【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)與移動(dòng)終端的迅猛發(fā)展與普及,智能手機(jī)價(jià)格的降低,促使智能手機(jī)在大部分地區(qū)及人群中普及,并成為人們生活中不可或缺的工具。通過(guò)移動(dòng)手機(jī)獲取GPS(Global Positioning System,全球定位系統(tǒng))軌跡數(shù)據(jù)變得越來(lái)越方便,規(guī)模也越來(lái)越龐大,因而出現(xiàn)了海量移動(dòng)對(duì)象軌跡數(shù)據(jù)。而這些軌跡數(shù)據(jù),產(chǎn)生了各種基于位置服務(wù)(Location Based Services,LBS)的應(yīng)用。因此,如何從海量的GPS軌跡數(shù)據(jù)中自動(dòng)提取、構(gòu)建路網(wǎng)并更新路網(wǎng)信息,已成為當(dāng)前研究的熱點(diǎn),該問(wèn)題的解決在改善城市交通環(huán)境、車(chē)輛導(dǎo)航、景區(qū)旅游和災(zāi)害救助等方面扮演著越來(lái)越重要的角色。采用計(jì)算機(jī)軟件、GIS(Geographic Information System或Geo-Information System,地理信息系統(tǒng))等相關(guān)技術(shù),考慮空間數(shù)據(jù)結(jié)構(gòu)中矢量數(shù)據(jù)結(jié)構(gòu)和柵格數(shù)據(jù)結(jié)構(gòu)的特點(diǎn),論文提出了一種眾源GPS軌跡歸并算法,該算法包括矢量數(shù)據(jù)柵格化、柵格數(shù)據(jù)二值化、柵格數(shù)據(jù)矢量化和軌跡中心線提取四個(gè)步驟。具體來(lái)講,首先基于ArcGIS Engine組件,并采用C#語(yǔ)言實(shí)現(xiàn)了軌跡歸并算法,然后在此基礎(chǔ)上進(jìn)行了4組實(shí)驗(yàn),并利用Google Earth和ArcGIS Desktop軟件對(duì)四組實(shí)驗(yàn)數(shù)據(jù)進(jìn)行了可視化分析。通過(guò)以上的分析,論文得出了以下幾點(diǎn)結(jié)論:(1)在軌跡數(shù)據(jù)預(yù)處理中,道格拉斯普克-線性簡(jiǎn)化(Douglas-Peucker,DP)算法對(duì)漂移點(diǎn)去除效果較好;具有噪音的基于密度的聚類(lèi)(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法對(duì)噪音點(diǎn)去除取得較好效果。(2)使用優(yōu)化的矢量數(shù)據(jù)柵格化方法,將矢量數(shù)據(jù)轉(zhuǎn)換成柵格數(shù)據(jù)可以取得較好效果;在輸出歸并后的軌跡中心線過(guò)程中,高斯濾波算法對(duì)軌跡進(jìn)行平滑處理可以獲取較好效果。(3)論文提出的眾源GPS軌跡歸并算法,能夠準(zhǔn)確提取軌跡路網(wǎng)并生成中心線。
[Abstract]:With the rapid development and popularization of mobile Internet and mobile terminals, the reduction of smartphone prices has promoted the popularity of smart phones in most areas and people, and has become an indispensable tool in people's lives. The acquisition of GPS (Global Positioning System, global positioning system (GPS (Global Positioning System,) trajectory data by mobile phone becomes more and more convenient, and the scale is becoming larger and larger, so a large number of mobile object trajectory data appear. These locus data, however, produce a variety of location-based service (Location Based Services,LBS) applications. Therefore, how to automatically extract, construct and update the road network information from massive GPS track data has become a hot topic of current research. The solution of this problem lies in improving the urban traffic environment and vehicle navigation. Scenic area tourism and disaster relief and other aspects play a more and more important role. By using computer software, GIS (Geographic Information System or Geo-Information System, Geographic Information system (GIS) and considering the characteristics of vector data structure and raster data structure in spatial data structure, a multisource GPS locus merging algorithm is proposed in this paper. The algorithm includes four steps: vector data rasterization, raster data binarization, raster data vectorization and locus centerline extraction. Specifically, the track merging algorithm is implemented based on ArcGIS Engine component and C # language, then four groups of experiments are carried out, and four groups of experimental data are visualized and analyzed by Google Earth and ArcGIS Desktop software. Through the above analysis, the paper draws the following conclusions: (1) in the trajectory data preprocessing, Douglas Puck linear simplification (Douglas-Peucker,DP) algorithm is better for the removal of drift points; The noisy density-based clustering (Density-Based Spatial Clustering of Applications with Noise,DBSCAN) algorithm has good effect on noise point removal. (2) an optimized vector data rasterization method is used. Good results can be obtained by converting vector data into raster data. In the process of output merging, Gao Si filter algorithm can get good results by smoothing the trajectory. (3) the multi-source GPS trajectory merging algorithm proposed in this paper can accurately extract the track network and generate the central line.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：P228.4

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本文編號(hào)：2406119