本文選題：換乘路徑優(yōu)化 + 客流預(yù)測(cè)�。� 參考：《北京交通大學(xué)》2015年博士論文

【摘要】：在智能交通系統(tǒng)(ITS)領(lǐng)域,交通網(wǎng)絡(luò)動(dòng)態(tài)客流分配理論是其關(guān)鍵技術(shù)。雖然動(dòng)態(tài)客流分配理論在傳統(tǒng)公共交通網(wǎng)絡(luò)中研究和應(yīng)用比較廣泛,但與軌道交通網(wǎng)絡(luò)中實(shí)時(shí)客流密集度相結(jié)合,提供動(dòng)態(tài)的換乘路徑信息方面存在不少亟待解決的問(wèn)題。交通誘導(dǎo)系統(tǒng)是軌道交通運(yùn)營(yíng)管理發(fā)展的方向與趨勢(shì),如何有效、實(shí)時(shí)地引導(dǎo)乘客選擇優(yōu)化的出行路徑,合理降低早晚高峰期間軌道路網(wǎng)中核心線(xiàn)路區(qū)間的客流運(yùn)營(yíng)壓力,是當(dāng)前交通系統(tǒng)工程科學(xué)研究熱點(diǎn)問(wèn)題之一。針對(duì)上述問(wèn)題,基于北京軌道交通路網(wǎng)的基礎(chǔ)客流數(shù)據(jù),本文重點(diǎn)研究了短期客流預(yù)測(cè)、客流密集度指數(shù)體系和客流路徑引導(dǎo)模型等關(guān)鍵問(wèn)題,實(shí)現(xiàn)了軌道交通網(wǎng)絡(luò)化運(yùn)營(yíng)中的乘客出行路徑選擇優(yōu)化的目的。本文的主要研究?jī)?nèi)容如下：(1)提出了基于支持向量機(jī)的短期組合客流預(yù)測(cè)算法。首先給出了遺傳算法與支持向量機(jī)組合的客流預(yù)測(cè)算法,其中遺傳算法能對(duì)支持向量機(jī)的參數(shù)選擇進(jìn)行優(yōu)化,使得組合算法具有更準(zhǔn)確的預(yù)測(cè)效果。其次提出了小波變換與支持向量機(jī)結(jié)合的客流預(yù)測(cè)算法,其中小波分解能無(wú)損地將客流信息分解為高頻和低頻數(shù)據(jù),并獲取多尺度細(xì)化的低頻序列,然后支持向量機(jī)對(duì)一個(gè)低頻和多個(gè)高頻序列進(jìn)行預(yù)測(cè),最后對(duì)預(yù)測(cè)的多個(gè)序列進(jìn)行小波重構(gòu)得到最終的客流預(yù)測(cè)結(jié)果。本文以北京軌道交通網(wǎng)絡(luò)客流數(shù)據(jù)為基礎(chǔ),在多種標(biāo)準(zhǔn)評(píng)價(jià)方式下,實(shí)證結(jié)果表明本文提出的客流預(yù)測(cè)算法與多種當(dāng)前比較流行的客流預(yù)測(cè)算法相比獲得更好的客流預(yù)測(cè)結(jié)果。(2)提出了一種基于軌道交通網(wǎng)絡(luò)客流密集度的路徑選擇模型。首先針對(duì)軌道交通網(wǎng)絡(luò)的劃分層次,提出了區(qū)間、線(xiàn)路和全路網(wǎng)客流密集度指數(shù),實(shí)證結(jié)果表明不同層次的客流密集度指數(shù)均能較好地反映軌道交通網(wǎng)絡(luò)的客流密集度。其次以客流密集度指數(shù)為基礎(chǔ),提出了一種基于軌道交通網(wǎng)絡(luò)客流密集度的路徑選擇非集計(jì)模型。該模型依據(jù)路網(wǎng)客流密集度和路網(wǎng)基礎(chǔ)數(shù)據(jù)實(shí)時(shí)計(jì)算路徑廣義費(fèi)用,動(dòng)態(tài)調(diào)整路徑分配比例并模擬路網(wǎng)客流分布。實(shí)證結(jié)果表明本文提出的路徑選擇模型能較好地模擬軌道交通網(wǎng)絡(luò)中的客流動(dòng)態(tài)分布變化。(3)提出了一種路徑引導(dǎo)下的時(shí)變比例調(diào)整模型。首先以客流密集度指數(shù)為基礎(chǔ),給出了一種根據(jù)交通誘導(dǎo)系統(tǒng)信息實(shí)時(shí)選擇廣義費(fèi)用最小路徑的路徑引導(dǎo)模型,以達(dá)到動(dòng)態(tài)調(diào)整軌道交通網(wǎng)絡(luò)客流壓力的目的。其次通過(guò)數(shù)學(xué)推導(dǎo)證明了模型的可行性,并推導(dǎo)驗(yàn)證了模型的多項(xiàng)基本性質(zhì)。最后以北京軌道交通客流數(shù)據(jù)為基礎(chǔ),從“區(qū)間--線(xiàn)路--路網(wǎng)”三個(gè)層次進(jìn)行模擬,實(shí)證結(jié)果表明交通誘導(dǎo)信息能較好地引導(dǎo)乘客選擇優(yōu)化的出行路徑,降低軌道交通網(wǎng)絡(luò)中部分線(xiàn)路和區(qū)域的客流密集度,合理降低早晚高峰期間核心線(xiàn)路的客流運(yùn)營(yíng)壓力,實(shí)現(xiàn)換乘優(yōu)化,達(dá)到改善路網(wǎng)客流狀況的目的。
[Abstract]:In the field of Intelligent Transportation system (its), the theory of dynamic passenger flow assignment in traffic network is the key technology. Although the theory of dynamic passenger flow assignment is widely studied and applied in the traditional public transport network, there are many problems to be solved urgently in providing dynamic transfer path information by combining with the real-time passenger flow density in rail transit network. Traffic guidance system is the development direction and trend of rail transit operation management. How to effectively and real-time guide passengers to choose the optimized travel path and reasonably reduce the passenger flow operation pressure in the core section of rail network during the morning and evening rush hour. It is one of the hot issues in the research of traffic system engineering. In view of the above problems, based on the basic passenger flow data of Beijing rail transit network, this paper focuses on the key issues such as short-term passenger flow prediction, passenger flow intensity index system and passenger flow path guidance model, etc. The purpose of optimizing the passenger travel path in the network operation of rail transit is realized. The main contents of this paper