Value-items from the Schwartz scale of Values have been added to travel data to investigate if the value-items can be used to model mode choice. Two kinds of mode choice models, both discrete choice models, multinomial models (MNL) and the Machine Learning Models Random Forests (RF) were constructed, using Travel Diary data (RVU) and additional data from European Social Survey (ESS). The additional data was connected to the base data by grouping the individuals using three key variables: gender, age, and household income. Models were then created with and without any data from the value-items to screen for variables that had an impact on the model. The RF model predicted the correct modes for all but the smaller groups, car passengers and biking. While the MNL model had less success accurately assessing which mode someone had chosen. The MNL with additional grouped value-items improved, while the models created using Random Forest had no difference in accuracy based on the addition. Even though there were some significant value-items in the MNL-models, the expected consequences from them small, as the base model specification might be insufficient in incorporating more relevant variables. Based on the Random Forest having no use from the value-items along with them being of similar importance no value-items stood out for further testing. The main findings were thus that no value-items of particular interest could be found with the RF model while the results for the MNL-model were inconclusive. Suggested improvements for further similar studies would be to perform grouping using data for a longer time frame and or to use a value-model as input for the mode choice modelling. It is deemed appropriate to study what values people associate with specific modes directly, and to investigate if other models such as car ownership models or models of choices between different versions of the same mode could be more suitable for additional value-data. / Värdeobjekt från Schwartz värderingsskala har kombinerats med resedata för att undersöka om värdeobjekten kan användas vid modellering av färdmedelsval. Två typer av färdmedelsmodeller, multinomiala modeller (MNL) och Random Forests konstruerades. Den data som användes var Resvanedata (RVU), med kompletterande värderingsdata från European Social Survey (ESS). ESS-datan kopplades till basdatan genom att gruppera individerna med hjälp av tre nyckelvariabler: kön, ålder och hushållsinkomst. Sedan skapades modeller med och utan den kompletterande datan för att se om modellerna påverkades. RF-modellens resultat överensstämde väl med de faktiska valen förutom för de mindre grupperna: bilpassagerare och cyklister. MNL-modellen hade mindre framgång med att bedöma vilket färdmedel en individ hade valt. MNL-modellen med ytterligare grupperade värdeobjekt förbättrades i jämförelse med grundmodellen, medan modellerna skapade med Random Forest inte skilde sig märkbart från varandra. Även om värdeobjekten i MNL-modellerna var signifikanta är de förväntade konsekvenserna av dem små, eftersom specifikationen för basmodellen tros saknar andra mer relevanta variabler. RF-modellen gynnades inte av värdeobjekten och inga värdeobjekt var betydande för modellen. De huvudsakliga fynden var att inga värdeobjekt av särskilt intresse kunde hittas med RF-modellen medan resultaten för MNL-modellen var ofullständiga. Föreslagna förbättringar för ytterligare liknande studier skulle vara att utföra gruppering med hjälp av data för ett längre tidsspann eller att introducera en värdemodell som indata för modelleringen av färdmedelsval. Det bedöms lämpligt att studera vilka värderingar människor förknippar med specifika färdmedel direkt samt att undersöka om andra modeller såsom av bilägande eller i val mellan olika versioner av samma färdmedel skulle var mer passande för att modelleras med hjälp av data med värderingar.

Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, and generalized linear models, shed light on their intricate dynamics. Findings offer insights into risk and protective factors associated with these variables, guiding tailored interventions for individuals in psychological distress. Additionally, policymakers can utilize these insights to develop comprehensive strategies promoting mental health and well-being at a societal level.

