本文關(guān)鍵詞： 地圖匹配 智能交通 浮動(dòng)車 云平臺(tái)　出處：《浙江工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代智能交通系統(tǒng)(ITS)的快速發(fā)展,地理信息技術(shù)、衛(wèi)星定位技術(shù)和觀代通信技術(shù)在解決城市智能交通方面發(fā)揮了巨大的作用。浮動(dòng)車數(shù)據(jù)作為智能交通系統(tǒng)的重要組成部分,是一種新型的城市出行規(guī)劃方式和路況信息獲取方式。地圖匹配技術(shù)是浮動(dòng)車數(shù)據(jù)處理中最關(guān)鍵的內(nèi)容之一,只有判斷出車輛在哪條道路上行駛,才能將GPS數(shù)據(jù)轉(zhuǎn)化為有效的道路交通狀態(tài)信息。云計(jì)算是一種將計(jì)算過程分?jǐn)偟郊簷C(jī)器中,使得每臺(tái)機(jī)器同時(shí)運(yùn)算整個(gè)過程的不同部分,分擔(dān)的任務(wù)最終合并結(jié)果,從而快速、有效的得到最終結(jié)果。本文主要工作闡述如下:(1)在地圖匹配系統(tǒng)中,提出了一種新型的HashMap網(wǎng)格索引算法。該算法使時(shí)間復(fù)雜度降為O(1),解決了傳統(tǒng)四叉樹索引算法在空間對(duì)象分布不均勻時(shí)查詢效率急劇下降的問題,且通過二次網(wǎng)格劃分和一次中心區(qū)域劃分,使得匹配準(zhǔn)確度得到了較大地提升。(2)在地圖匹配系統(tǒng)中引入海拔高程信息,將地圖匹配算法拆分為高架/非高架匹配算法,待匹配點(diǎn)通過判斷所在網(wǎng)格緩沖區(qū)內(nèi)是否包含高架路段信息來選擇匹配算法,改進(jìn)了傳統(tǒng)算法在處理高架和地面道路重疊時(shí)的不足,從而進(jìn)一步提高了匹配準(zhǔn)確度。(3)針對(duì)大規(guī)模浮動(dòng)車數(shù)據(jù)在傳統(tǒng)單機(jī)計(jì)算模型中進(jìn)行地圖匹配存在耗時(shí)大的問題,本文基于Hadoop云平臺(tái),通過Map/Reduce編程模型,對(duì)大規(guī)模浮動(dòng)車數(shù)據(jù)進(jìn)行分布式并行計(jì)算,實(shí)現(xiàn)了對(duì)地圖匹配快速有效地處理。(4)通過對(duì)單車跟蹤匹配測(cè)試、對(duì)高架和地面道路重疊時(shí)匹配測(cè)試、對(duì)大規(guī)模浮動(dòng)車數(shù)據(jù)匹配測(cè)試,得出本文的算法在正確率和計(jì)算效率兩方面均有較好的表現(xiàn)。
[Abstract]:With the rapid development of modern intelligent transportation system (ITS), geographic information technology (GIS). Satellite positioning technology and generation communication technology have played a great role in solving urban intelligent transportation. Floating vehicle data is an important part of intelligent transportation system. Map matching technology is one of the most important contents in floating vehicle data processing, only to determine which road the vehicle is driving on. Cloud computing is a kind of distributed computing process to cluster machines, so that each machine at the same time calculate different parts of the whole process. The shared tasks are combined to get the final results quickly and effectively. The main work of this paper is as follows: 1) in the map matching system. A new HashMap grid indexing algorithm is proposed, which reduces the time complexity to OF-1). It solves the problem that the query efficiency of the traditional quadtree index algorithm drops sharply when the spatial object distribution is not uniform, and through the quadratic grid division and the primary center region division. So that the matching accuracy is greatly improved. 2) the elevation information is introduced into the map matching system, and the map matching algorithm is divided into elevated / non-elevated matching algorithm. By judging whether the grid buffer contains elevated section information, the matching algorithm is selected, which improves the shortcomings of the traditional algorithm in dealing with the overlap of elevated and ground roads. Therefore, the matching accuracy is improved further.) aiming at the problem of large scale floating vehicle data matching in traditional single machine computing model, this paper is based on Hadoop cloud platform. Through the Map/Reduce programming model, the data of large-scale floating vehicle is computed in distributed parallel, and the map matching is processed quickly and effectively. The matching test of overlay and ground roads and the matching test of large-scale floating vehicle data show that the algorithm presented in this paper has good performance in both accuracy and computational efficiency.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495

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本文編號(hào)：1443000