本文選題：云計(jì)算　切入點(diǎn)：GPS浮動(dòng)車　出處：《武漢理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：應(yīng)用云計(jì)算技術(shù)解決交通領(lǐng)域的問題是當(dāng)前技術(shù)研究的一個(gè)熱門趨勢。在交通領(lǐng)域中,由遍布城市道路各個(gè)角落的檢測器和浮動(dòng)車采集的數(shù)據(jù)是海量的,且具有覆蓋范圍廣、信息量豐富等特點(diǎn),非常適合于應(yīng)用大數(shù)據(jù)技術(shù)進(jìn)行挖掘和實(shí)時(shí)分析工作。在城市交通大數(shù)據(jù)技術(shù)中,利用浮動(dòng)車GPS數(shù)據(jù),進(jìn)行路段行程時(shí)間的實(shí)時(shí)計(jì)算和道路擁堵狀態(tài)的評價(jià),可以為基于實(shí)時(shí)路況的路徑規(guī)劃、行程時(shí)間預(yù)測、道路擁堵預(yù)測等研究提供數(shù)據(jù)基礎(chǔ),可將有助于交通監(jiān)管平臺和居民及時(shí)了解當(dāng)前道路交通參數(shù)狀態(tài),提前規(guī)劃出行時(shí)間和出行路徑。本文主要研究了應(yīng)用云計(jì)算技術(shù)處理海量浮動(dòng)車GPS數(shù)據(jù),計(jì)算城市路段行程時(shí)間和交通狀態(tài)評價(jià)的方法和實(shí)現(xiàn)流程。本文通過對GPS浮動(dòng)車地圖匹配技術(shù)的研究分析,提出了適用于海量數(shù)據(jù)計(jì)算的地圖匹配改進(jìn)方法和具體實(shí)現(xiàn)流程;通過對行程時(shí)間估算方法的研究,提出了一種改進(jìn)的少樣本估算方法,同時(shí)探討了在Storm云平臺的具體實(shí)現(xiàn)技術(shù);通過對交通擁堵評價(jià)的研究分析,給出了一種基于路段行程時(shí)間的簡易評價(jià)指標(biāo),同時(shí)研究了在云平臺下的實(shí)現(xiàn)方案。本文所完成的研究工作主要如下:(1)系統(tǒng)研究了GPS浮動(dòng)車技術(shù)中的基礎(chǔ)技術(shù)—地圖匹配技術(shù)的主要原理和方法,分析了當(dāng)前主要地圖匹配方法的優(yōu)點(diǎn)和不足,提出適用于實(shí)際大數(shù)據(jù)應(yīng)用的基于權(quán)重的改進(jìn)匹配方法。為解決大量數(shù)據(jù)實(shí)時(shí)處理中的匹配效率問題,提出GIS路段劃分方法、四網(wǎng)格匹配法、權(quán)重計(jì)算方法優(yōu)化和利用路網(wǎng)拓?fù)浣Y(jié)構(gòu)的待匹配路段篩選方法,同時(shí)給出了具體細(xì)節(jié)技術(shù)的實(shí)現(xiàn)流程。(2)研究分析了城市交通路段行程時(shí)間估算的主要方法,在考慮實(shí)際應(yīng)用中的情況并總結(jié)各方法優(yōu)劣后,提出綜合時(shí)間插值法和速度-時(shí)間積分法的改進(jìn)方法:基于運(yùn)行時(shí)刻和運(yùn)行速度的少樣本估算方法。同時(shí)在路段行程時(shí)間的估算的基礎(chǔ)上提出了一種新的簡易擁堵評價(jià)模型。(3)研究設(shè)計(jì)了路段行程時(shí)間實(shí)時(shí)估算以及道路擁堵評價(jià)方法在云平臺中具體算法流程和實(shí)現(xiàn)方案,同時(shí)應(yīng)用Storm云平臺進(jìn)行了實(shí)際的程序開發(fā)和應(yīng)用。
[Abstract]:The application of cloud computing technology to solve traffic problems is a hot trend in the field of transportation. In the field of transportation, the data collected by detectors and floating vehicles in every corner of urban roads are massive and have a wide range of coverage. Because of its rich information, it is very suitable for mining and real time analysis by using big data technology. In the urban traffic big data technology, the floating vehicle GPS data is used. The real-time calculation of road travel time and the evaluation of road congestion can provide the data basis for the research of path planning, travel time prediction and road congestion prediction based on real-time road conditions. It will be helpful for the traffic supervision platform and residents to understand the current state of road traffic parameters and plan the travel time and route ahead of time. This paper mainly studies the application of cloud computing technology to deal with massive floating vehicle GPS data. Based on the research and analysis of GPS floating vehicle map matching technology, the paper puts forward a map matching improvement method and concrete realization flow for mass data calculation. Based on the study of travel time estimation method, an improved estimation method with less samples is proposed, and the specific implementation technology in Storm cloud platform is discussed. A simple evaluation index based on route travel time is given. At the same time, the realization scheme under the cloud platform is studied. The main research work in this paper is as follows: 1) the main principle and method of map matching technology, which is the basic technology of GPS floating vehicle technology, is studied systematically. This paper analyzes the advantages and disadvantages of the current main map matching methods, and puts forward an improved matching method based on weight, which is suitable for the practical application of big data. In order to solve the problem of matching efficiency in the real-time processing of a large amount of data, the GIS section partition method is proposed. Four grid matching method, weight calculation method optimization and road network topology structure to be used to match the section selection method, at the same time, the specific details of the implementation flow. 2) the main methods of estimating the travel time of urban traffic section are studied and analyzed. After considering the practical application and summarizing the advantages and disadvantages of each method, An improved method of integrated time interpolation and velocity-time integration is proposed, which is based on running time and running speed. Based on the estimation of travel time, a new simple method is proposed. The real time estimation of road travel time and the specific algorithm flow and implementation scheme of road congestion evaluation method in cloud platform are studied and designed. At the same time, the actual program development and application are carried out by using Storm cloud platform.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495

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本文編號：1594315