Made available in DSpace on 2016-04-27T16:58:51Z (GMT). No. of bitstreams: 1 Roberto Canossa.pdf: 1455069 bytes, checksum: 4384e636c4f411f9c2c2a3d9efb4dbb2 (MD5) Previous issue date: 2009-03-02 / Secretaria da Educação do Estado de São Paulo / The study of Statistics became, in 1997, part of the curriculum of basic school, therefore, the preparation of the mathematics teachers became necessary in order to approach this theme, once many of the teachers either did not have this content in his/her initial formation or had it, but in a superficial way. The surveys performed by our group have also shown these results. These results and the great difficulties some mathematics teachers from public schools, especially in Diadema, have in developing, with the students, the Statistics contents and its interpretations is our motivation to the realization of this paper. In order to do so, we intended to answer the following question of research: What are the didactics characteristics to a continued formation to High School teachers, aiming at working with concepts of median and quartiles, so that students will be able to make decisions from the analysis of the realized variation, with the help of Dot-Plot and Box-Plot? To such verification, we have elaborated and applied a diagnose questionnaire (appendix 1), we have realized continued formation workshops from the results of these questionnaires, and, at last, we have observed a class from a volunteer teacher. We could notice that the majority of teachers do not work the concepts of median and quartiles: they limit themselves to the concept of mean, variance and standard deviation, inserted only as mathematics formulas, without giving sense to such concepts; besides, they do not have knowledge of the graphs Dot-Plot and Box-Plot. The workshop allowed an advancement concerning reasoning and statistical literacy to the volunteer teacher. However, we could realize that the two sections of workshop were not enough to get to level 5 (integrated process reasoning) of statistical reasoning proposed by Garfield (2002) / O estudo da Estatística passou, em 1997, a fazer parte do currículo da Escola Básica, sendo necessária então a preparação dos professores de matemática para a abordagem desse tema, uma vez que muitos deles não tiveram esse conteúdo em sua formação inicial ou tiveram de forma superficial e tecnicista. As pesquisas realizadas em nosso grupo apontam também para esses resultados. Nossa motivação para a realização deste trabalho deve-se a esses resultados e à grande dificuldade que alguns professores de matemática da rede pública do Estado de São Paulo, especificamente na região de Diadema, têm em desenvolver com seus alunos os conteúdos relativos a Estatística e suas interpretações. Para isso, pretendemos responder a seguinte questão de pesquisa: Quais as características didáticas de uma formação continuada para professores do Ensino Médio, visando o trabalho com conceitos de mediana e quartis, para que os alunos possam tomar decisões a partir da análise da variação percebida, com o auxílio do Dot-Plot e do Box-Plot? Para tal verificação, elaboramos e aplicamos um questionário diagnóstico (apêndice 1), realizamos oficinas de formação continuada a partir dos resultados desse questionário e, por fim, observamos uma aula com a professora colaboradora. O que pudemos notar é que a maioria dos professores não trabalha os conceitos de mediana e quartis: limitam-se aos conceitos de média, variância e desvio-padrão, inseridos apenas com fórmulas matemáticas, sem dar sentido para tais conceitos; além disso, não têm conhecimento dos gráficos Dot-Plot e Box-Plot. A oficina permitiu um avanço no nível de raciocínio e alfabetização estatística da professora colaboradora, mas podemos perceber também que as duas sessões de oficinas realizadas não foram suficientes para chegar ao nível 5 (processos de raciocínio integrados) de raciocínio estatístico proposto por Garfield (2002)

<p>За потребе поређења скупова података осмишљен је приступ поређењу<br />који се заснива на употреби графова. У овом приступу развијене су две<br />врсте графовских представа: представе вредности које описују скуп<br />података и представе разлика које описују разлике између две<br />представе вредности. У испитивањима приступа над синтетичким и<br />реалним скуповима података, показано је да је кроз визуално<br />истраживање представа разлика и примену помоћних поступака обраде<br />могуће уочити корисне обрасце који приказују разлике између представа<br />вредности, а посредно и између скупова података описаних путем ових<br />представа вредности.