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

【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。云計算是一種將計算過程分攤到集群機器中,使得每臺機器同時運算整個過程的不同部分,分擔的任務(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。該算法使時間復雜度降為O(1),解決了傳統(tǒng)四叉樹索引算法在空間對象分布不均勻時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,改進了傳統(tǒng)算法在處理高架和地面道路重疊時的不足,從而進一步提高了匹配準確度。(3)針對大規(guī)模浮動車數(shù)據(jù)在傳統(tǒng)單機計算模型中進行地圖匹配存在耗時大的問題,本文基于Hadoop云平臺,通過Map/Reduce編程模型,對大規(guī)模浮動車數(shù)據(jù)進行分布式并行計算,實現(xiàn)了對地圖匹配快速有效地處理。(4)通過對單車跟蹤匹配測試、對高架和地面道路重疊時匹配測試、對大規(guī)模浮動車數(shù)據(jù)匹配測試,得出本文的算法在正確率和計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.
【學位授予單位】：浙江工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495

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本文編號：1443000