本文選題：出行調(diào)查　切入點(diǎn)：GPS軌跡數(shù)據(jù)　出處：《山東理工大學(xué)》2013年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：近年來(lái)城市化進(jìn)程加劇,在較短時(shí)間內(nèi)城市人口急劇增長(zhǎng),這考驗(yàn)著城市各方面的承載能力,尤其對(duì)城市交通提出了更高的要求。交通調(diào)查是交通理論研究和技術(shù)創(chuàng)新的基石,其中居民出行信息更是重要的調(diào)查內(nèi)容。目前廣泛應(yīng)用的居民出行調(diào)查法存在周期長(zhǎng)、成本高、數(shù)據(jù)質(zhì)量不高等問(wèn)題,已逐漸不能滿足大規(guī)模、高頻率的居民出行調(diào)查的要求。 伴隨無(wú)線通信網(wǎng)絡(luò)和全球定位系統(tǒng)(GPS)技術(shù)的迅猛發(fā)展,海量GPS數(shù)據(jù)的收集和傳輸成為可能,基于GPS的出行調(diào)查法應(yīng)運(yùn)而生。該方法是指給受訪者配備一個(gè)GPS接收器,采集其出行的軌跡數(shù)據(jù),通過(guò)數(shù)據(jù)挖掘及語(yǔ)義挖掘技術(shù),智能化的提取數(shù)據(jù)中所隱含的居民出行信息。本文對(duì)基于GPS的出行調(diào)查法的研究,圍繞從無(wú)直接意義的數(shù)據(jù)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(lèi)信息,具體如下： (1)行程識(shí)別 行程識(shí)別是出行信息提取的首要步驟。文中通過(guò)基于密度的軌跡點(diǎn)聚類(lèi)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(jìn)一步判斷低速區(qū)域是否為停留。辨識(shí)出軌跡中的停留,即找到了行程端點(diǎn),也就完成了行程識(shí)別的過(guò)程。 (2)基于模糊模式識(shí)別的出行方式判別 出行方式是出行信息提取的重點(diǎn)。文中針對(duì)出行方式模糊性的特點(diǎn),提出使用模糊模式識(shí)別進(jìn)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(duì)應(yīng)步行、自行車(chē)和機(jī)動(dòng)車(chē)這三種出行方式分別建立隸屬函數(shù),用matlab實(shí)現(xiàn)模糊模式識(shí)別模型的構(gòu)建,使用模型進(jìn)行出行方式判別。 (3)基于多級(jí)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(diǎn)。文中利用地理學(xué)中多級(jí)空間尺度理論,在不同級(jí)空間中分析GPS軌跡。著重剖析軌跡的微觀活動(dòng),從軌跡停留中進(jìn)一步辨識(shí)子停留。挖掘子停留的語(yǔ)義信息,用軌跡點(diǎn)特征參數(shù)(時(shí)長(zhǎng)、速度、轉(zhuǎn)角)對(duì)信息進(jìn)行量化。在大量數(shù)據(jù)統(tǒng)計(jì)結(jié)果基礎(chǔ)上構(gòu)建判別信息庫(kù),將子停留信息與判別信息庫(kù)中閥值進(jìn)行比對(duì),得知子停留活動(dòng)類(lèi)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(xué)位授予單位】：山東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：P228.4;U495

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本文編號(hào)：1563018