Ce travail de recherche traite de la problématique des transports dans les villes d’Indonésie, Jakarta et Bandung, mais également de la grande concurrence modale du trajet Jakarta-Bandung et Bandung-Jakarta. Les préférences des passagers sont des variables très importantes à connaître en raison de leurs impacts pour choisir un mode de transport parmi d’autres. Dans les transports, le modèle Logit est largement utilisé comme une méthode pour aborder la problématique du choix de transport multimodal comportant de multiples variables, mais dans la présente recherche, ces modèles ne sont pas appropriés pour la résolution de nos problèmes, car il y a des variables particulières à identifier et à prendre en compte. Par conséquent, nous avons développé pour nos besoins le modèle « Logit Mixed Multinomial Adapté (LMMA) » comme outil dédié à l’analyse décisionnelle dans le choix des modes de transport des passagers. La première partie de nos travaux de recherches porte sur l’identification et la compréhension des problèmes de transports intra-cité d’origine et de destination pour le choix du mode de transport entre Jakarta et Bandung (et puis entre Bandung et Jakarta). La seconde partie concerne le processus de décision final en proposant et en analysant les résultats d’un questionnaire adressé à de nombreux utilisateurs de la liaison Jakarta-Bandung (et Bandung-Jakarta). L’analyse permet pour chaque situation d’origine et de destination, et en tenant compte des services offerts par chaque mode inter-cité, d’identifier quel est le mode le plus compétitif. / An ideal city or intercity transport system is one where all the transport networks, involving in general different modes of transport, could serve together the cities connections to fulfill a passenger demand and satisfaction. Each transport network should have a logical layout (as possible with minimum discontinuities) to meet the required demands. Also in that ideal system, the different modes of transport should not only have their own good performances but also the exchange between modes should be done with harmony. The conditions as mentioned above are worldwide challenges. The present work deals with the transportation problematic between two Indonesian cities, and also with the high modal competition on the Jakarta-Bandung corridor. On that corridor, road transport is currently the main demanding mode for passengers transportation. The airlines cannot compete and discontinued their operations to this route. Nowadays, railway transport is decaying. Passengers preferences are the main variables for the final modal choice. It is necessary to know preferences due to their decisions impacts to choose one mode over the others. Those preferences are in fact not simple to express in a complex city and intercity transport system. In transportation, the Logit model is widely used as a method to explore the problematic of modal choices involving a lot of different variables. There are several Logit models already developed, such as “General Extreme Value”, “Probit”, and “Nested model”, but in this research, they are not compatible to solve our defined problems because there are some particular identified variables to be taken into account. Therefore we propose the "Adapted Mixed Multinomial Logit (AMML)" Model as a tool for analysis towards passenger's decision in modal choices. On the Jakarta-Bandung corridor, modal choices are influenced by the encountered problems in intercity transport at origin and destination. One part on this research deals with identification and understanding of the intracity transport problems of origin and destination on the choice of transport mode in Jakarta-Bandung corridor (Jakarta-Bandung and Bandung-Jakarta direction). The second part of this research deals with the final decision process by analyzing the results of questionnaires addressed to many users of the Jakarta-Bandung corridor. The five main variables of the last questionnaire are travel time, overall cost, security conditions, quality of travel information and connectivity conditions relevant to intercity transport and intracities transport conditions as well. After validation of the questionaires, this research uses the AMML model to get final decision result by comparing one mode among three intercity transport mode (train, minibus, and car) using the values of the variables. Taking into account the characteristics of each intercity mode of transportation, the analysis identifies the most competitive intercity transport mode for each situation from departure city to arrival city. Using alternative public and private transport modes policies, one could in the future modify passenger choice on intercity transport mode. Therefore, this study is relevant for improving of intracity and intercity transport systems.

進行調查時，最後的抽樣單位常是從不同的群集取得的，而同一群集內的樣本對象，因背景類似而對於某些問題常會傾向相同或類似的反應，研究者若忽略這種群內相關性，仍以獨立性樣本進行分析時，因其共變異數矩陣通常會與多項模式的共變異數矩陣相差懸殊，而造成所謂的額外變異或不足變異的現象。本文在不同的情況下，提出了Dirichlet-Multinomial模式(簡稱DM模式)、擴展的DM模式、以及兩種平均數-共變異數矩陣模式，以適當的彙整所有的群集資料。並討論DM與EDM模式中相關之參數及格機率之最大概似估計法，且分別對此兩種平均數-共變異數矩陣模式，提出求導廣義最小平方估計的程序。此外，也針對幾種特殊的二維表及三維表結構，探討對應的參數及格機率之估計方法。並提出計算簡易的Score統計檢定量以判斷群內相關(intra-cluster correlation)之存在性，及判斷資料集具有額外或不足變異，而對於不同母體的群內相關同質性檢定亦提出討論。 / This paper presents a modelling method of analyzing categorical data with overdispersion or underdispersion.　In many studies, data are collected from differ clusters, and members within the same cluster behave similary.　Thus, the responses of members within the same cluster are not independent and the multinomial distribution is not the correct distribution for the observed counts.　Therefore, the covariance matrix of the sample proportion vector tends to be much different from that of the multinomial model.　We discuss four different models to fit counts data with overdispersion or underdispersion feature, witch include Dirichlet-Multinomial model (DM model), extended DM model (EDM model), and two mean-covariance models.　Method of maximum-likelihood estimation is discussed for DM and EDM models.　Procedures to derive generalized least squares estimates are proposed for the two mean-covariance models respectively. As to the cell probabilities, we also discuss how to estimate them under several special structures of　two-way and three-way tables.　More easily evaluated Score test statistics are derived for the DM and EDM models to test the existence of　the intra-cluster correlation.　And the test of homogeneity of intra-cluster correlation among several populations is also derived.

