Autonomous Vehicles (AVs) are computer equipped vehicles that can operate without human driver’s active control using information provided by their sensors about the surrounding environment. Self-driving vehicles may have seemed to be a distant dream several years ago, but manufactures’ prototypes showed that AVs are becoming real now. Several car manufactures (i.e. Benz, Audi, etc.) and information technology firms (i.e. Google) have either showcased their fully AVs or announced their robot cars to be released in a few years. AVs hold the promise to transform the ways we live and travel. Although several studies have been conducted on the impacts of AVs, much remains to be explored regarding the various ways in which AVs could reshape our lifestyle. This dissertation addresses the knowledge gap in understanding the potential implications of AV technologies on travel behavior and system modeling. A comprehensive review of literature regarding AV adoption, potential impacts and system modeling was provided. Bass diffusion models were developed to investigate the market penetration process of AVs based on experience learned from past technologies. A stated preference survey was conducted to gather information from university population on the perceptions and attitudes toward AV technologies. The data collected from the Florida International University (FIU) was used to develop econometric models exploring the willingness to pay and relocation choices of travelers in light of the new technologies. In addition, the latest version of the Southeast Planning Regional Model (SERPM) 7.0, an Activity-Based Model (ABM), was employed to examine the potential impacts of AVs on the transportation network. Three scenarios were developed for short-term (2035), mid-term (2045) and long-term (2055) conditions. This dissertation provides a systematic approach to understand the potential implications of AV technologies on travel behavior and system modeling. The results of the survey data analysis and the scenario analysis also provide important inputs to guide planning and policy analysis on the impacts of AV technologies.

<p>This research explores the impact of road infrastructure on deforestation, the role of forest governance and a carbon tax/credit mechanism in mitigating the effect on land use change and subsequent GHG emissions, with application to the states of Pará and Mato Grosso in Brazil. Few studies have addressed how policies to protect forested land affect the rate of deforestation associated with road and infrastructure improvement. This research makes three main contributions to the literature of roads and deforestation: 1) the concept of cost of access to the “closest” market in terms of time (expressed in person hours per ten ton load) is introduced to reflect variations in the road network infrastructure; 2) development of empirical evidence of the role of forest governance in diminishing the rate of deforestation linked to roads, using data from Brazil; and 3) and assessment of the efficacy of a carbon tax/credit scheme for mitigating the impact of infrastructure investment on land use and resultant changes in GHG emissions. Access cost ranged between 0.01 and 3084 person hours per load, however 80 percent of the pixels measured less than 784 person hours across the three years analyzed (2003, 2013, and 2018). This measure facilitated a contrast in spatial accessibility due to road infrastructure across pixels within the same year and across years on a same pixel. The use of a fractional logit model allowed the incorporation of proportions of different land uses within a same pixel at the same resolution of other <a></a>variables not available at the same fine scale. Strong forest governance reduced up to 25% the elasticities on forest lands with respect to access cost; in other words, the impact of roads on deforestation is reduced by one fourth when forest governance is strengthened. These larger impacts occur at the frontier where most of the efforts need to be addressed. Finally, provided a shock in road infrastructure, a carbon tax/credit level of $82/tCO2e permitted to abate an additional amount of GHG emissions estimated in 244 million tons of CO2e released due to changes in carbon stocks and flow emissions from agricultural activities induced from changes in road infrastructure. More importantly, this research provided insights of a proportion of GHG emissions that could be abated at different levels of a carbon tax/credit.</p>

