乘客績效導向的快速公交系統(tǒng)評估研究
發(fā)布時間：2019-06-14 02:13
【摘要】：快速公交系統(tǒng)(BRT)是一種解決城市交通擁堵、環(huán)境污染、資源浪費等問題的有效手段。但BRT系統(tǒng)在我國的發(fā)展時間較短,除了少有的幾個城市,許多城市發(fā)展BRT處于示范引導作用。因此,本文從乘客的角度,選取BRT乘客績效指標,采用改進了的評估模型,研究乘客績效導向的BRT系統(tǒng)的評估,通過城市間的橫向?qū)Ρ?實現(xiàn)公共財政投入到BRT系統(tǒng)的績效監(jiān)管,既可為政府指出公共財政投資BRT系統(tǒng)建設、運營的著力點,也可為已建成BRT系統(tǒng)的城市提供高效評估的方法。本文回顧了國內(nèi)外關于BRT系統(tǒng)績效評估以及模糊神經(jīng)網(wǎng)絡集成方法等方面的相關研究。在此基礎上,對BRT系統(tǒng)的構成與功能定位,基于不同主體的績效展開闡述。對績效指標進行分析整合出乘客最關注的BRT的三個準則7個指標,構成本文的評估指標體系。同時,基于模糊評價法與神經(jīng)網(wǎng)絡的優(yōu)缺點,提出采用兩者集成的思想對評估模型進行改進。然后,提出模糊BP神經(jīng)網(wǎng)絡的評估步驟與數(shù)據(jù)處理方法,以及虛擬樣本生成技術解決訓練樣本量不足的方法。最后,以廣州、杭州、常州和濟南為的績效指標數(shù)據(jù)為基礎,基礎數(shù)據(jù)通過數(shù)據(jù)歸一化處理、虛擬樣本生成技術、模糊評價法等處理,得到神經(jīng)網(wǎng)絡的訓練樣本。最后通過樣本測試,完成驗證,得出相關結論。最后,本文所選的指標能夠反映乘客的績效關注點,改進的模糊BP神經(jīng)網(wǎng)絡模型能較為客觀、高效完成績效評估的計算。所采用虛擬樣本生成技術拓展樣本,既能保證原始樣本的特征,并提高神經(jīng)網(wǎng)絡的泛化能力,在其他城市同類項目的績效評估中具有可推廣性。論文創(chuàng)新點包括以下兩個方面:首先,模糊BP神經(jīng)網(wǎng)絡模型是一種基于模糊評價法與神經(jīng)網(wǎng)絡集成的改進方法,本文對模型的改進,并利用其它城市的數(shù)據(jù)進行訓練,并通過測試,驗證模型的可用性。其次,對于神經(jīng)網(wǎng)絡性能而言,過大或過少的訓練樣本均會影響神經(jīng)網(wǎng)絡的泛化能力和適應性。本文提出基于擾動思想的虛擬樣本生成技術拓展樣本,不僅能夠提神經(jīng)網(wǎng)絡的泛化能力,而且能夠有效避免陷入局部最小值。
[Abstract]:Bus rapid transit system (BRT) is an effective means to solve the problems of urban traffic congestion, environmental pollution, waste of resources and so on. However, the development time of BRT system in China is relatively short. In addition to a few cities, many cities are in the role of demonstration and guidance of BRT development. Therefore, from the point of view of passengers, this paper selects BRT passenger performance index, adopts the improved evaluation model, studies the evaluation of passenger performance-oriented BRT system, and realizes the performance supervision of public financial investment into BRT system through the horizontal comparison between cities, which can not only point out the focus of public finance investment BRT system construction and operation, but also provide an efficient evaluation method for cities that have built BRT system. In this paper, the research on performance evaluation of BRT system and fuzzy neural network integration method at home and abroad are reviewed. On this basis, the composition and functional positioning of BRT system are described based on the performance of different subjects. The analysis of the performance indicators integrates the three criteria and seven indicators of BRT, which are of the greatest concern to passengers, and constitutes the evaluation index system of this paper. At the same time, based on the advantages and disadvantages of fuzzy evaluation method and neural network, the idea of integration of fuzzy evaluation method and neural network is proposed to improve the evaluation model. Then, the evaluation steps and data processing methods of fuzzy BP neural network are put forward, and the method of virtual sample generation technology to solve the shortage of training samples is put forward. Finally, based on the performance index data of Guangzhou, Hangzhou, Changzhou and Jinan, the basic data are processed by data normalization, virtual sample generation technology, fuzzy evaluation method and so on, and the training samples of neural network are obtained. Finally, through the sample test, complete the verification, draw the relevant conclusions. Finally, the indicators selected in this paper can reflect the performance concerns of passengers, and the improved fuzzy BP neural network model can complete the calculation of performance evaluation more objectively and efficiently. The virtual sample generation technology can not only ensure the characteristics of the original sample, but also improve the generalization ability of the neural network, which can be extended in the performance evaluation of the same kind of projects in other cities. The innovation of this paper includes the following two aspects: firstly, the fuzzy BP neural network model is an improved method based on the integration of fuzzy evaluation method and neural network. In this paper, the model is improved and trained with the data of other cities, and the availability of the model is verified by testing. Secondly, for the performance of neural network, too large or too few training samples will affect the generalization ability and adaptability of neural network. In this paper, a virtual sample generation technique based on disturbance idea is proposed to expand the sample, which can not only improve the generalization ability of neural network, but also effectively avoid falling into the local minimum.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.17