過去都市的發展與道路規劃多以汽機車為主體，對於自行車的騎乘環境相對不夠友善，而隨著近年國內自行車使用率逐年攀升，據資料指出自行車發生事故的機率也有提高的趨勢，顯示自行車於道路上之安全性考量更需重視。故本研究將針對台北市自行車肇事資料進行深入探討，找出影響肇事嚴重度之因素，進而從規劃設計面研擬降低自行車事故之改善策略。 本研究係以民國98年至102年台北市自行車事故資料為分析對象，將肇事嚴重程度分為「死亡或頭部受傷」、「人員受傷」及「未受傷」三類，同時根據文獻回顧及實務上所能取得之資料，蒐集人、路、環境等24項研究變數。首先透過統計分析了解肇事資料之特性，而後再以多項式羅吉斯迴歸模型，分別針對整體事故以及不同空間及不同事故型態之自行車肇事資料，建構自行車肇事嚴重度模型，以釐清影響自行車事故之主要因素。 研究結果顯示，道路因素中事故位置為路口及路段對於自行車事故皆有顯著影響，其中路口造成死亡或受傷之機率更高；環境因素中，因彎道或建物造成視距不良對於增加自行車事故亦有顯著影響，而坡道則會降低事故發生之機率；在人的因素中，18歲以下和年齡越大、酒駕、直行或右轉，皆會增加因自行車事故致死或受傷之機率。最後依據實證之結果，謹從交通管理中的3E政策-交通工程（Engineering）、交通教育（Education）及交通執法（Enforcement）三面向之觀念及角度帶入都市設計層面，提出道路及環境改善措施，以提升都市街道之自行車騎乘環境，並透過教育宣導、推廣活動及相關法令規範等配套措施，藉以增加自行車之騎乘安全。

隨著氣候變遷與溫室效應影響日益明顯，台灣近年發生極端強降雨颱風的次數越來越頻繁，更造成流域地區嚴重災情，而從莫拉克風災經驗可體會到家戶風險溝通的重要性，也意識到我國實務與學術上相關研究的缺乏，故本研究探討家戶風險溝通機制中各項重要因素與調適行為間的關係，以及找出影響家戶調適行為決策之關鍵因素，進而提出家戶風險溝通策略之改善建議，以促進家戶採取調適行為。 本研究經由文獻回顧建立家戶調適行為之風險溝通概念架構，依循此架構研擬問卷，以高屏溪流域地區家戶為研究對象進行問卷調查，透過結構方程模式(SEM)驗證風險溝通架構，了解風險溝通機制各項因素與影響調適行為各因素之關係，後以面對災害回應之強烈將調適行為積極程度分為「消極或低度積極」、「中度積極」與「高度積極」，運用多項式羅吉斯迴歸模型建立家戶應變措施決策模型與調適措施決策模型，找出影響家戶調適行為決策之關鍵因素。 研究結果顯示，調適行為受到內在認知的影響，而內在認知同時受風險溝通機制與外在環境之影響，就風險溝通機制而言，親友鄰居、村里長與地方政府等社區網絡為重要管道。影響調適行為之關鍵因素以災害認知為主，其次為調適行為認知，居住村里次之，其中災害認知與調適行為認知越高，越有可能採取較積極之調適行為，此外，由於自然社會環境、風險溝通特性與社會經濟背景等因素交互影響下，各村里在調適行為決策上也有所差異。最後依據實證結果，與水患自主防災社區風險溝通現況，提出改善家戶風險溝通之策略建議，期望增進風險溝通機制的完備與促進家戶採取調適行為，以減緩極端氣候造成的衝擊。 / Along with the intensification of global climate change and greenhouse effect, typhoons with extreme rainfall strike Taiwan more and more frequently, which cause severe disasters in watershed area. From the experience of Typhoon Morakot in 2009, we realized the importance of risk communication with households and also the lack of related academic research. As a result, this study aims to discuss important factors in risk communication mechanism and their relationships with adaptive behaviors. It also find out key factors influencing decision-making of adaptive behaviors. Based on literature review, this study build a conceptual framework of risk communication process to describe how to trigger adaptive behaviors and encourage adaptive behaviors with risk communication. This study send out questionnaires to the households in Kaoping River Watershed and use structural equation modeling(SEM) to verify the conceptual framework. Then according to attitude of positive degree, adaptive behaviors are classified into“passive or low”,“medium” and “high” levels. By multinomial logistic regression, an empirical analysis was performed to analyze the key factors influencing decision-making of adaptive behaviors. The results show that adaptive behaviors are affected by internal cognition and at the same time internal cognition are affected by risk communication mechanism and external environment. As for risk communication mechanism, family, friends, neighbors and local governments are crucial communication channel. Key factors influencing decision-making of adaptive behaviors are cognition of disaster and adaptive behavior. People with higher cognition of disaster and adaptive behavior would more likely to take positive adaptive behaviors. Besides, community they lived in is also a key factor. Because the interaction of environments, risk communication patterns and socioeconomic attributes, people from different communities would take different adaptive behaviors. Based on empirical results, this study propose suggestions of risk communication strategies in order to better the risk communication mechanism and encourage households to take adaptive behaviors.

