本文選題：公路節(jié)點(diǎn) + MD模型��； 參考：《北京工業(yè)大學(xué)》2015年碩士論文

【摘要】：科學(xué)的運(yùn)量預(yù)測對區(qū)域內(nèi)各種客運(yùn)方式的規(guī)劃建設(shè)、運(yùn)輸組織、經(jīng)濟(jì)效益及市場分配等有巨大的影響,而現(xiàn)有的公路客運(yùn)量預(yù)測方法多以短期預(yù)測為主,能夠精細(xì)預(yù)測中長期的方法較少,且隨著高鐵客運(yùn)的加入導(dǎo)致產(chǎn)生誘增運(yùn)量及公路客運(yùn)產(chǎn)生轉(zhuǎn)移運(yùn)量。為了能夠合理預(yù)測,本論文對公路節(jié)點(diǎn)進(jìn)行系統(tǒng)性研究,并對MD模型進(jìn)行改善與深化,完善了其理論,建立了基于MD模型的公路節(jié)點(diǎn)客運(yùn)量的預(yù)測流程。論文首先對國內(nèi)外公路客運(yùn)量的預(yù)測方法進(jìn)行分析總結(jié),對本文要采用的MD模型預(yù)測法的國內(nèi)外研究成果進(jìn)行闡述。其次,在“北京市城市交通運(yùn)行保障工程技術(shù)研究中心”開放基金項(xiàng)目與“北京市公路客運(yùn)樞紐站場規(guī)劃布局基礎(chǔ)研究”兩個(gè)課題的支撐下,以北京市為例,對公路客運(yùn)客流機(jī)理、需求結(jié)構(gòu)及影響因素進(jìn)行分析,提出公路節(jié)點(diǎn)的運(yùn)營組織模式,并從定形、定性、定向、定量四個(gè)方面對公路節(jié)點(diǎn)進(jìn)行系統(tǒng)性分析,為公路節(jié)點(diǎn)客運(yùn)量預(yù)測奠定理論基礎(chǔ)。再次,采用支持向量機(jī)、RBF神經(jīng)網(wǎng)絡(luò)、時(shí)序預(yù)測三種典型的預(yù)測方法對北京市公路客運(yùn)量進(jìn)行預(yù)測,對比各種方法的適用范圍及優(yōu)缺點(diǎn),并對MD模型的適用性進(jìn)行了分析。基于此,在MD模型的出行犧牲量模型中加入出行疲勞度和延誤率兩因素,通過追蹤車輛的方法對這兩個(gè)因素的相關(guān)參數(shù)進(jìn)行了調(diào)查,進(jìn)而構(gòu)建新的出行犧牲量模型。針對出行者的行為時(shí)間價(jià)值,首次引入基尼系數(shù)來確定時(shí)間價(jià)值的方差,進(jìn)一步改進(jìn)及完善MD模型的理論與方法,建立了一套完善的預(yù)測流程。最后,以京津唐經(jīng)濟(jì)圈為例,進(jìn)行公路節(jié)點(diǎn)客運(yùn)量需求預(yù)測。與原MD模型和Nested-Logit模型進(jìn)行對比,證明了改進(jìn)MD模型的合理性及有效性。該研究對促進(jìn)MD模型在我國公路客運(yùn)量預(yù)測的推廣及應(yīng)用具有重要的意義。
[Abstract]:Scientific traffic forecasting has a great influence on the planning and construction, transportation organization, economic benefit and market distribution of various passenger transport modes in the region. However, the existing highway passenger volume forecasting methods are mainly short-term forecasting.There are few methods to accurately predict the medium and long term, and with the addition of high-speed rail passenger, the induced volume of passenger transport and the transfer volume of road passenger transport are generated.In order to forecast reasonably, this paper makes systematic research on highway node, improves and deepens MD model, perfects its theory, and establishes the forecasting flow of highway node passenger volume based on MD model.Firstly, this paper analyzes and summarizes the forecasting methods of highway passenger volume at home and abroad, and expounds the domestic and foreign research results of MD model forecasting method to be adopted in this paper.Secondly, under the support of the open fund project of "Beijing Municipal Transportation Operation and support Engineering Technology Research Center" and the "basic Research on Planning and layout of Beijing Highway passenger Transport Hub Station", taking Beijing as an example,The mechanism, demand structure and influencing factors of highway passenger passenger flow are analyzed, and the operation organization mode of highway node is put forward, and the systematic analysis of highway node is carried out from four aspects: fixed, qualitative, orientated and quantitative.It lays a theoretical foundation for highway node passenger volume prediction.Thirdly, support vector machine (SVM) RBF neural network and three typical forecasting methods of time series are used to forecast the passenger volume of Beijing highway. The applicability of MD model is analyzed by comparing the applicable range, advantages and disadvantages of these methods.Based on this, two factors, travel fatigue and delay rate, are added to the travel sacrifice model of MD model, and the related parameters of these two factors are investigated by means of tracking the vehicle, and a new travel sacrifice model is constructed.According to the behavioral time value of the traveler, the Gini coefficient is introduced to determine the variance of the time value for the first time, the theory and method of MD model are further improved and improved, and a set of perfect forecasting flow is established.Finally, take the Beijing-Tianjin-Tang economic circle as an example, carries on the highway node passenger volume demand forecast.Compared with the original MD model and Nested-Logit model, the rationality and validity of the improved MD model are proved.This study is of great significance to promote the popularization and application of MD model in highway passenger volume prediction in China.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U492.413

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 宋雪梅;蔣陽升;云亮;;MD預(yù)測模型的計(jì)算方法研究[J];交通運(yùn)輸工程與信息學(xué)報(bào);2010年02期

2 王英濤;;高鐵時(shí)代我國道路客運(yùn)發(fā)展的新定位[J];綜合運(yùn)輸;2010年12期

，

本文編號：1742944