天堂国产午夜亚洲专区-少妇人妻综合久久蜜臀-国产成人户外露出视频在线-国产91传媒一区二区三区

當前位置:主頁 > 科技論文 > 測繪論文 >

GPS軌跡信息的語義挖掘

發(fā)布時間:2018-03-03 22:35

  本文選題:出行調查 切入點:GPS軌跡數(shù)據 出處:《山東理工大學》2013年碩士論文 論文類型:學位論文


【摘要】:近年來城市化進程加劇,在較短時間內城市人口急劇增長,這考驗著城市各方面的承載能力,尤其對城市交通提出了更高的要求。交通調查是交通理論研究和技術創(chuàng)新的基石,其中居民出行信息更是重要的調查內容。目前廣泛應用的居民出行調查法存在周期長、成本高、數(shù)據質量不高等問題,已逐漸不能滿足大規(guī)模、高頻率的居民出行調查的要求。 伴隨無線通信網絡和全球定位系統(tǒng)(GPS)技術的迅猛發(fā)展,海量GPS數(shù)據的收集和傳輸成為可能,基于GPS的出行調查法應運而生。該方法是指給受訪者配備一個GPS接收器,采集其出行的軌跡數(shù)據,通過數(shù)據挖掘及語義挖掘技術,智能化的提取數(shù)據中所隱含的居民出行信息。本文對基于GPS的出行調查法的研究,圍繞從無直接意義的數(shù)據中智能提取出行信息。主要提取行程、出行方式和出行目的三類信息,具體如下: (1)行程識別 行程識別是出行信息提取的首要步驟。文中通過基于密度的軌跡點聚類獲取軌跡的低速區(qū)域,也就是受訪者可能的停留位置;將低速區(qū)域匹配到GIS地理信息系統(tǒng)上,進一步判斷低速區(qū)域是否為停留。辨識出軌跡中的停留,即找到了行程端點,也就完成了行程識別的過程。 (2)基于模糊模式識別的出行方式判別 出行方式是出行信息提取的重點。文中針對出行方式模糊性的特點,提出使用模糊模式識別進行出行方式判別。利用主成分分析法確定出特征變量,用以表征行程段出行方式信息;對應步行、自行車和機動車這三種出行方式分別建立隸屬函數(shù),用matlab實現(xiàn)模糊模式識別模型的構建,使用模型進行出行方式判別。 (3)基于多級空間尺度的出行目的推斷 出行目的是出行信息提取的難點。文中利用地理學中多級空間尺度理論,在不同級空間中分析GPS軌跡。著重剖析軌跡的微觀活動,從軌跡停留中進一步辨識子停留。挖掘子停留的語義信息,用軌跡點特征參數(shù)(時長、速度、轉角)對信息進行量化。在大量數(shù)據統(tǒng)計結果基礎上構建判別信息庫,將子停留信息與判別信息庫中閥值進行比對,得知子停留活動類型,繼而獲知出行者的出行目的。
[Abstract]:In recent years, the urbanization process has intensified and the urban population has increased rapidly in a relatively short period of time, which tests the carrying capacity of various aspects of the city, especially puts forward higher requirements for urban traffic. Traffic survey is the cornerstone of traffic theory research and technological innovation. Among them, the resident travel information is an important investigation content. At present, the widely used resident travel survey method has many problems, such as long period, high cost, low data quality and so on, which can not meet the requirements of large-scale and high-frequency residents' travel survey. With the rapid development of wireless communication network and GPS (Global Positioning system) technology, it is possible to collect and transmit huge amounts of GPS data, and the GPS based travel survey method comes into being, which means that the interviewees are equipped with a GPS receiver. Through data mining and semantic mining technology, we can intelligently extract the resident travel information implied in the data. In this paper, we study the trip survey method based on GPS. The travel information is extracted intelligently from the data without direct meaning. There are three kinds of information: itinerary, travel mode and travel purpose. The details are as follows:. Stroke identification. Travel identification is the first step to extract travel information. In this paper, the low speed region of trajectory is obtained by density-based locus clustering, that is, the possible stay position of interviewee; the low speed region is matched to GIS GIS. Furthermore, it is determined whether the low speed region is a stopover. The identification of the stopover in the trajectory, that is to say, finding the end point of the stroke, will also complete the process of the travel identification. Identification of trip modes based on Fuzzy pattern recognition. Trip mode is the key point of trip information extraction. In view of the fuzziness of trip mode, fuzzy pattern recognition is used to distinguish trip mode, and principal component analysis is used to determine the characteristic variable. It is used to represent travel mode information of travel segment. Membership function is established for three travel modes namely walking bicycle and motor vehicle. Fuzzy pattern recognition model is constructed by matlab and trip mode identification is carried out by using the model. Travel destination inference based on multilevel spatial scale. The purpose of travel is difficult to extract travel information. In this paper, we use the theory of multilevel spatial scale in geography to analyze the GPS locus in different levels of space. The information is quantified by the characteristic parameters of the locus points (time, speed, angle), and the discriminant information base is constructed on the basis of a large number of statistical results. The sub-stay information is compared with the threshold value in the discriminant information base, and the type of sub-stay activity is obtained, and then the travel purpose of the traveller is obtained.
【學位授予單位】:山東理工大學
【學位級別】:碩士
【學位授予年份】:2013
【分類號】:P228.4;U495

【參考文獻】

相關期刊論文 前4條

1 桂智明;李玉擰;陳彩;;多級空間尺度下的軌跡知識發(fā)現(xiàn)[J];北京工業(yè)大學學報;2011年10期

2 譚建軍;陳少沛;李英遠;;城市交通GIS數(shù)據建模中的語義關系研究[J];測繪科學;2009年S2期

3 李偉芬;丁靜;苗卿;;空間數(shù)據多尺度研究綜述[J];電腦知識與技術(學術交流);2007年13期

4 孫美玲,李永樹;GIS環(huán)境下空間數(shù)據多尺度特征及其關鍵問題探討[J];四川測繪;2002年04期

相關博士學位論文 前1條

1 鄧中偉;面向交通服務的多源移動軌跡數(shù)據挖掘與多尺度居民活動的知識發(fā)現(xiàn)[D];華東師范大學;2012年



本文編號:1563018

資料下載
論文發(fā)表

本文鏈接:http://sikaile.net/kejilunwen/dizhicehuilunwen/1563018.html


Copyright(c)文論論文網All Rights Reserved | 網站地圖 |

版權申明:資料由用戶64cd7***提供,本站僅收錄摘要或目錄,作者需要刪除請E-mail郵箱bigeng88@qq.com