</p> / <p>Za potrebe poređenja skupova podataka osmišljen je pristup poređenju<br />koji se zasniva na upotrebi grafova. U ovom pristupu razvijene su dve<br />vrste grafovskih predstava: predstave vrednosti koje opisuju skup<br />podataka i predstave razlika koje opisuju razlike između dve<br />predstave vrednosti. U ispitivanjima pristupa nad sintetičkim i<br />realnim skupovima podataka, pokazano je da je kroz vizualno<br />istraživanje predstava razlika i primenu pomoćnih postupaka obrade<br />moguće uočiti korisne obrasce koji prikazuju razlike između predstava<br />vrednosti, a posredno i između skupova podataka opisanih putem ovih<br />predstava vrednosti.</p> / <p>In order to support data set comparison, a graph-based approach to<br />comparison was devised. In this approach, two types of graph-based<br />representations were introduced: value representations that represent a data<br />set and difference representations that represent differences between two<br />value representations. The results of approach evaluations on synthetic and<br />real data sets indicate that, by visually exploring difference representations<br />and applying auxiliary procedures, it is possible to discover useful patterns<br />which describe differences between two value representations and,<br />consequently, differences between the data sets corresponding to the value<br />representations.</p>

Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. Notably, it includes experimentations with two variants of the Recurrent Neural Networks, namely Gated Recurrent Unit (GRU) and Long-Short-Term-Memory (LSTM). Their performance is compared against traditional approaches namely Multiple Linear Regression (MLR) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Further on, the investigation focuses on tailoring the Recurrent Neural Network model to improve the performance. The experiments focus on the impact of different model architectures. Secondly, it focuses on testing the effect of time-related feature selection as an additional input to the Recurrent Neural Network (RNN) networks. Specifically, it explored how traditional methods such as Exploratory Data Analysis, Autocorrelation, and Partial Autocorrelation Functions Plots can contribute to the performance of RNN model. The current work shows through an empirical study on three industrial datasets that GRU architecture is a powerful method for the long-term forecasting task which outperforms LSTM on certain scenarios. In comparison to the MLR model, the RNN achieved a reduction in the RMSE between 5% up to to 10%. The most important findings include: (i) GRU architecture outperforms LSTM on industrial energy consumption datasets when compared against a lower number of hidden units. Also, GRU outperforms LSTM on certain datasets, regardless of the choice units number; (ii) RNN variants yield a better accuracy than statistical or regression models; (iii) using ACF and PACF as dicovery tools in the feature selection process is unconclusive and unefficient when aiming for a general model; (iv) using deterministic features (such as day of the year, day of the month) has limited effects on improving the deep learning model’s performance. / Noggranna långsiktiga energiprognosprognoser för industriella enheter är av intresse för distributionsföretag eftersom det potentiellt kan bidra till att minska deras churn och erbjuda stöd i beslutsfattandet vid säkring. Detta avhandlingsarbete presenterar olika metoder för att prognostisera energiförbrukningen för industriella enheter under en lång tids förutsägelsehorisont på 1 år. I synnerhet inkluderar det experiment med två varianter av de återkommande neurala nätverken, nämligen GRU och LSTM. Deras prestanda jämförs med traditionella metoder, nämligen MLR och SARIMA. Vidare fokuserar undersökningen på att skräddarsy modellen för återkommande neurala nätverk för att förbättra prestanda. Experimenten fokuserar på effekterna av olika modellarkitekturer. För det andra fokuserar den på att testa effekten av tidsrelaterat funktionsval som en extra ingång till RNN -nätverk. Specifikt undersökte den hur traditionella metoder som Exploratory Data Analysis, Autocorrelation och Partial Autocorrelation Funtions Plots kan bidra till prestanda för RNN -modellen. Det aktuella arbetet visar genom en empirisk studie av tre industriella datamängder att GRU -arkitektur är en kraftfull metod för den långsiktiga prognosuppgiften som överträffar ac LSTM på vissa scenarier. Jämfört med MLR -modellen uppnådde RNN en minskning av RMSE mellan 5 % upp till 10 %. De viktigaste resultaten inkluderar: (i) GRU -arkitekturen överträffar LSTM på datauppsättningar för industriell energiförbrukning jämfört med ett lägre antal dolda enheter. GRU överträffar också LSTM på vissa datauppsättningar, oavsett antalet valenheter; (ii) RNN -varianter ger bättre noggrannhet än statistiska modeller eller regressionsmodeller; (iii) att använda ACF och PACF som verktyg för upptäckt i funktionsvalsprocessen är otydligt och ineffektivt när man siktar på en allmän modell; (iv) att använda deterministiska funktioner (t.ex. årets dag, månadsdagen) har begränsade effekter på att förbättra djupinlärningsmodellens prestanda.