綜觀台灣過去數十年來的產業經濟發展歷程，在國際間有許多正面的評價，稱之為「台灣經濟奇蹟」。在政府政策致力於塑造有利於科技產業發展的環境下，製造業為台灣產業帶來重大的貢獻，當我國不同的產業類別之製造業發揮其優勢競爭力的同時，亦使得台灣地區北、中、南三大區域發展出各自獨特且具有潛力的製造業特性。 事實上，區域產業特性與賦能之優劣將可透過區域中廠商與行動者的活動來表現，尤其是區域中的模範角色，是檢視區域中有無可學習的模範者和模仿其整體投資階段過程的重要指標，對於區域內外企業進駐和設立工廠的組織決策相當重要（Bandura, 1986; van Praag, 1996; Kriegesmann, 1999; Fornahl, 2003）。這樣的仿效行為將指向區域中旗艦廠商投資設廠區位選擇與組織決策行動，換句話說，旗艦廠商（flagship firm）是區域發展中重要的模範角色（role model）（Fornahl, 2003），而其區位選擇與組織決策行為對於區域發展將佔有舉足輕重的地位。另一方面，過去傳統投資區位選擇的討論，多論及單一廠商設立工廠在地理空間的決策行為。然而本研究認為旗艦廠商多工型工廠（multiplant）的特性在組織決策上的區位選擇行為，面對各區域經濟與產業發展特性的差異，應有不同於過去文獻與理論的考量。 是故，本研究透過天下雜誌公佈台灣地區2007年製造業1000大廠商，聚焦台灣地區製造業旗艦廠商面對投資母國區位選擇的考量，透過旗艦廠商在台投資多工型工廠歷程決策之整體分析，藉由投資時間與空間區位，以及廠商組織型態面向的討論，了解台灣地區旗艦廠商投資多工型工廠於母國區位選擇之因素。透過多項羅吉特模型的實證研究，本研究歸納研究成果，提出下列政策建議： 一、推動企業總公司在地化發展，有助區域內旗艦廠商再投資母國之活動 二、透過旗艦廠商在區域內之投資經驗，引導再投資活動，以利區域發展 三、區域內產業特性強化與升級，以加強區域高科技產業支撐力 四、科學工業園區與區域內科技基礎產業優勢分進合擊，提升產業關聯性 五、提升區域總體經濟發展，有助於吸引區域內高科技產業再投資之活動

Apresenta-se um procedimento para estimativa da matriz origem-destino (O/D) a partir de um modelo de escolha discreta em combinação com uma matriz O/D de transporte público, a qual pode ser estimada de maneira relativamente fácil e rápida. O modelo de escolha discreta utilizado para realizar a divisão modal é o modelo logit multinomial. A calibração do modelo logit é realizada com base em 505 observações e considerando três situações: a população de viajantes sem segmentação, com segmentação segundo posse de automóvel no domicílio, e com segmentação segundo a distância de viagem. Analisa-se a precisão da estimativa do número de viagens quando as zonas de controle que formam a área de estudo são divididas segundo o critério de distância de acesso às linhas de ônibus. Na verificação realizada são empregados dados obtidos da pesquisa domiciliar realizada na cidade de Bauru, SP. Os erros cometidos na estimativa são medidos e comparados com os resultados obtidos na pesquisa domiciliar sem expansão. O trabalho demostrou experimentalmente que o procedimento proposto é uma alternativa viável para se obter a distribuição de viagens e, portanto, para determinar a matriz O/D. / A procedure is presented for estimating an origin-destination (O/D) matrix using a discrete choice model jointly with a public transport O/D matrix; the latter being relatively easy and fast to obtain. A multinomial logit discrete choice model is used to determine the mode split of travelers among several available modes. The model is estimated using a sample of 505 observations chosen from a household 0/D survey in the city of Bauru, state of Sao Paulo. This survey is used throughout to verify the results of the proposed methodology. Three different models are considered: one without segmentation of the population, one with segmentation according to car ownership and one with segmentation according to travel distance. An examination is made of the changes on the level of precision of the number of trips estimated with the division of the control zones that conform the study area; the aforementioned division was carried out on the basis of an access distance to the bus network criterion. The estimation errors are measured and compared to the unexpanded O/D survey results. The research shows empirically that the proposed procedure can be used to obtain the distribution of trips and hence to determine an aggregate O/D matrix.

Apresenta-se um procedimento para estimativa da matriz origem-destino (O/D) a partir de um modelo de escolha discreta em combinação com uma matriz O/D de transporte público, a qual pode ser estimada de maneira relativamente fácil e rápida. O modelo de escolha discreta utilizado para realizar a divisão modal é o modelo logit multinomial. A calibração do modelo logit é realizada com base em 505 observações e considerando três situações: a população de viajantes sem segmentação, com segmentação segundo posse de automóvel no domicílio, e com segmentação segundo a distância de viagem. Analisa-se a precisão da estimativa do número de viagens quando as zonas de controle que formam a área de estudo são divididas segundo o critério de distância de acesso às linhas de ônibus. Na verificação realizada são empregados dados obtidos da pesquisa domiciliar realizada na cidade de Bauru, SP. Os erros cometidos na estimativa são medidos e comparados com os resultados obtidos na pesquisa domiciliar sem expansão. O trabalho demostrou experimentalmente que o procedimento proposto é uma alternativa viável para se obter a distribuição de viagens e, portanto, para determinar a matriz O/D. / A procedure is presented for estimating an origin-destination (O/D) matrix using a discrete choice model jointly with a public transport O/D matrix; the latter being relatively easy and fast to obtain. A multinomial logit discrete choice model is used to determine the mode split of travelers among several available modes. The model is estimated using a sample of 505 observations chosen from a household 0/D survey in the city of Bauru, state of Sao Paulo. This survey is used throughout to verify the results of the proposed methodology. Three different models are considered: one without segmentation of the population, one with segmentation according to car ownership and one with segmentation according to travel distance. An examination is made of the changes on the level of precision of the number of trips estimated with the division of the control zones that conform the study area; the aforementioned division was carried out on the basis of an access distance to the bus network criterion. The estimation errors are measured and compared to the unexpanded O/D survey results. The research shows empirically that the proposed procedure can be used to obtain the distribution of trips and hence to determine an aggregate O/D matrix.

