本文選題：均衡配置 + 基尼系數(shù)�。� 參考：《昆明理工大學(xué)》2012年碩士論文

【摘要】：面對(duì)城市日益增長的客流需求,最大限度地優(yōu)化公交運(yùn)力資源配置勢在必行。改善公交運(yùn)力配置,不權(quán)僅是提高公交運(yùn)營效率的需要,更是提高公交服務(wù)水平,實(shí)現(xiàn)公交運(yùn)力資源均衡配置和創(chuàng)建環(huán)境友好型社會(huì)的內(nèi)在需要。 要解決公交問題,僅僅依靠增加公交車的數(shù)量,或者調(diào)整公交線路并不能從根本上解決問題,相反,一味地增加公交車的數(shù)量,不權(quán)會(huì)造成公交資源的浪費(fèi),更會(huì)加大城市交通的壓力。因此,只有充分地利用現(xiàn)有的公交資源,在滿足公交企業(yè)和乘客多方利益的前提下,對(duì)相關(guān)資源進(jìn)行優(yōu)化配置,才是解決問題的關(guān)鍵所在 本文首先在綜合分析公交運(yùn)力配置的影響囚素的基礎(chǔ)上,將公交運(yùn)力優(yōu)化配置問題定性為多目標(biāo)優(yōu)化問題,并進(jìn)一步介紹了基于基尼系數(shù)的多目標(biāo)優(yōu)化模型,在分析原模型的基礎(chǔ)上,引進(jìn)了基尼系數(shù)這一經(jīng)濟(jì)學(xué)指標(biāo),通過量化表示將資源配置均衡度作為新的目標(biāo)函數(shù)加入原模型,得到了改進(jìn)的新模型。并通過實(shí)例化數(shù)據(jù)驗(yàn)證,通過前后的調(diào)整,各站點(diǎn)的客流量分擔(dān)率(基尼系數(shù))由原來的0.29改進(jìn)為0.21,從客流量分布均衡性的角度說明了新模型比原模型具有較好的現(xiàn)實(shí)擬合度。 本文明確了客流量預(yù)測在整個(gè)公交運(yùn)力配置流程中的關(guān)鍵作用�？土髁康臏�(zhǔn)確預(yù)測是公交資源優(yōu)化配置的依據(jù)和前提。本文給出了基于Elman神經(jīng)網(wǎng)絡(luò)的公交客流量預(yù)測流程。最后,通過數(shù)據(jù)搜集,利用北京市某條線路近8年的客流量數(shù)據(jù)進(jìn)行了實(shí)例預(yù)測。最后通過與當(dāng)前較流行的BP靜態(tài)神經(jīng)網(wǎng)絡(luò)比較,發(fā)現(xiàn)Elman神經(jīng)網(wǎng)絡(luò)整體上比BP神經(jīng)網(wǎng)絡(luò)具有較高的預(yù)測精度。
[Abstract]:In the face of the increasing demand of passenger flow in cities, it is imperative to optimize the allocation of public transport capacity resources to the maximum extent. To improve the allocation of public transport capacity is not only the need to improve the efficiency of public transport operation, but also to improve the level of public transport service, to achieve the balanced allocation of public transport capacity resources and to create an environment-friendly society. To solve the problem of public transport, merely relying on increasing the number of buses, or adjusting the bus routes, cannot fundamentally solve the problem. On the contrary, blindly increasing the number of buses will result in a waste of public transport resources. Will increase the pressure of urban traffic. Therefore, the key to solve the problem is to make full use of existing public transport resources and to optimize the allocation of related resources on the premise of satisfying the interests of public transport enterprises and passengers. Based on the comprehensive analysis of the influence of public transport capacity allocation, this paper defines the optimal allocation of public transport capacity as a multi-objective optimization problem, and further introduces the multi-objective optimization model based on Gini coefficient. Based on the analysis of the original model, this paper introduces the Gini coefficient as an economic index, and adds the equilibrium degree of resource allocation as a new objective function to the original model through quantitative expression, and obtains the improved new model. Through the validation of the instantiation data, the Gini coefficient of each station is improved from 0.29 to 0.21 through the adjustment before and after, which shows that the new model has better realistic fit than the original model from the point of view of the equilibrium of passenger flow distribution. This paper clarifies the key role of passenger flow prediction in the whole public transportation capacity allocation process. The accurate forecast of passenger flow is the basis and premise of the optimal allocation of public transportation resources. This paper presents the flow of bus passenger flow forecasting based on Elman neural network. Finally, through the data collection, using the passenger flow data of a certain line in Beijing in the past 8 years, a case prediction is carried out. Finally, compared with the popular BP static neural network, it is found that the Elman neural network has a higher prediction accuracy than the BP neural network as a whole.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F572.88

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本文編號(hào)：1968103