隨著經濟繁榮和網絡發展的日新月異，線上線下每時每刻都產生龐大數據，其中約有80%的文字、影像等非結構化數據，如何量化和採取適合的分析方法，成為有效提取有價值信息及對其加以利用的關鍵。針對文字類型的資料，本文提出探索性資料分析方法，並以《新青年》雜誌的語言變化為例，呈現如何選取文本特徵并对其量化及分析的過程。 首先，本文以卷為分析單位，多角度量化《新青年》雜誌各卷的文本結構，包括文本用字、用句、文言和白虛字使用以及常用字詞共用等方面，通過多種圖表相結合的呈現方式，窺探《新青年》雜誌語言變化歷程以及轉變特點。這其中既包括了對文言文到白話文轉變機制的探索，也包括白話語言演化的探索。其次，根據各卷初探的結果，尋找可區隔文言文和白話文兩種語言形式的文本特徵變數，再以《新青年》第一卷和第七卷為訓練樣本，結合主成分和羅吉斯迴歸，對文、白兩種語言形式的文章進行分類訓練，再利用第四卷進行測試。結果證實，所提取的文本變數能夠有效實現對文、白兩種語言形式的文章的區分。此外，本文亦根據前述初探結果以及人文學者經驗，探索《新青年》雜誌後期語言形式的變化，即從五四運動時期的白話文至以「紅色中文」為特徵的白話文（二戰之後中國使用的白話文）的變化。以第七卷和第十一卷為樣本進行訓練，結果證實這兩卷語言形式存在明顯區別；並加入台灣《聯合報》和中國大陸的《人民日報》進行分類預測，發現兩類報刊的語言偏向有明顯差異，值得後續深入研究。 / Tremendous data are produced every day, due to the rapid development of computer technology and economics. Unstructured data, such as text, pictures, videos, etc., account for nearly 80 percent of all data created. Choosing appropriate methods for quantifying and analyzing this kind of data would determine whether or not we can extract useful information. For that, we propose a standard operating process of exploratory data analysis (EDA) and use a case study of language changes in New Youth Magazine as a demonstration. First, we quantify the texts of New Youth magazine from different perspectives, including the uses of words, sentences, function words, and share of common vocabulary. We aim to detect the evolution of modern language itself as well as changes from traditional Chinese to modern Chinese. Then, according to the results of exploratory data analysis, we treat the first and seventh volumes of New Youth magazine for training data to develop classification model and apply the model to fourth volume (i.e., testing data). The results show that the traditional Chinese and modern Chinese can be successfully classified. Next, we intend to verify the changes from modern Chinese of the May 4th Movement to those by advocating Socialism. We treat the seventh volume and eleventh volume of New Youth magazine as training data and again develop a classification model. Then we apply this model to the United Daily News from Taiwan and People’s Daily from Mainland China. We found these two newspapers are very different and the style of United Daily News is closer to that of seventh volume, while the style of People’s Daily is more like that of eleventh volume. This indicates that the People’s Daily is likely to be influenced by the Soviet Union.

