發(fā)布時(shí)間：2018-11-05 07:45

【摘要】：本文為了進(jìn)一步滿足處理海量GPS數(shù)據(jù)的精度與速率的要求,主要完成了地圖匹配算法在精度上的改進(jìn)和改進(jìn)后地圖匹配算法在MapReduce并行計(jì)算框架上的并行化設(shè)計(jì)與實(shí)現(xiàn)。針對(duì)海量GPS數(shù)據(jù)分析的精度問(wèn)題,主要是分析一些現(xiàn)有地圖匹配算法在現(xiàn)實(shí)生活中運(yùn)用場(chǎng)景的優(yōu)缺點(diǎn),結(jié)合部分地圖匹配算法的優(yōu)點(diǎn)與本人的一些研究成果,提出一種結(jié)合部分交通規(guī)則的地圖匹配算法。該算法主要借用隱馬爾可夫模型(Hidden Markov Model,HMM)對(duì)地圖匹配過(guò)程進(jìn)行建模,充分考慮了前后GPS信息和電子地圖中的路網(wǎng)拓?fù)潢P(guān)系,提高了地圖匹配算法的精度,以進(jìn)一步滿足海量GPS數(shù)據(jù)的挖掘需求。本文完成了包括噪聲數(shù)據(jù)過(guò)濾、冗余數(shù)據(jù)去除、缺失數(shù)據(jù)補(bǔ)充和漂移數(shù)據(jù)修正的GPS數(shù)據(jù)預(yù)處理過(guò)程,改進(jìn)的地圖匹配算法的設(shè)計(jì)與實(shí)現(xiàn)以及與之對(duì)比的一種拓?fù)湫畔⒌牡貓D匹配算法的設(shè)計(jì)與實(shí)現(xiàn)等工作,通過(guò)與基于拓?fù)湫畔⒌牡貓D匹配算法在精度和處理速度上進(jìn)行對(duì)比,得出在處理文章中所給的采樣頻率的GPS數(shù)據(jù)上,改進(jìn)后的地圖匹配算法匹配結(jié)果的精度更高。針對(duì)海量GPS數(shù)據(jù)處理速度的問(wèn)題,本文主要對(duì)地圖匹配算法的并行化進(jìn)行了研究。該問(wèn)題的研究意義在于更快的處理大量時(shí)空數(shù)據(jù)的地圖匹配問(wèn)題,提高處理的速率,節(jié)約時(shí)間成本。本文完成了包括噪聲數(shù)據(jù)去除并行化、冗余數(shù)據(jù)去除并行化、缺失數(shù)據(jù)補(bǔ)充并行化和漂移數(shù)據(jù)修正并行化的GPS數(shù)據(jù)預(yù)處理過(guò)程的并行化,改進(jìn)的地圖匹配算法并行化的設(shè)計(jì)與實(shí)現(xiàn)以及與之對(duì)比的改進(jìn)的地圖匹配算法單機(jī)版本的設(shè)計(jì)與實(shí)現(xiàn)等工作,通過(guò)對(duì)比地圖匹配算法的單機(jī)版本和并行化版本的結(jié)果和執(zhí)行時(shí)間,得出了基于MapReduce計(jì)算框架設(shè)計(jì)的地圖匹配算法設(shè)計(jì)的正確性,同時(shí)也得出了該并行版本所耗時(shí)間最短,而且在數(shù)據(jù)量逐漸增大時(shí),這種實(shí)現(xiàn)方法相比其他兩種在速率上的優(yōu)勢(shì)更大,進(jìn)而得出這種并行化算法在處理大量數(shù)據(jù)的優(yōu)越性。
[Abstract]:In order to meet the requirements of accuracy and speed of processing massive GPS data, this paper mainly completes the design and implementation of the improved map matching algorithm in the framework of MapReduce parallel computing. Aiming at the accuracy of massive GPS data analysis, this paper mainly analyzes the advantages and disadvantages of some existing map matching algorithms in real life, and combines the advantages of some map matching algorithms with some of my research results. A map matching algorithm combining partial traffic rules is proposed. The algorithm mainly uses hidden Markov model (Hidden Markov Model,HMM) to model the map matching process, fully considers the GPS information and the network topology relationship in the electronic map, and improves the accuracy of the map matching algorithm. In order to further meet the massive GPS data mining needs. In this paper, the GPS data preprocessing process including noise data filtering, redundant data removal, missing data supplement and drift data correction is completed. The design and implementation of the improved map matching algorithm and the design and implementation of a map matching algorithm based on topological information are compared with the map matching algorithm based on topology information in precision and processing speed. It is concluded that the improved map matching algorithm is more accurate in processing the GPS data of sampling frequency given in the paper. Aiming at the problem of processing speed of massive GPS data, this paper mainly studies the parallelization of map matching algorithm. The research significance of this problem is to deal with the map matching problem of a large amount of space-time data more quickly, to improve the processing rate and to save time cost. This paper completes the parallelization of GPS data preprocessing, which includes noise data removal parallelization, redundant data removal parallelization, missing data supplement parallelization and drift data correction parallelization. The design and implementation of the parallelization of the improved map matching algorithm and the design and implementation of the single version of the improved map matching algorithm are also presented. By comparing the results and execution times of the single and parallel versions of the map matching algorithm, the correctness of the map matching algorithm design based on the MapReduce computing framework is obtained, and the shortest time consumed by the parallel version is also obtained. Moreover, when the amount of data increases gradually, this method has more advantages than the other two methods in the speed, and then obtains the superiority of this parallel algorithm in dealing with a large number of data.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：P228.4;TP311.13

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相關(guān)期刊論文 前6條

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本文編號(hào)：2311379