由於電腦科技的快速發展，網際網路(World Wide Web；簡稱WWW)使得資料共享及搜尋更為便利，其中的網路搜尋引擎(Search Engine)更是尋找資料的利器，最知名的「Google」公司就是藉由搜尋引擎而發跡。網頁搜尋多半依賴各網頁的特徵，像是熵(Entropy)即是最為常用的特徵指標，藉由使用者選取「關鍵字詞」，找出與使用者最相似的網頁，換言之，找出相似指標函數最高的網頁。藉由相似指標函數分類也常見於生物學及生態學，但多半會計算兩個社群間的相似性，再判定兩個社群是否相似，與搜尋引擎只計算單一社群的想法不同。 本文的目標在於研究若資料服從多項分配，特別是似幾何分配的多項分配（許多生態社群都滿足這個假設），單一社群的指標、兩個社群間的相似指標，何者會有較佳的分類正確性。本文考慮的指標包括單一社群的熵及Simpson指標、兩社群間的熵及相似指標(Yue and Clayton, 2005)、支持向量機(Support Vector Machine)、邏輯斯迴歸等方法，透過電腦模擬及交叉驗證(cross-validation)比較方法的優劣。本文發現單一社群熵指標之表現，在本文的模擬研究有不錯的分類結果，甚至普遍優於支持向量機，但單一社群熵指標分類法的結果並不穩定，為該分類方法之主要缺點。 / Since computer science had changed rapidly, the worldwide web made it much easier to share and receive the information. Search engines would be the ones to help us find the target information conveniently. The famous Google was also founded by the search engine. The searching process is always depends on the characteristics of the web pages, for example, entropy is one of the characteristics index. The target web pages could be found by combining the index with the keywords information given by user. Or in other words, it is to find out the web pages which are the most similar to the user’s demands. In biology and ecology, similarity index function is commonly used for classification problems. But in practice, the pairwise instead of single similarity would be obtained to check if two communities are similar or not. It is dislike the thinking of search engines. This research is to find out which has better classification result between single index and pairwise index for the data which is multinomial distributed, especially distributed like a geometry distribution. This data assumption is often satisfied in ecology area. The following classification methods would be considered into this research: single index including entropy and Simpson index, pairwise index including pairwise entropy and similarity index (Yue and Clayton, 2005), and also support vector machine and logistic regression. Computer simulations and cross validations would also be considered here. In this research, it is found that the single index, entropy, has good classification result than imagine. Sometime using entropy to classify would even better than using support vector machine with raw data. But using entropy to classify is not very robust, it is the one needed to be improved in future.

We estimate the willingness to pay for electricity generated from renewable energy in the Czech Republic. Discrete choice experiment is used to elicit preferences for various attributes of renewable electricity support scheme (PM emission, GHG emission, size of RE power plant, revenue distribution, and costs). Original survey is carried with 404 respondents living in two regions - Ustecky (polluted area) and Southern Bohemia (cleaner area). We find that respondents prefer decentralized renewable electricity sources over centralized, local air quality improvements over reduction in greenhouse gas emissions. Estimated marginal willingness to pay for 1% reduction in emission of particulate matter equals to 49 CZK, respectively 3.7 % of average monthly electricity bill. In total, WTP for green electricity is larger than current compulsory contributions to renewable energy support scheme. Powered by TCPDF (www.tcpdf.org)

