request
With the enlargement of city scale, the urbanization advancement speeding up unceasingly and motor vehicle ownership increased rapidly. Now fast road network scale has been completed cannot meet the rapid growth of the urban transportation demand basically, and the "urban disease of traffic congestion" is spreading rapidly to the small and medium-sized cities. The urban road network planning and construction beginning to get seriously in more and more cities, moreover the construction of urban expressway can improve the urban environment and can quickly ease city traffic. Reasonable road network scale of urban expressway has the close relationship with the expressway construction investment, social benefit, and urban planning road network service level and so on. Therefore, how to determine the scale of urban rapid road network has become a key in the expressway planning and construction.First of all, this paper finds out fast road network scale, For example:by2020, Shanghai fast road network density was0.60, Wuhan0.65, Hangzhou was0.86. By comparing with the urban road traffic planning and design specification (GB50220-95) found currently planning road network density fast in many big cities ware more than the upper limit of specification, based on this, this paper put forwards the demand for fast road network scale computing research.Second, urban road network scale factors were studied in this paper, based on the predecessors of the existing research, increasing population density, land properties and ramp spacing effect factors, adopting the method of investigation and analysis put forward main influence factors of expressway network scale. Lastly, selecting high correlation factor as independent variables for regression analysis.In the process of research on calculation method for expressway network scale requirements, existing algorithms have been analyzed. Especially, focusing on more deeply analyzing and researching to the algorithm of supply and demand balance, the traffic supply model based on road network capacity and space-time consumption, according to the motor vehicle ownership and motor vehicle OD survey of rapid road traffic demand model, and the principle of supply equal to demand, four single foreca-sting model have been combined.In order to synthetically use the effective information of single forecasting model, and scatter the forecasting risky of the single model, the paper put forwards the thinking of combined forecast, through comparison study finding that combination forecast model have high precision than the single forecasting model, and the error is smaller.Taking a city as an example to demonstration analysis. Forecasting and analyzing motor vehicle ownership of the city, according to the proportion of total city traffic demand that the fast network can share in the end of planning year, and determining the fast network traffic demand. Using the combination forecast model to predict and analyze the city’s expressway networks the demand scale. Finally, expressway traffic demand was distributed to city road network by VISUM software. It is in line with the actual demand that combination forecast model is adopted to calculate the city expressway network scale demand. This paper also study a instance as Shanghai, Shanghai’s planning expressway network scale is400km, this article adopts the combination forecast model to calculate Shanghai expressway network scale was408.9km in the end of planning year, the both length existed little difference.Finally, this paper is demonstrated that the combination forecast model is better than single prediction model by examples. Meanwhile, this paper also sums up the different expressway length of urban scale.
Keywords/Search Tags:City expressway, Expressway network scale, Composite model, Influencing factor, Computing method
PDF Full Text Request
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