本文選題：城市軌道交通車站　切入點(diǎn)：慢行交通吸引　出處：《蘭州交通大學(xué)》2014年碩士論文

【摘要】：城市軌道交通作為現(xiàn)代城市交通的干線交通，是一種重要的綠色交通方式，與步行、自行車、電動(dòng)車三種慢行交通，一同構(gòu)成了城市居民出行方式的重要組成部分。本文主要是研究乘客在慢行交通換乘軌道交通過程中對(duì)慢行交通方式的選擇問題，通過采用數(shù)據(jù)調(diào)查并建立ML離散選擇模型，可為軌道交通站點(diǎn)對(duì)慢行交通客流吸引量的預(yù)測(cè)做基礎(chǔ)性工作，為城市軌道交通站點(diǎn)布置和設(shè)計(jì)提供基礎(chǔ)性數(shù)據(jù)和參考依據(jù)。通過研究城市軌道交通站點(diǎn)對(duì)周圍區(qū)域的慢行交通量的吸引問題，并應(yīng)用于軌道交通量預(yù)測(cè)，可使慢行交通與城市軌道交通換乘更協(xié)調(diào)，使城市綠色交通出行更為便利。 論文首先從我國(guó)城市交通擁堵、軌道交通以及綠色交通的發(fā)展等角度入手，對(duì)研究背景進(jìn)行闡述，分析了研究慢行交通與城市軌道交通換乘銜接問題的重要性與必要性。通過介紹國(guó)內(nèi)外相關(guān)的研究成果，并進(jìn)行分析總結(jié)，得出兩點(diǎn)結(jié)論：對(duì)銜接軌道交通站點(diǎn)的慢行交通方式特性的研究不夠；對(duì)銜接軌道交通站點(diǎn)的慢行交通方式吸引量預(yù)測(cè)研究不足。介紹了離散選擇模型的基本理論知識(shí)以及SP與RP調(diào)查方法的基礎(chǔ)理論知識(shí)。并以武漢軌道交通車站王家墩東站為例，進(jìn)行數(shù)據(jù)調(diào)查工作，在調(diào)查方案設(shè)計(jì)中分別設(shè)計(jì)了RP調(diào)查內(nèi)容與SP調(diào)查內(nèi)容。在確定調(diào)查區(qū)域范圍時(shí)，提出了“扇形分段區(qū)域抽樣法”這一調(diào)查區(qū)域選定方法，確定了所要調(diào)查的區(qū)域，使調(diào)查區(qū)域更具有隨機(jī)性與代表性。在慢行出行方式選擇中，基于電動(dòng)車的出行特點(diǎn)，提出將其歸類于慢行交通方式，與步行、自行車一起構(gòu)成慢行交通方式選擇項(xiàng)集合。 利用SPSS軟件對(duì)各因素與慢行交通方式選擇相關(guān)性進(jìn)行了數(shù)據(jù)分析。通過計(jì)算各種影響因素的卡方檢驗(yàn)數(shù)據(jù)，確定離散變量為慢行交通工具擁有種類、住址距軌道交通站的距離、交通服務(wù)水平，連續(xù)變量為出行費(fèi)用。最后，根據(jù)調(diào)查數(shù)據(jù)，聯(lián)合RP數(shù)據(jù)與SP數(shù)據(jù)建立融合離散選擇模型，即ML模型。通過transcad軟件對(duì)ML模型進(jìn)行標(biāo)定，結(jié)果顯示：平衡系數(shù)檢驗(yàn)值為3.792，較為顯著，離散變量中乘客慢行交通工具擁有種類各檢驗(yàn)值為：無(wú)自行車與電動(dòng)車的t檢驗(yàn)值為1.274，只擁有自行車的t檢驗(yàn)值為1.650，，只擁有電動(dòng)車的t檢驗(yàn)值為1.703，其它變量的檢驗(yàn)值也都比較顯著。由此可認(rèn)為所建立的RP/SP數(shù)據(jù)融合離散選擇模型具有很好的應(yīng)用參考價(jià)值，并可作為城市軌道軌道站點(diǎn)慢行交通吸引客流預(yù)測(cè)研究的參考。
[Abstract]:Urban rail transit, as the main line of modern urban traffic, is an important green transportation mode, and three kinds of slow-moving traffic, such as walking, bicycle and electric vehicle, This paper mainly studies the choice of slow transit mode in the process of slow transit transfer rail transit, and establishes ML discrete choice model by adopting data survey. It can do basic work for the prediction of passenger attraction to slow traffic at rail transit stations. This paper provides basic data and reference basis for the layout and design of urban rail transit stations. By studying the attraction of urban rail transit stations to the slow traffic volume in the surrounding area, the paper applies it to the prediction of the traffic volume of urban rail transit. It can make slow transit and urban rail transit more harmonious, and make urban green transportation more convenient. Firstly, from the point of view of urban traffic congestion, rail transit and the development of green traffic in China, the paper expounds the background of the research. This paper analyzes the importance and necessity of studying the connection between slow transit and urban rail transit, introduces the related research results at home and abroad, and makes an analysis and summary. Two conclusions are drawn: the study on the characteristics of slow transit mode connecting rail transit stations is not enough; This paper introduces the basic theoretical knowledge of discrete selection model and the basic theoretical knowledge of SP and RP investigation methods, and takes Wangjiadun East Station of Wuhan Rail Transit Station as an example. The contents of RP investigation and SP investigation are designed in the design of investigation scheme. When determining the scope of investigation area, the method of "sector sectional sampling" is put forward to select the investigation area. The area to be investigated is determined, which makes the investigation area more random and representative. In the selection of slow trip mode, based on the travel characteristics of electric vehicle, it is proposed to classify it into slow traffic mode and walking mode. Bicycles together form a collection of slow traffic mode options. The correlation between each factor and the choice of slow traffic mode is analyzed by using SPSS software. By calculating the chi-square test data of various influencing factors, the discrete variables are determined to be the type of slow traffic vehicle. The distance between address and rail transit station, the level of traffic service, and the continuous variables are the travel costs. Finally, according to the survey data, combining RP data with SP data, a fusion discrete selection model is established. ML model. The ML model is calibrated by transcad software. The results show that the balance coefficient test value is 3.792, which is significant. Among the discrete variables, the test value of the type of passenger slow-moving vehicle is 1.274 for non-bicycle and electric vehicle, 1.650 for bicycle only, 1.703 for electric vehicle, and 1.703 for other variables. It can be concluded that the discrete selection model of RP/SP data fusion has good reference value. It can be used as a reference for the prediction of slow-moving traffic in urban rail stations.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U239.5;U491.12

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