Supraglacial lakes in the ablation zone of the Greenland Ice Sheet (GrIS) often drain rapidly (in hours to days) by hydraulically-driven fracture (“hydrofracture”) in the summer. Hydrofracture can deliver large meltwater volumes to the ice-bed interface and open-up surface-to-bed connections, thereby routing surface meltwater to the subglacial system, altering basal water pressures and, consequently, the velocity profile of the GrIS. The study of rapidly draining lakes is thus important for developing coupled hydrology and ice-dynamics models, which can help predict the GrIS’s future mass balance. Remote sensing is commonly used to identify the location, timing and magnitude of rapid lake-drainage events for different regions of the GrIS and, with the increased availability of high-quality satellite data, may be able to offer additional insights into the GrIS’s surface hydrology. This study uses new remote-sensing datasets and develops novel analytical techniques to produce improved knowledge of rapidly draining lake behaviour in west Greenland over recent years. While many studies use 250 m MODerate-resolution Imaging Spectroradiometer (MODIS) imagery to monitor intra- and inter-annual changes to lakes on the GrIS, no existing research with MODIS calculates changes to individual and total lake volume using a physically-based method. The first aim of this research is to overcome this shortfall by developing a fully-automated lake area and volume tracking method (“the FAST algorithm”). For this, various methods for automatically calculating lake areas and volumes with MODIS are tested, and the best techniques are incorporated into the FAST algorithm. The FAST algorithm is applied to the land-terminating Paakitsoq and marine-terminating Store Glacier regions of west Greenland to investigate the incidence of rapid lake drainage in summer 2014. The validation and application of the FAST algorithm show that lake areas and volumes (using a physically-based method) can be calculated accurately using MODIS, that the new algorithm can identify rapidly draining lakes reliably, and that it therefore has the potential to be used widely across the GrIS to generate novel insights into rapidly draining lakes. The controls on rapid lake drainage remain unclear, making it difficult to incorporate lake drainage into models of GrIS hydrology. The second aspect of this study therefore investigates whether various hydrological, morphological, glaciological and surface-mass-balance controls can explain the incidence of rapid lake drainage on the GrIS. These potential controlling factors are examined within an Exploratory Data Analysis statistical technique to elicit statistical similarities and differences between the rapidly and non-rapidly draining lake types. The results show that the lake types are statistically indistinguishable for almost all factors, except lake area. It is impossible, therefore, to elicit an empirically-supported, deterministic method for predicting hydrofracture in models of GrIS hydrology. A frequent problem in remote sensing is the need to trade-off high spatial resolution for low temporal resolution, or vice versa. The final element of this thesis overcomes this problem in the context of monitoring lakes on the GrIS by adapting the FAST algorithm (to become “the FASTER algorithm”) to use with a combined Landsat 8 and Sentinel-2 satellite dataset. The FASTER algorithm is applied to a large, predominantly land-terminating region of west Greenland in summers 2016 and 2017 to track changes to lakes, identify rapidly draining lakes, and ascertain the extra quantity of information that can be generated by using the two satellites simultaneously rather than individually. The FASTER algorithm can monitor changes to lakes at both high spatial (10 to 30 m) and temporal (~3 days) resolution, overcoming the limitation of low spatial or temporal resolution associated with previous remote sensing of lakes on the GrIS. The combined dataset identifies many additional rapid lake-drainage events than would be possible with Landsat 8 or Sentinel-2 alone, due to their low temporal resolutions, or with MODIS, due to its inferior spatial resolution.

