發(fā)布時(shí)間：2018-01-26 03:51

  本文關(guān)鍵詞： 可視分析 稀疏交通軌跡 套牌車識別 用戶行為模式 張量分解　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著城市的快速發(fā)展,城市生活更加多元化和復(fù)雜化,人們豐富的出行活動帶來了大量的移動軌跡數(shù)據(jù)。軌跡數(shù)據(jù)包含時(shí)空和語義信息,有助于城市規(guī)劃和人們行為活動的理解,已成為一大研究熱點(diǎn)。本文采用開車數(shù)據(jù)和公共交通數(shù)據(jù)對用戶出行模式進(jìn)行了的相關(guān)可視分析,實(shí)現(xiàn)了相應(yīng)的可視分析技術(shù)。本文以稀疏交通卡口數(shù)據(jù)研究開車出行行為模式。系統(tǒng)實(shí)現(xiàn)了交通卡口宏觀流量探索;基于套牌車識別算法設(shè)計(jì)了可視查詢模型;通過空間氣泡圖、CirFlow圖、往返時(shí)間分布圖等分析可視查詢結(jié)果在全局、單卡口、卡口對上的時(shí)空分布。通過案例分析了交通卡口流量分布、套牌車行為的識別過程等,驗(yàn)證了本文方法能夠有效分析卡口通行數(shù)據(jù)。本文基于公共自行車租賃數(shù)據(jù)研究公共交通出行模式。本文建立了時(shí)間-空間-用戶屬性三維張量模型,采用非負(fù)張量分解算法將張量分解為多個(gè)基本模式。在時(shí)間、空間和用戶屬性維度上,分別采用時(shí)間曲線圖、熱力圖和年齡儀表圖進(jìn)行可視編碼和展示,支持用戶交互式探索不同模式間的時(shí)間、空間、用戶年齡的分布情況。最后,利用紐約和芝加哥兩個(gè)城市的公共自行車租賃數(shù)據(jù)案例,驗(yàn)證了用戶行為模式分析的有效性。
[Abstract]:With the rapid development of cities, urban life becomes more diversified and complicated. People's abundant travel activities bring a lot of moving trajectory data, which contain space-time and semantic information. It is helpful to understand urban planning and people's behavior, and has become a hot research topic. In this paper, we use driving data and public transportation data to analyze the user travel patterns visually. The corresponding visual analysis technology is realized. In this paper, the driving behavior pattern is studied with sparse traffic bayonet data. The macroscopic flow exploration of traffic bayonet is realized systematically. The visual query model is designed based on the identification algorithm. The spatial bubble chart CirFlow chart and round-trip time distribution map are used to analyze the spatial and temporal distribution of the visual query results in the global single bayonet and the upper side of the bayonet. The traffic flow distribution of the bayonet is analyzed by a case study. The process of identifying the behavior of a licensed car, etc. It is verified that this method can effectively analyze the traffic data of the bayonet. Based on the public bicycle rental data, this paper studies the public transport travel mode. In this paper, a three-dimensional Zhang Liang model of time-space-user attributes is established. The non-negative Zhang Liang decomposition algorithm is used to decompose Zhang Liang into several basic patterns. In the dimension of time, space and user attributes, time curve diagram, thermal diagram and age meter chart are used to encode and display them visually. Support users to interactively explore the distribution of time, space, and user age between different models. Finally, use the public bicycle rental data from New York and Chicago. The validity of user behavior pattern analysis is verified.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52;U491

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 高強(qiáng);張鳳荔;王瑞錦;周帆;;軌跡大數(shù)據(jù):數(shù)據(jù)處理關(guān)鍵技術(shù)研究綜述[J];軟件學(xué)報(bào);2017年04期

2 王祖超;袁曉如;;軌跡數(shù)據(jù)可視分析研究[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2015年01期

3 王濤;王順;沈益民;;交通流大數(shù)據(jù)中的套牌車并行檢測算法[J];湖北工程學(xué)院學(xué)報(bào);2014年06期

4 唐曉東;;套牌機(jī)動車輛檢測方法分析[J];中國人民公安大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年02期

5 蒲劍蘇;屈華民;倪明選;;移動軌跡數(shù)據(jù)的可視化[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2012年10期

，

本文編號：1464593