本文關(guān)鍵詞：融合復(fù)合特征的移動(dòng)軌跡預(yù)測(cè)方法的研究與實(shí)現(xiàn)　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 軌跡預(yù)測(cè) 模式挖掘 語義特征 概率路徑 局部匹配

【摘要】：隨著寬帶無線接入技術(shù)和移動(dòng)終端技術(shù)的飛速發(fā)展,人們迫切希望能夠隨時(shí)隨地在移動(dòng)過程中享受互聯(lián)網(wǎng)的信息和服務(wù),移動(dòng)互聯(lián)網(wǎng)應(yīng)運(yùn)而生并迅猛發(fā)展。各種基于位置服務(wù)的設(shè)備都要求提供移動(dòng)設(shè)備的準(zhǔn)確位置。目前,雖然定位系統(tǒng)的可靠性和準(zhǔn)確性有所提高,但是由于GPS系統(tǒng)、移動(dòng)設(shè)備、無線網(wǎng)絡(luò)的局限性,定位系統(tǒng)有時(shí)難以精確追蹤物體,需要可靠的方法預(yù)測(cè)移動(dòng)對(duì)象的將來位置。本文提出了兩種移動(dòng)對(duì)象軌跡預(yù)測(cè)的方法:融合語義特征的移動(dòng)對(duì)象軌跡預(yù)測(cè)方法SG和融合動(dòng)態(tài)環(huán)境的移動(dòng)對(duì)象概率路徑預(yù)測(cè)方法P3D。融合語義特征的移動(dòng)用戶軌跡預(yù)測(cè)方法SG首先將用戶的地理軌跡轉(zhuǎn)化成包含語義行為的軌跡,挖掘出語義模式集,同時(shí)在語義軌跡中分析移動(dòng)用戶的公共行為,將具有相似語義行為的用戶進(jìn)行聚類,挖掘出每個(gè)聚類的地理模式集。基于挖掘到的用戶個(gè)體語義模式集和相似用戶地理模式集,構(gòu)造用來索引和局部匹配的模式樹STP-Tree和SLP-Tree。通過對(duì)STP-Tree和SLP-Tree的索引和局部匹配,引入一個(gè)加權(quán)函數(shù)對(duì)給定移動(dòng)用戶的最近運(yùn)動(dòng)進(jìn)行預(yù)測(cè)。融合動(dòng)態(tài)環(huán)境的移動(dòng)對(duì)象概率路徑預(yù)測(cè)方法P3D通過動(dòng)態(tài)選擇軌跡來動(dòng)態(tài)建立預(yù)測(cè)模型。P3D的優(yōu)點(diǎn):1)要被預(yù)測(cè)的目標(biāo)軌跡在模型建立之前被選擇,可以針對(duì)和目標(biāo)軌跡相關(guān)的軌跡建立模型。2)不像惰性學(xué)習(xí)方法,P3D基于少量選定的參考軌跡,用精確的學(xué)習(xí)方法在可接受的時(shí)延內(nèi)得到準(zhǔn)確的預(yù)測(cè)模型。3)如果預(yù)測(cè)運(yùn)動(dòng)和準(zhǔn)確的結(jié)果不匹配,P3D可以通過動(dòng)態(tài)重構(gòu)建新模型進(jìn)行持續(xù)自校正。在大量真實(shí)和人工軌跡數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明:本文SG方法的準(zhǔn)確性和性能較傳統(tǒng)方法都有顯著提高。P3D通過自校正持續(xù)預(yù)測(cè),可以動(dòng)態(tài)調(diào)整預(yù)測(cè)結(jié)果,相比沒有自校正持續(xù)預(yù)測(cè)方法,性能有了明顯的提升。
[Abstract]:With the rapid development of broadband wireless access technology and mobile terminal technology, people are eager to enjoy the information and services of Internet anytime and anywhere. Mobile Internet arises at the historic moment and develops rapidly. All kinds of location-based devices are required to provide the exact location of mobile devices. At present, the reliability and accuracy of positioning system have been improved. However, due to the limitations of GPS systems, mobile devices and wireless networks, positioning systems sometimes find it difficult to track objects accurately. A reliable method is needed to predict the future position of moving objects. In this paper, two methods of trajectory prediction for moving objects are proposed:. The mobile object trajectory prediction method SG which fuses semantic features and the mobile object probabilistic path prediction method P3D. the mobile user trajectory prediction method based on semantic feature is firstly applied to user's geography by combining semantic features with SG and moving object probabilistic path prediction method based on dynamic environment. The locus is transformed into a locus containing semantic behavior. At the same time, the common behaviors of mobile users are analyzed in the semantic locus, and the users with similar semantic behaviors are clustered. Mining the geographical pattern set of each cluster. Based on the user individual semantic pattern set and similar user geographical pattern set. Construct pattern trees STP-Tree and SLP-Tree. for indexing and local matching by indexing and local matching of STP-Tree and SLP-Tree. A weighted function is introduced to predict the recent movement of a given mobile user. A method for predicting the probabilistic path of moving objects in a dynamic environment P3D dynamically establishes the prediction model by dynamically selecting the trajectory. P3D. Point:. 1). The target trajectory to be predicted is selected before the model is built. A model. 2) can be built for the trajectory associated with the target trajectory.) unlike the lazy learning method, P3D is based on a small number of selected reference trajectories. An accurate prediction model. 3 is obtained by using an accurate learning method within an acceptable time delay) if the predicted motion and the exact result do not match. P3D can be continuously self-corrected by dynamic reconstruction of new models. Experimental results on a large number of real and artificial trajectory data sets show that:. Compared with traditional methods, the accuracy and performance of SG method in this paper are significantly improved. P3D through self-tuning continuous prediction. The prediction results can be adjusted dynamically. Compared with no self-tuning continuous prediction method, the performance has been improved significantly.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN925.93

【共引文獻(xiàn)】

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1 胡臻龍;;基于數(shù)據(jù)挖掘的高效取樣方法對(duì)手機(jī)用戶的周期運(yùn)動(dòng)模式的研究[J];科技通報(bào);2013年11期

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本文編號(hào)：1404731