Nowadays quality of life is often discussed topic. In defining this term, there is considerable ambiguity and disunity, since there is no universally accepted definition, nor theoretically sophisticated model. However, despite this fact, the level of quality of life is currently one of the most discussed topic. Monitoring the quality of life by using a variety of indicators are engaged in several international organizations, one of them is the Development Programme of the United Nations. This organization annually publishes the Human Development Index, which divides the world´s countries into four groups according to their level of development: low, medium, high and very high development. The aim of this thesis is to analyze the quality of life in 125 countries by using cluster analysis, accurately the Ward's method. Quality of life in this thesis is evaluated based on 19 demographic and economic indicators, which include life expectancy, literacy rate, access to drinking water and infant mortality rate. The cluster analysis divided the country into individual clusters by their similarities. Six clusters were created by this analysis, which had been compared with the results of Human Development Index. The clusters very well reflect the division, which is commonly used in the characterization of developing and developed countries. Each of the six clusters can be very well described and characterized in terms of quality of life. It is also possible qualify those clusters as poorest developing, low developed, moderately developed, medium development, high and very high development countries. Based on the results it can be stated that this analysis is consistent with other indicators of quality of life and the resulting clusters are identical with the division of countries which is commonly used.

Otimizar simultaneamente os interesses dos usuários, anunciantes e publicadores é um grande desafio na área de publicidade computacional. Mais precisamente, a ordenação de anúncios, ou ad ranking, desempenha um papel central nesse desafio. Por outro lado, nem mesmo as melhores fórmulas ou algoritmos de ordenação são capazes de manter seu status por um longo tempo em um ambiente que está em constante mudança. Neste trabalho, apresentamos uma análise orientada a dados que mostra a importância de combinar diferentes dimensões de publicidade computacional por meio de uma abordagem evolutiva para ordenação de anúncios afim de responder a mudanças de forma mais eficaz. Nós avaliamos as dimensões de valor comercial, desempenho histórico de cliques, interesses dos usuários e a similaridade textual entre o anúncio e a página. Nessa avaliação, nós averiguamos o desempenho e a correlação das diferentes dimensões. Como consequência, nós desenvolvemos uma abordagem evolucionária para combinar essas dimensões. Essa abordagem é composta por três partes: um repositório de configurações para facilitar a implantação e avaliação de experimentos de ordenação; um componente evolucionário de avaliação orientado a dados; e um motor de programação genética para evoluir fórmulas de ordenação de anúncios. Nossa abordagem foi implementada com sucesso em um sistema real de publicidade computacional responsável por processar mais de quatorze bilhões de requisições de anúncio por mês. De acordo com nossos resultados, essas dimensões se complementam e nenhuma delas deve ser neglicenciada. Além disso, nós mostramos que a combinação evolucionária dessas dimensões não só é capaz de superar cada uma individualmente, como também conseguiu alcançar melhores resultados do que métodos estáticos de ordenação de anúncios. / Simultaneous optimization of users, advertisers and publishers\' interests has been a formidable challenge in online advertising. More concretely, ranking of advertising, or more simply ad ranking, has a central role in this challenge. However, even the best ranking formula or algorithm cannot withstand the ever-changing environment of online advertising for a long time. In this work, we present a data-driven analysis that shows the importance of combining different aspects of online advertising through an evolutionary approach for ad ranking in order to effectively respond to changes. We evaluated aspects ranging from bid values and previous click performance to user behavior and interests, including the textual similarity between ad and page. In this evaluation, we assessed commercial performance along with the correlation between different aspects. Therefore, we proposed an evolutionary approach for combining these aspects. This approach was composed of three parts: a configuration repository to facilitate deployment and evaluation of ranking experiments; an evolutionary data-based evaluation component; and a genetic programming engine to evolve ad ranking formulae. Our approach was successfully implemented in a real online advertising system that processes more than fourteen billion ad requests per month. According to our results, these aspects complement each other and none of them should be neglected. Moreover, we showed that the evolutionary combination of these aspects not only outperformed each of them individually, but was also able to achieve better overall results than static ad ranking methods.

No description available.

