本文選題：基于位置服務(wù) + 移動(dòng)定位技術(shù)�。� 參考：《東北大學(xué)》2014年碩士論文

【摘要】：近年來(lái),隨著移動(dòng)定位技術(shù)的成熟和流行,基于位置服務(wù)越來(lái)越受到人們的關(guān)注。位置預(yù)測(cè)技術(shù)是其中重要部分,并有著廣泛的應(yīng)用。目前,位置預(yù)測(cè)方法中較為常用的是基于Markov模型的預(yù)測(cè)方法,然而這類方法存在很多問(wèn)題,例如：沒(méi)有有效的劃分區(qū)域、沒(méi)有考慮用戶特性以及只基于當(dāng)前位置進(jìn)行預(yù)測(cè)。因此,基于Markov模型的新的位置預(yù)測(cè)方法的研究迫在眉睫。本文以傳統(tǒng)的基于Markov模型的位置預(yù)測(cè)理論為基礎(chǔ),并針對(duì)其中存在的問(wèn)題加以改進(jìn)。建立了包括離線數(shù)據(jù)處理、線下模型訓(xùn)練和線上位置預(yù)測(cè)的完整位置預(yù)測(cè)方案,使得預(yù)測(cè)準(zhǔn)確率更高,系統(tǒng)適用范圍更廣。本文的主要貢獻(xiàn)如下：首先,針對(duì)GPS數(shù)據(jù)遠(yuǎn)比其他數(shù)據(jù)更容易獲取的特點(diǎn),提出只基于用戶歷史GPS數(shù)據(jù)進(jìn)行位置預(yù)測(cè)的方案,使得位置預(yù)測(cè)方法更符合實(shí)際、應(yīng)用范圍更廣其次,針對(duì)傳統(tǒng)位置預(yù)測(cè)中將地圖網(wǎng)格化的方法所存在的問(wèn)題,提出一種新的劃分方案,從GPS數(shù)據(jù)中提取興趣點(diǎn),并依據(jù)興趣點(diǎn)將地圖進(jìn)行更有意義的劃分。再次,針對(duì)傳統(tǒng)Markov模型預(yù)測(cè)方法沒(méi)有考慮用戶特性的問(wèn)題,提出一種聚類算法,將用戶聚類為用戶組并為每個(gè)用戶組建立預(yù)測(cè)模型,實(shí)驗(yàn)證明可以有效的提高預(yù)測(cè)的準(zhǔn)確率。最后,針對(duì)傳統(tǒng)基于Markov模型的位置預(yù)測(cè)方法存在的問(wèn)題,提出了建立混合多步Markov模型的方法,考慮了軌跡上的多個(gè)位置,并給出了每個(gè)位置對(duì)預(yù)測(cè)的影響系數(shù)。另外,因?yàn)橛脩裘看蔚囊苿?dòng)行為并不一定完全符合用戶的習(xí)慣,所以提出一種貝葉斯方法,可以僅根據(jù)當(dāng)前軌跡選擇最符合該軌跡的模型進(jìn)行預(yù)測(cè)。同時(shí),解決了難以為新加入系統(tǒng)的用戶和數(shù)據(jù)稀疏的用戶進(jìn)行預(yù)測(cè)的問(wèn)題。通過(guò)理論分析和實(shí)驗(yàn)評(píng)估,證明了本文提出的基于混合多步Markov模型位置預(yù)測(cè)方法符合理論上的可行性和操作上的正確性。
[Abstract]:In recent years, with the maturity and popularity of mobile location technology, location-based services have attracted more and more attention. Position prediction technology is an important part of it, and has a wide range of applications. At present, the prediction method based on Markov model is commonly used in the location prediction method. However, there are many problems in this method, such as no effective division of regions, no consideration of user characteristics and only prediction based on the current location. Therefore, it is urgent to study a new position prediction method based on Markov model. This paper is based on the traditional position prediction theory based on Markov model and improves the existing problems. A complete position prediction scheme including off-line data processing, offline model training and on-line position prediction is established, which makes the prediction accuracy higher and the system more applicable. The main contributions of this paper are as follows: firstly, in view of the fact that GPS data are far easier to obtain than other data, a position prediction scheme based on user history GPS data is proposed, which makes the location prediction method more practical. Secondly, aiming at the problems existing in the traditional method of gridding map in position prediction, a new partition scheme is proposed to extract the interest points from GPS data, and to divide the map more meaningfully according to the points of interest. Thirdly, aiming at the problem that the traditional Markov model prediction method does not consider the characteristics of users, a clustering algorithm is proposed, which can cluster users into user groups and establish prediction models for each user group. Experiments show that the prediction accuracy can be improved effectively. Finally, aiming at the problems of the traditional Markov model-based location prediction method, a hybrid multistep Markov model is proposed, which considers multiple locations on the trajectory and gives the influence coefficients of each location on the prediction. In addition, because the user's mobile behavior does not always conform to the user's habits, a Bayesian method is proposed, which can only be predicted by selecting the most suitable model according to the current trajectory. At the same time, it solves the problem that it is difficult to predict the new users and the users with sparse data. Through theoretical analysis and experimental evaluation, it is proved that the proposed position prediction method based on mixed multistep Markov model is feasible in theory and correct in operation.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.5

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