本文關(guān)鍵詞： OD調(diào)查 OD矩陣 手機定位 停留點 出行鏈 交通小區(qū)　出處：《西南交通大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：城市交通是城市發(fā)展的基礎(chǔ)和前提,是聯(lián)系當(dāng)代社會經(jīng)濟活動的紐帶,若是想要使一個城市快速發(fā)展起來,那么我們必須首先解決其交通問題。解決交通問題主要包括交通問題的診斷、交通系統(tǒng)的規(guī)劃和交通管理政策的制定,其一個重要的參考依據(jù)便是居民出行OD調(diào)查(Origin Destination Survey)。居民出行OD調(diào)查即調(diào)查居民出行從出發(fā)地點到目的地點的全面情況,了解居民出行的構(gòu)成、流量以及去向。傳統(tǒng)的居民出行OD調(diào)查方法主要采取人工詢問和表格調(diào)查的方式,如路邊詢問法、表格調(diào)查法、電話調(diào)查法等,這些傳統(tǒng)調(diào)查方法需要耗費大量的人力、財力,并且其抽樣率較低、得到的OD矩陣精確度不高：基于現(xiàn)代先進技術(shù)的出行OD調(diào)查方法主要包括微波檢測法、地感線圈檢測法、視頻圖像檢測法、GPS浮動車等,這些方法存在需要改動現(xiàn)有設(shè)備、安裝維護費用高、覆蓋面較小等不足。隨著移動通信技術(shù)的迅速發(fā)展,手機普及率逐漸升高,大量的手機用戶在日常出行中產(chǎn)生了海量的定位數(shù)據(jù),這些定位數(shù)據(jù)的采集無需更改現(xiàn)有的設(shè)備,并且具有全天候連續(xù)采集、抽樣率高、數(shù)據(jù)量龐大的特點,本論文基于手機定位數(shù)據(jù)的諸多優(yōu)點研究了利用這些手機定位數(shù)據(jù)來獲取居民出行OD矩陣的方法。本文首先簡述了論文研究的背景與意義、國內(nèi)外研究現(xiàn)狀以及本文的主要研究內(nèi)容,并就本研究相關(guān)的理論基礎(chǔ)進行了介紹；分析了手機定位數(shù)據(jù)中主要存在的問題,并針對其存在的數(shù)據(jù)缺陷、定位漂移、定位數(shù)據(jù)非等時采集等問題進行了相應(yīng)的數(shù)據(jù)預(yù)處理；然后提出了一種基于“空間-時間”雙層聚類的停留點識別算法,先在空間層面利用DBSCAN算法對定位點進行聚類,并利用K-近鄰法的思想優(yōu)化聚類結(jié)果,得到候選停留點,再在時間層上對定位數(shù)據(jù)聚類以對候選停留點進行劃分和取舍,識別出用戶出行的停留點和出行鏈；考慮到聚類和交通小區(qū)劃分之間存在很大的相似性,利用K-Means算法對停留點聚類實現(xiàn)交通小區(qū)劃分,其中,K-Means算法中的參數(shù)k根據(jù)交通小區(qū)劃分原則確定：然后將出行鏈映射到劃分好的交通小區(qū)中,從中識別區(qū)外出行,再經(jīng)過統(tǒng)計計算后得到居民出行OD矩陣：最后基于濰坊市電信用戶手機定位數(shù)據(jù),設(shè)計實驗驗證了基于手機定位數(shù)據(jù)的居民出行OD矩陣獲取方法,并結(jié)合GIS分析了濰坊市OD客流的地理空間分布特征。
[Abstract]:Urban transportation is the foundation and premise of urban development and the link to contemporary social and economic activities. If you want to make a city develop rapidly, Well, we must first solve its traffic problems. Solving traffic problems mainly includes the diagnosis of traffic problems, the planning of traffic systems and the formulation of traffic management policies. One of its important reference bases is Origin Destination Survey. The residents' travel OD survey is to investigate the overall situation of residents' travel from departure place to destination, and to understand the composition of residents' travel. Flow and destination. Traditional OD survey methods for residents' travel mainly adopt manual inquiry and form investigation, such as roadside inquiry, tabular investigation, telephone survey, etc. These traditional survey methods require a large amount of manpower and financial resources. And its sampling rate is low and the precision of OD matrix is not high. The methods of OD survey based on modern advanced technology mainly include microwave detection method, ground inductance coil detection method, video image detection method and GPS floating vehicle, etc. With the rapid development of mobile communication technology, the penetration rate of mobile phones has gradually increased, with the rapid development of mobile communication technology, such as the need to modify existing equipment, the high cost of installation and maintenance, and the small coverage. A large number of mobile phone users have generated a large amount of location data in their daily travel. The acquisition of these location data does not need to change the existing equipment, and it has the characteristics of continuous acquisition, high sampling rate and huge amount of data. Based on the advantages of mobile phone location data, this paper studies how to use these mobile phone location data to obtain the OD matrix of resident travel. Firstly, the background and significance of this paper are briefly described. Domestic and foreign research status and the main research content of this paper, and the related theoretical basis of this study are introduced, the main problems in mobile phone positioning data are analyzed, and in view of its data defects, positioning drift, In this paper, the corresponding data preprocessing is carried out for the non-isochronous data acquisition, and then an algorithm based on "space-time" two-layer clustering is proposed to identify the location points. Firstly, the DBSCAN algorithm is used to cluster the location points at the spatial level. Using the idea of K- nearest neighbor method to optimize the clustering results, the candidate stopover points are obtained, and then the location data are clustered on the time level to divide and choose the candidate stopover points, and the user travel stopover points and trip chains are identified. Considering that there is a great similarity between clustering and traffic cell division, K-Means algorithm is used to realize traffic cell division. The parameter k of K-Means algorithm is determined according to the principle of traffic cell division. Then the trip chain is mapped to the divided traffic area, from which the travel outside the area can be identified. Finally, based on the mobile phone location data of telecom users in Weifang, the method of obtaining OD matrix based on mobile phone positioning data is designed and verified. Combined with GIS, the spatial distribution characteristics of OD passenger flow in Weifang are analyzed.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U491.12;TP311.13

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