本文關(guān)鍵詞：基于手機(jī)定位數(shù)據(jù)的城市居民出行特征提取方法研究　出處：《東南大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

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【摘要】：利用手機(jī)定位技術(shù)采集居民出行信息具有實(shí)時(shí)性、低成本、樣本大、易實(shí)施等優(yōu)點(diǎn),與傳統(tǒng)出行調(diào)查優(yōu)勢(shì)互補(bǔ),成為出行特征研究的又一重要數(shù)據(jù)來(lái)源。然而手機(jī)定位數(shù)據(jù)時(shí)間分布不均勻,且定位精度具有不確定性,影響因素繁復(fù),目前已有的出行特征提取方法還存在一定不足。本研究基于大規(guī)模真實(shí)手機(jī)信令定位數(shù)據(jù),從數(shù)據(jù)時(shí)空特性角度,嘗試建立一種更為準(zhǔn)確可行的出行特征提取方法,具有重要的理論與工程實(shí)踐意義。第一,為了對(duì)定位數(shù)據(jù)產(chǎn)生的機(jī)制有一個(gè)全面深入的了解,研究對(duì)蜂窩通信網(wǎng)絡(luò)和手機(jī)定位技術(shù)的基本原理進(jìn)行介紹,并對(duì)手機(jī)信令數(shù)據(jù)內(nèi)涵進(jìn)行深入解讀。在此基礎(chǔ)上,結(jié)合出行的一般定義和手機(jī)定位數(shù)據(jù)特點(diǎn),對(duì)一次“手機(jī)出行”進(jìn)行定義,并進(jìn)一步說(shuō)明利用手機(jī)定位技術(shù)提取出行特征的適用性,為后續(xù)研究打下鋪墊。第二,數(shù)據(jù)處理與特性分析。研究深入分析定位數(shù)據(jù)產(chǎn)生機(jī)制,并借鑒已有的研究成果,建立一種更為完善的多層次數(shù)據(jù)處理方法,包括對(duì)多種無(wú)效數(shù)據(jù)過(guò)濾、多種噪音數(shù)據(jù)識(shí)別處理等,進(jìn)而為出行特征提取提供了高質(zhì)量數(shù)據(jù)來(lái)源。然后,研究從事件類型、時(shí)間、距離、平均速度等多個(gè)維度,分析數(shù)據(jù)特性,為建立出行特征提取模型提供依據(jù)。第三,建立出行特征提取模型。在以上研究的基礎(chǔ)上,研究深入分析手機(jī)定位軌跡點(diǎn)時(shí)空特征,發(fā)現(xiàn)手機(jī)用戶出行軌跡點(diǎn)主要由代表停留的圓形停留區(qū)域以及代表出行的長(zhǎng)條形出行區(qū)域組成�；诖�,研究提出一種時(shí)空聚類算法識(shí)別圓形停留區(qū)域,將軌跡點(diǎn)劃分為停留點(diǎn)和運(yùn)動(dòng)點(diǎn),同時(shí)獲取停留起訖時(shí)間、停留位置等多種出行信息。基于以上信息,研究建立了出行次數(shù)、出行距離、出行速度、出行時(shí)間分布等多種出行特征指標(biāo)的計(jì)算方法。最后,研究多角度分析了從手機(jī)出行到用戶出行的擴(kuò)樣影響因素,建立多層擴(kuò)樣方法。第四,實(shí)例分析與驗(yàn)證。首先,研究結(jié)合“手機(jī)出行”定義,對(duì)模型中每個(gè)參數(shù)進(jìn)行影響分析和標(biāo)定。然后,研究選取上海市某工作日大規(guī)模手機(jī)定位數(shù)據(jù)進(jìn)行示例分析,并將模型分析結(jié)果與上海市第四次出行調(diào)查數(shù)據(jù)進(jìn)行對(duì)比分析。對(duì)比發(fā)現(xiàn)出行次數(shù)、出行時(shí)間分布等多個(gè)出行特征指標(biāo)與居民調(diào)查數(shù)據(jù)高度相關(guān),CORREL檢驗(yàn)值在0.92以上,說(shuō)明研究提出的出行特征提取方法具有較好的可靠性和適用性。最后,研究總結(jié)模型方法的創(chuàng)新與不足,并對(duì)后續(xù)研究提出展望。
[Abstract]:The use of mobile phone positioning technology to collect resident travel information has the advantages of real-time, low cost, large sample, easy to implement, and complementary with the traditional travel survey. It has become another important data source for the study of travel characteristics. However, the time distribution of mobile phone location data is uneven, the location accuracy is uncertain, and the influencing factors are complicated. At present, there are still some shortcomings in the existing travel feature extraction methods. This study is based on large-scale real mobile phone signaling location data, from the point of view of data space-time characteristics. Try to establish a more accurate and feasible travel feature extraction method, which has important theoretical and engineering significance. First, in order to have a comprehensive and in-depth understanding of the mechanism of location data generation. This paper introduces the basic principles of cellular communication network and mobile phone location technology, and deeply interprets the meaning of mobile phone signaling data. On this basis, combined with the general definition of travel and mobile phone location data characteristics. To define a "mobile travel", and further explain the use of mobile phone location technology to extract travel characteristics of applicability, for the follow-up study lay the groundwork. Second. Data processing and characteristic analysis. Research on location data generation mechanism, and use for reference of existing research results, establish a more perfect multi-level data processing method, including a variety of invalid data filtering. A variety of noise data processing, which provides a high-quality data source for travel feature extraction. Then, from the event type, time, distance, average speed and other dimensions, analysis of data characteristics. In order to establish a travel feature extraction model to provide the basis. Third, establish a trip feature extraction model. On the basis of the above research, in-depth analysis of mobile phone locus locus space-time features. It is found that the mobile phone user travel path points are mainly composed of the circular stay area which represents the stay and the long travel area which represents the trip. Based on this, a spatio-temporal clustering algorithm is proposed to identify the circular stay area. Track points are divided into stopover points and motion points, and a variety of travel information, such as stop time, stop position and so on, are obtained. Based on the above information, the number of trips, travel distance and travel speed are studied. Finally, the paper analyzes the influence factors of mobile phone travel to user travel, and establishes a multi-layer sample expansion method. 4th. First, combining the definition of "mobile phone trip", each parameter in the model is analyzed and calibrated. The study selected a large-scale mobile phone location data for a workday in Shanghai for example analysis, and the results of model analysis and Shanghai 4th trip survey data were compared and analyzed. Travel time distribution and other travel characteristics are highly correlated with resident survey data and Correl test value is more than 0.92. The results show that the proposed method has good reliability and applicability. Finally, the innovation and deficiency of the model method are summarized, and the prospect of the future research is put forward.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491

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