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首頁(yè)> 外文學(xué)位 >Caracterisation des lieux d'activites a partir de donnees de cartes a puce de transport collectif.
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Caracterisation des lieux d'activites a partir de donnees de cartes a puce de transport collectif.

機(jī)譯：根據(jù)公共交通智能卡的數(shù)據(jù)來(lái)表征活動(dòng)場(chǎng)所。

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The use of smart-card in transit system has developed a lot since the end of the 90's. Their first goal is to collect fare automatically and to check instantly the ticket validity. These systems produce a huge amount of data that can be used to analyze a transit network. Actually, with the databases stemming from such systems, we can have a full and continuous picture of travelers' behaviors. These behaviors are conditioned by the geography of the area and the structure of the network. For instance, people use public transportation mostly for commuting. That's why city centers attract most users during the morning peak hours. These variations in space and time will be the core of this study.;In the first part, we are going to present a literature review about variability analysis and models of transportation users' behaviors, smart-card systems and their interests and finally, data mining and its application on geospatial databases.;In a second part, we are going to present the STO public transportation network and to explain the methodology used to characterize activity places. The method is divided in two main phases: (1) On one hand, we are going to study bus stops' behaviors by clustering the bus stop population in a small number of homogeneous groups with data mining algorithms. During this phase, one of the major issues we face is the determination of an appropriate number of groups. Thus, after having discussed and identified a appropriate number of groups, we are going to study the stops variability within these groups in order to decrease the data noise. (2) On the other hand, we are going to create a geographic information system (GIS) of the STO public transportation network. This GIS locates all major activity places in the Ottawa region and all the STO bus stops. Then, we are going to define proximity relations between GIS elements to, finally, find a characterization for the activity places.;The end of this second part is presenting the databases used to undertake the studies and is describing the main facts of the network (number of boardings per hour per day, origin-destination...).;We are going to analyze the influence of major activity locations on a public transportation network using smart-card data from the Outaouais Transit Company (Societe des transports de l'Outaouais, STO). That is to say, we are going to see how users who board near a main trip generator usually behave. The volume of data in the transportation database is so huge (more than 800,000 data a month) that we are going to use data mining techniques to extract knowledge from them.;The third part is applying the methodology and extracting the results. Actually, we are going to cluster our bus stops population in nine typical behavior groups. The study of the belonging group variability will show us that groups are composed of a core of non-variable stops and a cloud of very unpredictable elements. It will permit us to decrease the noise in the data. By analyzing the proportion of each type of users (Adult, elderly, student) in each group, we will see that there is a majority of students on stops mostly used between 9:00 am and 16:00 pm and that they tend to make shorter trips. This analyze also confirms that users living far from the city center use transportation earlier in the morning and they tend to do longer trips. And finally, the activity places characterization reveals us that car parks create boardings mostly between 7:00 and 8:00 in the morning, the city center between 16:00 and 17:00, hospitals and shopping centers create a regular use during all the day.
機(jī)譯：自從90年代末以來(lái)，在交通運(yùn)輸系統(tǒng)中使用智能卡已經(jīng)有了很大發(fā)展。他們的首要目標(biāo)是自動(dòng)收費(fèi)，并立即檢查車票的有效性。這些系統(tǒng)產(chǎn)生大量可用于分析公交網(wǎng)絡(luò)的數(shù)據(jù)。實(shí)際上，利用來(lái)自此類系統(tǒng)的數(shù)據(jù)庫(kù)，我們可以對(duì)旅行者的行為進(jìn)行完整而連續(xù)的描述。這些行為取決于區(qū)域的地理位置和網(wǎng)絡(luò)的結(jié)構(gòu)。例如，人們主要將公共交通工具用于通勤。這就是市中心在早上高峰時(shí)段吸引大多數(shù)用戶的原因。這些時(shí)空的變化將是本研究的核心。在第一部分中，我們將對(duì)運(yùn)輸用戶的行為，智能卡系統(tǒng)及其利益以及數(shù)據(jù)的可變性分析和模型進(jìn)行文獻(xiàn)綜述。在第二部分中，我們將介紹STO公共交通網(wǎng)絡(luò)并解釋用于描述活動(dòng)場(chǎng)所的方法。該方法分為兩個(gè)主要階段：（1）一方面，我們將通過(guò)使用數(shù)據(jù)挖掘算法將公交車站人口聚類為少量的同類組來(lái)研究公交車站的行為。在這一階段，我們面臨的主要問(wèn)題之一是確定適當(dāng)數(shù)量的團(tuán)體。因此，在討論并確定了適當(dāng)數(shù)量的組之后，我們將研究這些組內(nèi)的站點(diǎn)可變性，以減少數(shù)據(jù)噪聲。 （2）另一方面，我們將創(chuàng)建STO公共交通網(wǎng)絡(luò)的地理信息系統(tǒng)（GIS）。該GIS可以找到渥太華地區(qū)所有主要活動(dòng)場(chǎng)所以及所有STO公交車站。然后，我們將定義GIS元素之間的鄰近關(guān)系，以最終找到活動(dòng)場(chǎng)所的特征。;第二部分的末尾是用于進(jìn)行研究的數(shù)據(jù)庫(kù)，并描述了網(wǎng)絡(luò)的主要事實(shí)（每天每小時(shí)的登機(jī)次數(shù)，始發(fā)地……）；我們將使用來(lái)自?shī)W陶瓦伊運(yùn)輸公司（Societe des transports de l''的智能卡數(shù)據(jù)）分析主要活動(dòng)地點(diǎn)對(duì)公共交通網(wǎng)絡(luò)的影響STO）也就是說(shuō)，我們將看到在主行程發(fā)生器附近登機(jī)的用戶通常的行為。運(yùn)輸數(shù)據(jù)庫(kù)中的數(shù)據(jù)量非常大（每月超過(guò)80萬(wàn)個(gè)數(shù)據(jù)），我們將使用數(shù)據(jù)挖掘技術(shù)從中提取知識(shí)。第三部分是應(yīng)用方法論并提取結(jié)果。實(shí)際上，我們將公交車站的人口分為九個(gè)典型的行為群體。對(duì)歸屬組變異性的研究將向我們表明，組是由不變變量的核心和非常不可預(yù)測(cè)的元素組成的云組成的。這將使我們減少數(shù)據(jù)中的噪聲。通過(guò)分析每個(gè)組中每種類型的用戶（成人，老人，學(xué)生）的比例，我們將看到有大多數(shù)學(xué)生在?？空局惺褂脮r(shí)間最多為上午9:00至下午16:00，并且他們傾向于短途旅行。該分析還證實(shí)，遠(yuǎn)離市中心的用戶在清晨使用交通工具，并且他們傾向于出行更長(zhǎng)的時(shí)間。最后，活動(dòng)場(chǎng)所的特征向我們揭示了停車場(chǎng)主要是在早上7:00至8:00之間創(chuàng)建登機(jī)牌，市中心在16:00至17:00之間是登機(jī)牌，醫(yī)院和購(gòu)物中心在所有活動(dòng)期間都經(jīng)常使用天。

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  • 作者

    Piriou, Clement.;

  • 作者單位

    Ecole Polytechnique, Montreal (Canada).;

  • 授予單位 Ecole Polytechnique, Montreal (Canada).;
  • 學(xué)科 Engineering Industrial.
  • 學(xué)位 M.Sc.A.
  • 年度 2008
  • 頁(yè)碼 120 p.
  • 總頁(yè)數(shù) 120
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類
  • 關(guān)鍵詞

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