are as follows: (1) A short-term combined passenger flow prediction algorithm based on support vector machine (SVM) is proposed. In this paper, a passenger flow prediction algorithm based on the combination of genetic algorithm and support vector machine is presented, in which the genetic algorithm can optimize the parameter selection of support vector machine, so that the combination algorithm has more accurate prediction effect. Secondly, a passenger flow prediction algorithm based on wavelet transform and support vector machine is proposed, in which wavelet decomposition can decompose the passenger flow information into high frequency and low frequency data, and obtain the low frequency sequence with multi-scale thinning. Then support vector machine (SVM) is used to predict one low frequency and several high frequency sequences. Finally, wavelet reconstruction of multiple predicted sequences is carried out to get the final result of passenger flow prediction. Based on the passenger flow data of Beijing rail transit network, this paper is based on a variety of standard evaluation methods. The empirical results show that the proposed passenger flow forecasting algorithm is better than many popular passenger flow forecasting algorithms. (2) A route selection model based on the passenger flow density of rail transit network is proposed. First of all, according to the hierarchy of rail transit network, the author puts forward the passenger flow intensity index of interval, route and the whole network. The empirical results show that the different levels of passenger flow intensity index can reflect the passenger flow intensity of rail transit network. Secondly, based on the passenger flow intensity index, a path selection disaggregate model based on the passenger flow density of rail transit network is proposed. Based on the density of the passenger flow and the basic data of the road network, the model calculates the generalized cost of the route in real time, dynamically adjusts the distribution ratio of the route and simulates the distribution of the passenger flow in the road network. The empirical results show that the proposed path selection model can well simulate the dynamic distribution of passenger flow in rail transit networks. (3) A time-varying proportional adjustment model under path guidance is proposed. Based on the passenger flow intensity index, this paper presents a real-time route guidance model for selecting the generalized minimum cost path according to the traffic guidance system information, in order to dynamically adjust the passenger flow pressure of rail transit network. Secondly, the feasibility of the model is proved by mathematical derivation, and many basic properties of the model are proved. Finally, based on the passenger flow data of Beijing rail transit, the simulation is carried out from the three levels of "interval-line-road network". The empirical results show that the traffic guidance information can better guide passengers to choose the optimal travel path. The passenger flow density of some routes and regions in the rail transit network is reduced, the operation pressure of the core lines during the morning and evening peak period is reduced reasonably, the transfer optimization is realized, and the passenger flow condition of the road network is improved.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U291.75;U495

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本文編號(hào)：2063309