【摘要】：隨著衛(wèi)星技術(shù)、無(wú)線通訊技術(shù)和定位技術(shù)的迅猛發(fā)展,現(xiàn)在已可以快速、便捷地獲取海量的車(chē)輛軌跡數(shù)據(jù)。這些軌跡數(shù)據(jù)具有數(shù)量多、覆蓋廣、密度大的特點(diǎn),對(duì)其進(jìn)行分析和挖掘,可以獲得車(chē)輛關(guān)于移動(dòng)過(guò)程方面的信息。要應(yīng)用這些海量軌跡數(shù)據(jù),需首先對(duì)其進(jìn)行探索性分析,而該分析的重要基礎(chǔ)就是數(shù)據(jù)的可視化。車(chē)輛軌跡數(shù)據(jù)從其物理構(gòu)成上來(lái)說(shuō),由大量矢量軌跡點(diǎn)組成�，F(xiàn)有成熟商業(yè)數(shù)據(jù)庫(kù)管理軟件內(nèi)部采用的可視化機(jī)理是通過(guò)創(chuàng)建矢量索引的方式來(lái)實(shí)施可視化。這種方法的缺點(diǎn)在于,當(dāng)點(diǎn)的數(shù)量激增時(shí),如數(shù)百萬(wàn)個(gè)點(diǎn),可視化的效率會(huì)迅速降低,也阻礙進(jìn)一步的探索性分析。為了解決此問(wèn)題,本文提出了一種基于影像金字塔的可視化方法,并在此基礎(chǔ)上展開(kāi)探索性分析。 影像金字塔是指在同一空間參照下,由分辨率從高到低、數(shù)據(jù)量從大到小的圖片構(gòu)成的金字塔狀影像集,是一種適用于柵格圖像的分層數(shù)據(jù)結(jié)構(gòu)形式,可以滿足不同的顯示要求。我們提出的可視化方法簡(jiǎn)述如下：將矢量的軌跡點(diǎn)轉(zhuǎn)換成柵格數(shù)據(jù),平面上,為其建立格網(wǎng)索引,縱向上,為其構(gòu)建影像金字塔。為避開(kāi)硬件限制,防止圖片太大無(wú)法生成,以及提高存取效率,將金字塔每層圖片再分割成地圖瓦片。顯示時(shí),根據(jù)當(dāng)前顯示級(jí)別,將視圖范圍與金字塔的某層進(jìn)行求交運(yùn)算,相交部分的圖片或圖片的一部分畫(huà)到屏幕顯示。文中詳細(xì)介紹了矢量軌跡點(diǎn)到柵格數(shù)據(jù)的轉(zhuǎn)換,格網(wǎng)索引的建立,影像金字塔及地圖瓦片的構(gòu)建方法,以及可視化操作的實(shí)現(xiàn)步驟,并實(shí)現(xiàn)了一個(gè)案例應(yīng)用。以上海市某日出租車(chē)軌跡和深圳市多日出租車(chē)軌跡作為實(shí)驗(yàn)數(shù)據(jù),為分析不同參數(shù)對(duì)可視化效率的影響,共設(shè)計(jì)了四組對(duì)比實(shí)驗(yàn),通過(guò)運(yùn)行本文所提出的可視化方法,實(shí)現(xiàn)了軌跡的快速可視化,運(yùn)行結(jié)果顯示,本文提出的方法可以有效地解決海量車(chē)輛軌跡數(shù)據(jù)可視化效率低下的問(wèn)題。在實(shí)現(xiàn)可視化方法的基礎(chǔ)上開(kāi)展了初步的探索性分析,包括軌跡信息回放、行駛信息統(tǒng)計(jì)和駕駛行為分析等功能。行駛信息統(tǒng)計(jì)包括計(jì)算載客時(shí)間、空載時(shí)間和平均速度等；駕駛行為分析指計(jì)算出租車(chē)在載客狀態(tài)下的超速、急加速和急減速比率。
[Abstract]:With the rapid development of satellite technology, wireless communication technology and positioning technology, massive vehicle trajectory data can be obtained quickly and conveniently. These trajectory data have the characteristics of large quantity, wide coverage and high density. By analyzing and mining them, we can obtain the information about the moving process of the vehicle. In order to apply these massive trajectory data, it is necessary to make an exploratory analysis of them first, and the important foundation of this analysis is the visualization of the data. Vehicle trajectory data is composed of a large number of vector locus points in terms of its physical composition. The visualization mechanism used in existing commercial database management software is to create vector index to implement visualization. The disadvantage of this method is that when the number of points increases, millions of points, the efficiency of visualization will be reduced rapidly, and the further exploratory analysis will be hindered. In order to solve this problem, a visualization method based on image pyramid is proposed, and an exploratory analysis is carried out. Image pyramid is a kind of pyramid image set which is composed of images with high resolution from high to low and data from large to small under the same spatial reference. It is a kind of layered data structure suitable for raster images. Can meet different display requirements. The visualization method proposed by us is as follows: the vector locus points are converted into raster data, the grid index is built on the plane, and the image pyramid is constructed longitudinally. To avoid hardware constraints, prevent images from being too large to generate, and improve access efficiency, each layer of the pyramid is further divided into map tiles. Display, according to the current level of display, the scope of the view and a layer of the pyramid to calculate the intersection, the intersection part of the picture or part of the picture to the screen display. In this paper, the transformation of vector locus to raster data, the establishment of grid index, the construction of image pyramid and map tile, and the realization of visual operation are introduced in detail, and a case application is realized. In order to analyze the effect of different parameters on the visualization efficiency, four groups of comparative experiments were designed based on the experimental data of one day taxi track in Shanghai and the multi-day taxi track in Shenzhen City, and the visualization method proposed in this paper was run. The results show that the method proposed in this paper can effectively solve the problem of low visualization efficiency of massive vehicle trajectory data. Based on the visualization method, the preliminary exploratory analysis is carried out, including the functions of track information playback, driving information statistics and driving behavior analysis. The statistics of driving information include the calculation of passenger time, no-load time and average speed, and the analysis of driving behavior refers to the calculation of the ratio of speeding, rapid acceleration and rapid deceleration of taxis under the condition of carrying passengers.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：P208;U495

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 趙苗苗;基于出租車(chē)軌跡數(shù)據(jù)挖掘的推薦模型研究[D];首都經(jīng)濟(jì)貿(mào)易大學(xué);2015年

2 錢(qián)科宇;基于WebGIS的車(chē)輛監(jiān)控系統(tǒng)的性能優(yōu)化研究[D];南京郵電大學(xué);2015年

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本文編號(hào)：2170754