A comprehensive knowledge about the infiltration situation in a sewer system is required for sustainable operation and cost-effective maintenance. Due to the high expenditures of infiltration measurements an optimisation of necessary measurement campaigns and a reliable transfer of measurement results to comparable areas are essential. Suitable methods were developed to improve the information yield of measurements by identifying appropriate measuring point locations and to assign measurement results to other potential measuring points by comparing sub-catchments and classifying reaches. The methods are based on the introduced similarity approach “Similar sewer conditions lead to similar infiltration/inflow rates” and on modified multivariate statistical techniques. The developed methods have a high degree of freedom against data needs. They were successfully tested on real and generated data. For suitable catchments it is estimated, that the optimisation potential amounts up to 40 % accuracy improvement compared to non-optimised measuring point configurations. With an acceptable error the transfer of measurement results was successful for up to 75 % of the investigated sub-catchments. With the proposed methods it is possible to improve the information about the infiltration status of sewer systems and to reduce the measurement related uncertainty which results in significant cost savings for the operator. / Für den nachhaltigen Betrieb und die kosteneffiziente Unterhaltung von Kanalnetzen ist eine genaue Bestimmung ihrer Fremdwassersituation notwendig. Eine Optimierung der dazu erforderlichen Messkampagnen und eine zuverlässige Übertragung der Messergebnisse auf vergleichbare Gebiete sind aufgrund der hohen Aufwendungen für Infiltrationsmessungen angezeigt. Dafür wurden geeignete Methoden entwickelt, welche einerseits den Informationsgehalt von Messungen durch die Bestimmung optimaler Messpunkte verbessern und andererseits Messresultate mittels Vergleichen von Teileinzugsgebieten und Klassifizierungen von Kanalhaltungen zu anderen potenziellen Messstellen zuordnen. Die Methoden basieren auf dem Ähnlichkeitsansatz “Ähnliche Kanaleigenschaften führen zu ähnlichen Fremdwasserraten” und nutzen modifizierte multivariate statistische Verfahren. Sie haben einen hohen Freiheitsgrad bezüglich der Datenanforderung. Die Methoden wurden erfolgreich anhand gemessener und generierter Daten validiert. Es wird eingeschätzt, dass das Optimierungspotenzial bei geeigneten Einzugsgebieten bis zu 40 % gegenüber nicht optimierten Mess-netzen beträgt. Die Übertragung der Messergebnisse war mit einem akzeptablen Fehler für bis zu 75 % der untersuchten Teileinzugsgebiete erfolgreich. Mit den entwickelten Methoden ist es möglich, den Kenntnisstand über die Fremdwassersituation eines Kanalnetzes zu verbessern und die messungsbezogene Unsicherheit zu verringern. Dies resultiert in Kostenersparnissen für den Betreiber.

In der Mobilitätsforschung entstand in den vergangenen Jahrzehnten eine breite Wissensbasis für das Verständnis von Verkehrsursachen und Zusammenhängen, die das Verkehrsverhalten determinieren. Mit der Entwicklung von Verkehrsmodellen lag das Forschungsinteresse zunächst primär bei Ökonomen und Ökonometrikern sowie Verkehrsingenieuren. Bald kamen andere Wissenschaftsbereiche wie die Psychologie oder die Geowissenschaften hinzu, welche sich in der Folge zunehmend mit dem Thema Mobilität befassten und die zur Erklärung des menschlichen Verhaltens ganz unterschiedliche Methoden und Maßstäbe nutzten. Heute versuchen zumeist handlungsorientierte Ansätze, auf Individualebene, Faktoren zu bestimmen, die Aufschluss über die Verhaltensvariabilität in der Bevölkerung geben und damit einen möglichst großen Beitrag zur Varianzaufklärung leisten. Werden Einflussfaktoren in geeigneter Weise identifiziert und quantifiziert, können Defizite und Chancen erkannt und das Verhalten steuernde Maßnahmen entworfen werden. Mit deren Hilfe wird ungewollten Entwicklungen entgegengesteuert. Junge Erwachsene stellen aufgrund ihrer sehr unterschiedlichen Phasen im Lebenszyklus, z. B. gerade anstehender oder abgeschlossener Ausbildung, Umzug in eine eigene