本文關(guān)鍵詞：城市交通軌跡大數(shù)據(jù)的語(yǔ)義分析和可視化　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 交通軌跡大數(shù)據(jù) Hive數(shù)據(jù)倉(cāng)庫(kù) 壓縮 詞向量 可視化

【摘要】：近年來,隨著"數(shù)字城市","智慧城市"等概念的興起,基于GPS定位采樣的交通軌跡數(shù)據(jù)研究正成為越來越熱門的課題。交通軌跡數(shù)據(jù)具有很強(qiáng)的時(shí)空屬性,同時(shí)兼有大數(shù)據(jù)的特征。對(duì)軌跡數(shù)據(jù)的高效組織和合理展示是城市交通研究課題中的一大難點(diǎn)。本文開發(fā)了一套架構(gòu)于Hadoop生態(tài)之上的分布式文件系統(tǒng),用來存儲(chǔ)海量交通數(shù)據(jù)。并使用Hive數(shù)據(jù)倉(cāng)庫(kù)管理海量交通軌跡數(shù)據(jù)。針對(duì)交通軌跡數(shù)據(jù)規(guī)模大的特點(diǎn),本文采用了視圖,索引等技術(shù),優(yōu)化數(shù)據(jù)庫(kù)檢索語(yǔ)句,降低檢索開銷。同時(shí),按照"天"和"小時(shí)"這兩個(gè)時(shí)間節(jié)點(diǎn)對(duì)交通軌跡數(shù)據(jù)分區(qū),優(yōu)化存儲(chǔ)結(jié)構(gòu),減少全盤掃描操作的頻率。為了減小海量數(shù)據(jù)的存儲(chǔ)規(guī)模,本文還嘗試了基于軌跡數(shù)據(jù)的壓縮技術(shù)。此外,本文另辟蹊徑,提出了一種基于語(yǔ)義分析處理軌跡數(shù)據(jù)信息的方法,通過對(duì)城市熱點(diǎn)區(qū)塊間聯(lián)系的研究,分析交通軌跡數(shù)據(jù)背后隱含的語(yǔ)義信息。該方法基于詞向量模型,由深度學(xué)習(xí)框架TensorFlow編程實(shí)現(xiàn)。在可視化環(huán)節(jié),通過研究城市熱點(diǎn)區(qū)塊的交通流量情況,分析整個(gè)城市的交通模式,并根據(jù)車輛細(xì)軌跡數(shù)據(jù)信息,對(duì)城市交通管理提出一些建議。最后,根據(jù)本文研究?jī)?nèi)容,實(shí)現(xiàn)了 B/S架構(gòu)的交通軌跡語(yǔ)義分析和可視化系統(tǒng)原型。該原型系統(tǒng)集成了從軌跡數(shù)據(jù)的采集、預(yù)處理、軌跡數(shù)據(jù)分析、可視化展示等一系列操作的自動(dòng)化批處理流程。
[Abstract]:In recent years, with the emergence of "digital city", "intelligent city" and other concepts, the research of traffic trajectory data based on GPS positioning sampling is becoming more and more popular. The traffic trajectory data has a strong spatial and temporal attribute, and also has the characteristics of large data. The efficient organization and rational display of track data is a difficult problem in urban traffic research. In this paper, a distributed file system based on Hadoop ecology is developed to store mass traffic data. And use Hive data warehouse to manage mass traffic trajectory data. In view of the large scale of traffic trajectory data, this paper adopts the technology of view and index to optimize the database retrieval sentence and reduce the retrieval cost. At the same time, according to the two time nodes of "day" and "hour", the storage structure is optimized to reduce the frequency of full scanning operation. In order to reduce the storage size of massive data, this paper also attempts a compression technology based on trajectory data. In addition, this paper proposes a new way to process trajectory data based on semantic analysis. By analyzing the links between urban hot spots, we can analyze the implicit semantic information behind traffic trajectory data. The method is based on the word vector model and is programmed by the depth learning framework (TensorFlow). In the visualization link, through studying the traffic flow situation of the urban hot spots, the traffic mode of the whole city is analyzed, and some suggestions for urban traffic management are put forward according to the information of vehicle fine track. Finally, according to the research content of this paper, the traffic trajectory semantic analysis and visual system prototype of B/S architecture are realized. The prototype system integrates a series of automated batch processing processes, such as track data collection, preprocessing, trajectory data analysis, visual display and a series of operations.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U12;TP311.13

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本文編號(hào)：1341242