Doctor of Philosophy / Department of Physics / Dean Zollman / As part of a collaborative study of the science preparation of elementary school teachers, we investigated the quality of students’ reasoning and explored the relationship between sophistication of reasoning and the degree to which the courses were considered inquiry oriented. To probe students’ reasoning, we developed open-ended written content questions with the distinguishing feature of applying recently learned concepts in a new context. We devised a protocol for developing written content questions that provided a common structure for probing and classifying students’ sophistication level of reasoning. In designing our protocol, we considered several distinct criteria, and classified students’ responses based on their performance for each criterion. First, we classified concepts into three types: Descriptive, Hypothetical, and Theoretical and categorized the abstraction levels of the responses in terms of the types of concepts and the inter-relationship between the concepts. Second, we devised a rubric based on Bloom’s revised taxonomy with seven traits (both knowledge types and cognitive processes) and a defined set of criteria to evaluate each trait. Along with analyzing students’ reasoning, we visited universities and observed the courses in which the students were enrolled. We used the Reformed Teaching Observation Protocol (RTOP) to rank the courses with respect to characteristics that are valued for the inquiry courses. We conducted logistic regression for a sample of 18 courses with about 900 students and reported the results for performing logistic regression to estimate the relationship between traits of reasoning and RTOP score. In addition, we analyzed conceptual structure of students’ responses, based on conceptual classification schemes, and clustered students’ responses into six categories. We derived regression model, to estimate the relationship between the sophistication of the categories of conceptual structure and RTOP scores. However, the outcome variable with six categories required a more complicated regression model, known as multinomial logistic regression, generalized from binary logistic regression. With the large amount of collected data, we found that the likelihood of the higher cognitive processes were in favor of classes with higher measures on inquiry. However, the usage of more abstract concepts with higher order conceptual structures was less prevalent in higher RTOP courses.

住宅選擇是每一個家戶都會面臨到的問題，過去文獻發現購屋者先選擇租屋或購屋，若決定購屋，則先決定於何區位購屋，然後再決定購買何種房屋類型之房屋，然而卻未曾提及購屋者於不同市場類型住宅間之選擇。預售屋、新成屋以及中古屋等不同市場類型之住宅，各自隱含不同的效用及風險，影響著購屋者之選擇，因此本研究試圖討論購屋者於不同市場類型住宅間之選擇與偏好。 本研究採用內政部營建署「住宅需求動向調查」資料，利用混合多項羅吉特模型探討不同限制條件下，預售屋、新成屋與中古屋之個體選擇行為。實證結果顯示，投資者較偏好於購買知覺風險較高之預售屋，期待以高知覺風險換取高的報酬；教育程度較高者，因對居住品質要求愈高，因此傾向於選擇設備新穎之預售屋與新成屋；家戶平均月所得較高之購屋者，負擔能力較高，因此選擇總價較高之預售屋機率較高，其次為新成屋。此外，搜尋頻率愈高者，選擇預售屋之機率愈高，因預售屋無實體存在，預售屋購屋者為降低其知覺風險，將花費更高之搜尋成本。在價格彈性分析部分，實證結果顯示預售屋之競爭力最高，但預售屋之受衝擊力亦最高，而中古屋之競爭力於三種市場類型中居次，但中古屋衝擊力最小，因此，當單價屬性發生變動時，較不影響中古屋購屋者之選擇，但卻大幅影響預售屋購屋者之選擇機率。 / Every household would face housing choice, the past housing choice study founded that households decided tenure choice first, if they decides to buy a house, they first decided on what location, and then decided what type of housing to buy, but it has not been mentioned the housing choice among different residential market types. Pre-sale houses, newly constructed houses and existing houses implied different effectiveness and risks, affecting the choice of homebuyers. This article tried to discuss homeowners’ choice among different residential market types. This study use Construction and Planning Agency, "Housing Demand Survey of 2009" data, use mixed multinomial logit model, investigated under different constraints, housing choice behavior among pre-sale houses, newly constructed houses and existing houses. The empirical results showed that investors prefer higher perceived risk in buying pre-sale housing, looking for a high perceived risk and high rewards; higher education level, due to the higher quality requirements for living, so they preferred pre-sale houses and newly constructed houses. Homebuyers which have higher average monthly household income, have more affordable ability, so the probability of choosing pre-sale houses are much higher, followed by the newly constructed houses. In addition, the higher search frequency were more likely to choose pre-sale houses because pre-sale houses for sale no physical presence, pre-sale housing homebuyers in order to reduce their perceived risk, would spend more search costs. In the price elasticity analysis, empirical results showed that the pre-sale houses had the highest competitiveness, but the impact force was also the highest, while the existing houses market, the competitiveness of the third types was the second place, and the competitiveness of the existing houses was the smallest. Thus, when a change in unit price attribute, does not affect existing houses homebuyers, but significantly affected the choice probability of pre-sale houses homebuyers.