Wohnung, Familiengründung, Neuorientierung in Arbeitsroutinen oder das Einleben in ein anderes Lebensumfeld einer fremden Stadt, intuitiv eine sehr heterogene Gruppe dar. Die Modellierung des Verhaltens ist für diese Altersgruppe besonders schwierig. Aus der Komplexität dieser Problemstellung heraus ist ersichtlich, dass fundierte Analysen zur Mobilität junger Erwachsener notwendig sind, um verkehrsplanerische Defizite aufzudecken und Chancen zu erkennen. Der methodische Schwerpunkt des Beitrages liegt auf der Bildung einer Typologie des Verkehrsverhaltens junger Erwachsener. Die verwendete Datengrundlage ist das „Deutsche Mobilitätspanel – MOP“. Dabei wird der Versuch unternommen, zunächst Variablen aller relevanten Dimensionen des handlungsorientierten, aktivitätsbasierten Verkehrsverhaltens zusammenzustellen und für eine entsprechende Analyse aufzubereiten. Im Anschluss werden geeignete und in den Sozialwissenschaften erprobte Verfahren zur Ähnlichkeitsmessung eingesetzt, um möglichst verhaltensähnliche Personen zu typologisieren. Im Weiteren finden konfirmatorische Analysetechniken Anwendung, mit deren Hilfe Verhaltenshintergründe erklärt und inferenzstatistisch geprüft werden. Als Ergebnis wird eine clusteranalytische Typologisierung vorgestellt, die im Anschluss anhand soziodemografischer Indikatoren und raumstruktureller Kriterien der Lagegunst beschrieben wird. Aufgrund der gewonnenen Erkenntnisse können objektive und im Idealfall quantifizierbare, d. h. prognosefähige Merkmale zur Bildung verkehrssoziologischer und weitgehend verhaltensähnlicher Personengruppen genutzt werden. / Over the last few decades of mobility research, a wide base of knowledge for understanding travel determinants and causal relationships in mobility behavior has been established. The development of travel models was at first of interest primarily to economists and econometricians as well as transportation engineers. They were soon joined by other scientific areas such as psychology or the geosciences, which as a result increasingly addressed the theme of mobility and used quite different methodologies and criteria for explaining human behavior. Today, activity-oriented approaches generally attempt to determine individual-level factors that provide information on behavioral variability within the population, thereby contributing greatly to explaining variances. If explanatory factors can be properly identified and quantified, then deficiencies and opportunities can be recognized and measures for influencing behavior can be conceptualized. With their help, undesirable developments can be avoided. Because of their highly differing stages in life, e.g. upcoming or recently completed education, moving into their own apartment, starting a family, becoming oriented in a work routine or adapting to a new environment in a different city, young adults are intuitively a very heterogeneous group. Modeling the behavior of this age group is particularly difficult. This problem makes it clear that founded analysis of the mobility of young adults is necessary in order to recognize deficiencies and opportunities in transportation planning. The methodological focus of this work is on creating a typology of young adults’ travel behavior. The base data is from the “Deutsches Mobilitätspanel – MOP” (German Mobility Panel). An attempt is made to gather and prepare all relevant dimensions of decision-oriented, activity-based travel behavior for a corresponding analysis. Afterward, appropriate and proven methods from the social sciences are used to test for similarity in order to identify groups of persons which are as behaviorally homogeneous as possible. In addition, confirmatory data analysis is utilized which helps explain and test, through inferential statistics, determinants of behavior. The resulting typology from the cluster analysis is presented and followed by a description using sociodemographic indicators and spatial criteria of accessibility. The findings make it possible to use objective and, ideally, quantifiable and therefore forecastable characteristics for identifying sociological population groups within which similar travel behavior is displayed.