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

【摘要】：科學的運量預測對區(qū)域內各種客運方式的規(guī)劃建設、運輸組織、經濟效益及市場分配等有巨大的影響,而現(xiàn)有的公路客運量預測方法多以短期預測為主,能夠精細預測中長期的方法較少,且隨著高鐵客運的加入導致產生誘增運量及公路客運產生轉移運量。為了能夠合理預測,本論文對公路節(jié)點進行系統(tǒng)性研究,并對MD模型進行改善與深化,完善了其理論,建立了基于MD模型的公路節(jié)點客運量的預測流程。論文首先對國內外公路客運量的預測方法進行分析總結,對本文要采用的MD模型預測法的國內外研究成果進行闡述。其次,在“北京市城市交通運行保障工程技術研究中心”開放基金項目與“北京市公路客運樞紐站場規(guī)劃布局基礎研究”兩個課題的支撐下,以北京市為例,對公路客運客流機理、需求結構及影響因素進行分析,提出公路節(jié)點的運營組織模式,并從定形、定性、定向、定量四個方面對公路節(jié)點進行系統(tǒng)性分析,為公路節(jié)點客運量預測奠定理論基礎。再次,采用支持向量機、RBF神經網絡、時序預測三種典型的預測方法對北京市公路客運量進行預測,對比各種方法的適用范圍及優(yōu)缺點,并對MD模型的適用性進行了分析�；诖�,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因素的相關參數(shù)進行了調查,進而構建新的出行犧牲量模型。針對出行者的行為時間價值,首次引入基尼系數(shù)來確定時間價值的方差,進一步改進及完善MD模型的理論與方法,建立了一套完善的預測流程。最后,以京津唐經濟圈為例,進行公路節(jié)點客運量需求預測。與原MD模型和Nested-Logit模型進行對比,證明了改進MD模型的合理性及有效性。該研究對促進MD模型在我國公路客運量預測的推廣及應用具有重要的意義。
[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.
【學位授予單位】：北京工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U492.413

【參考文獻】

相關期刊論文 前2條

1 宋雪梅;蔣陽升;云亮;;MD預測模型的計算方法研究[J];交通運輸工程與信息學報;2010年02期

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

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本文編號：1742944