本文編號：2499015
[Abstract]:Bus rapid transit system (BRT) is an effective means to solve the problems of urban traffic congestion, environmental pollution, waste of resources and so on. However, the development time of BRT system in China is relatively short. In addition to a few cities, many cities are in the role of demonstration and guidance of BRT development. Therefore, from the point of view of passengers, this paper selects BRT passenger performance index, adopts the improved evaluation model, studies the evaluation of passenger performance-oriented BRT system, and realizes the performance supervision of public financial investment into BRT system through the horizontal comparison between cities, which can not only point out the focus of public finance investment BRT system construction and operation, but also provide an efficient evaluation method for cities that have built BRT system. In this paper, the research on performance evaluation of BRT system and fuzzy neural network integration method at home and abroad are reviewed. On this basis, the composition and functional positioning of BRT system are described based on the performance of different subjects. The analysis of the performance indicators integrates the three criteria and seven indicators of BRT, which are of the greatest concern to passengers, and constitutes the evaluation index system of this paper. At the same time, based on the advantages and disadvantages of fuzzy evaluation method and neural network, the idea of integration of fuzzy evaluation method and neural network is proposed to improve the evaluation model. Then, the evaluation steps and data processing methods of fuzzy BP neural network are put forward, and the method of virtual sample generation technology to solve the shortage of training samples is put forward. Finally, based on the performance index data of Guangzhou, Hangzhou, Changzhou and Jinan, the basic data are processed by data normalization, virtual sample generation technology, fuzzy evaluation method and so on, and the training samples of neural network are obtained. Finally, through the sample test, complete the verification, draw the relevant conclusions. Finally, the indicators selected in this paper can reflect the performance concerns of passengers, and the improved fuzzy BP neural network model can complete the calculation of performance evaluation more objectively and efficiently. The virtual sample generation technology can not only ensure the characteristics of the original sample, but also improve the generalization ability of the neural network, which can be extended in the performance evaluation of the same kind of projects in other cities. The innovation of this paper includes the following two aspects: firstly, the fuzzy BP neural network model is an improved method based on the integration of fuzzy evaluation method and neural network. In this paper, the model is improved and trained with the data of other cities, and the availability of the model is verified by testing. Secondly, for the performance of neural network, too large or too few training samples will affect the generalization ability and adaptability of neural network. In this paper, a virtual sample generation technique based on disturbance idea is proposed to expand the sample, which can not only improve the generalization ability of neural network, but also effectively avoid falling into the local minimum.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.17
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