【摘要】：隨著我國(guó)城市化進(jìn)程的加快,城市人口急劇增長(zhǎng),交通壓力不斷增大,城市道路擁擠不堪,給市民的正常出行帶來(lái)了極大不便,城市交通問(wèn)題日益突出。要解決這一難題,不能把希望僅儀寄托在公路建設(shè)上,發(fā)展城市軌道交通才是應(yīng)對(duì)城市交通擁堵的好方法。目前我國(guó)的城市軌道交通建設(shè)正處在一個(gè)前所未有的蓬勃發(fā)展時(shí)期,快速發(fā)展的同時(shí)也帶來(lái)了很多問(wèn)題,主要有城市軌道交通客流預(yù)測(cè)不準(zhǔn)確以及城市軌道交通列車(chē)編組形式不合適等。由于以往常規(guī)的客流預(yù)測(cè)不準(zhǔn)確使得以其為基礎(chǔ)的運(yùn)營(yíng)編組設(shè)計(jì)不合適,從而導(dǎo)致了現(xiàn)階段城市軌道交通擁擠不堪或者運(yùn)能浪費(fèi),進(jìn)而引起城市軌道交通運(yùn)營(yíng)成本的增加。 針對(duì)此問(wèn)題本文在已有研究的基礎(chǔ)上,總結(jié)出城市軌道交通客流具有時(shí)變性、均衡性以及周期性變化等特點(diǎn),構(gòu)造了基于灰色預(yù)測(cè)模型和神經(jīng)網(wǎng)絡(luò)模型的城市軌道交通客流短時(shí)預(yù)測(cè)組合模型。利用神經(jīng)網(wǎng)絡(luò)模型來(lái)修正灰色預(yù)測(cè)模型的殘差,兩種模型互補(bǔ)對(duì)于城市軌道交通斷面客流短時(shí)預(yù)測(cè)具有一定的合理性和參考性,可以作為城市軌道交通運(yùn)營(yíng)編組設(shè)計(jì)優(yōu)化的基礎(chǔ)。 參考智能交通信號(hào)燈的原理,根據(jù)城市軌道交通短時(shí)預(yù)測(cè)的實(shí)時(shí)斷面客流量進(jìn)行運(yùn)營(yíng)編組設(shè)計(jì),更貼近客流的實(shí)際客流情況,具有實(shí)時(shí)性、靈活性和快速響應(yīng)性。將城市軌道交通客流以一周為一個(gè)周期,使用最近一周的歷史斷面客流作為訓(xùn)練樣本,應(yīng)用嵌入式灰色神經(jīng)網(wǎng)絡(luò)組合模型進(jìn)行短時(shí)預(yù)測(cè),即可得到下一周期的斷面客流短時(shí)預(yù)測(cè)量,得到的斷面客流量更符合客流不斷變化的趨勢(shì)。在此基礎(chǔ)上進(jìn)行運(yùn)營(yíng)編組設(shè)計(jì),使得城市軌道交通更能適應(yīng)客流量的實(shí)時(shí)變化,可以滿足不斷變化的客流需求。然后將按照計(jì)劃運(yùn)營(yíng)所得到的實(shí)際客流歸入歷史客流,進(jìn)行更新修正,作為下一周期客流短時(shí)預(yù)測(cè)及運(yùn)營(yíng)編組設(shè)計(jì)的基礎(chǔ)。通過(guò)基于短時(shí)預(yù)測(cè)的城市軌道交通運(yùn)營(yíng)編組優(yōu)化,在一定程度上可以提高城市軌道交通系統(tǒng)運(yùn)能,提升運(yùn)營(yíng)效率,降低運(yùn)營(yíng)成本。
[Abstract]:With the acceleration of urbanization in China, the rapid growth of urban population, increasing traffic pressure, urban road congestion, to the normal travel of citizens has brought great inconvenience, urban traffic problems are increasingly prominent. In order to solve this problem, the hope should not only be placed on the highway construction, but also the development of urban rail transit is a good way to deal with urban traffic congestion. At present, the construction of urban rail transit in our country is in an unprecedented period of vigorous development. The rapid development has also brought many problems at the same time. The main problems are that the forecast of urban rail transit passenger flow is not accurate and the form of train formation is not suitable. Because of the inaccuracy of the routine passenger flow prediction in the past, the operational marshalling design based on it is not suitable, which leads to the overcrowded or wasteful urban rail transit at the present stage, which leads to the increase of the operation cost of the urban rail transit. In this paper, based on the existing research, the characteristics of urban rail transit passenger flow are summarized, such as time-varying, equilibrium and periodic change, etc. Based on grey prediction model and neural network model, the combined model of short time forecast of urban rail transit passenger flow is constructed. The neural network model is used to correct the residual error of the grey prediction model. The two models complement each other and have some rationality and reference for the short-term passenger flow prediction of urban rail transit section. It can be used as the basis for the optimization of urban rail transit operation marshalling design. According to the principle of intelligent traffic signal light and according to the real-time section passenger flow forecast of urban rail transit, the operation marshalling design is carried out, which is closer to the actual passenger flow situation of passenger flow, and has the characteristics of real-time, flexibility and quick response. Taking the urban rail transit passenger flow as a cycle, using the historical section passenger flow of the last week as the training sample, the embedded grey neural network combination model is used for short-term prediction. The short-term prediction of cross-section passenger flow in the next cycle can be obtained, and the obtained cross-section passenger flow is more in line with the changing trend of passenger flow. On this basis, the operation marshalling design is carried out to make the urban rail transit more adaptable to the real-time change of the passenger flow and to meet the changing demand of the passenger flow. Then the actual passenger flow according to the planned operation is classified into the historical passenger flow and updated and revised as the basis for the short-term prediction of passenger flow in the next cycle and the design of operational marshalling. Through the optimization of urban rail transit operation organization based on short-term prediction, the operation capacity of urban rail transit system can be improved to a certain extent, the operation efficiency can be improved, and the operation cost can be reduced.
【學(xué)位授予單位】：大連交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：U293.5;U293.13

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