股利政策之選擇攸關公司資金成本效益與投資者權益，投資者依據公司未來股利發生時點、金額與不確定性等因素來判斷公司的內涵價值（intrinsic value），亦即股利的配發是公司管理當局與股東利益間的抵換。本論文將考量我國企業實務上股利支付內容，將股利形式細分為無股利分配及有股利配發，包含單一股利政策－現金股利、盈餘配股、公積配股，複合股利政策－現金股利搭配盈餘配股、現金股利搭配公積配股、盈餘配股搭配公積配股及綜合股利政策（現金股利、盈餘配股、公積配股三者兼發）之發放模式，共計有八種股利支付類型。 　　共同基金乃證券市場中連接有價證券與投資者間之專業投資管道，由於基金收益直接進行再投資產生之租稅規避濾網（tax-free filter）效果，藉以探討公司選擇股利政策時之顧客效應。同時將影響股利政策制定之構面因素－公司的股利配發來源、獲利性狀況、資金融通與債務償還能力、企業未來投資成長機會、產業類別與租稅改革制度等一併納入分析檢證。 　　本論文採用實證模型分析為主，文獻檢閱分析為輔，以1995- 2000年我國上市公司曾被國內股票型投信基金選為投資標的者，運用Mutinomial logit model及股利政策相關文獻，建構本研究實證模型；在政策意涵上，股利稅制的規劃與共同基金租稅課徵問題亦一併討論，以檢討我國現行稅制環境是否健全。 　　本研究所獲結論歸納如下： 　　(1) 共同基金持股比重變動對股利政策影響存在明顯差異 　　　　股票型基金持股比重大小對現金股利搭配盈餘配股與盈餘配股搭配公積配股選擇皆有顯著正效果，但是否基於股東特性而論則難以斷定。現行我國共同基金持股率占公司股權結構比重不大、透過除權日規避股利發放及大股東利用共同基金進行投信交叉持股等問題，皆是造成實證結果與預期相左之可能原因。 　　(2) 租稅制度改革對股利配發內容的選擇扮演要角 　　　　兩稅合一制度實施後，總股利支付率較實施前為高，主要在於規避保留盈餘加徵10﹪的稅負及股利的發放具有抵稅權，使公司不再傾向保留盈餘而提高發放股利之意願。 　　(3) 股利配發形式選擇 　　　　◎支持股利配發來源與股利政策的關係 　　　　◎公司舉債程度高低與現金股利配發機率呈負相關 　　　　◎公司獲利性狀況攸關股利發放與否 　　　　◎未來成長機會與現金股利配發呈反向關係 　　　　◎股利政策具有產業差異性，高科技產業傾向選擇股票股利偏 高之股利組合政策 　　以公司立場而言，宜需定期評估經營利基與股利政策選擇因素，建立一套合理之股利預測分析模式，並考量平衡股利政策（現金股利與股票股利搭配）模式，以減緩我國企業普遍採股票股利配發造成股權過度膨脹及盈餘稀釋之弊。從投資者角度來看，共同基金係專業化投資管道，藉由大眾資金匯集之規模效益，能使資金有效配置（或風險分散）並提升國內專業法人比重，減緩市場投機風氣。然共同基金投資相關法令之設立仍需更臻完備，稅制環境對公司股利決策具重要影響性，對追求租稅中立目標而言，證券交易所得是否復徵所得稅，亦是政府單位需審慎考量的議題。

This study uses conjoint analysis to examine the preferences of buyers for Bureau of Land Management (BLM) wild horses based on physical attributes of wild horses and individual characteristics of the buyers. Generalized ordered logit models and multinomial logit models are used to study the impact of the buyers’ demographic characteristics such as age, gender, knowledge about wild horse care, and number of wild horses previously adopted on physical attributes of the horses such as color, age, height, training status, temperament, conformation, and unique markings. Using a choice experiment, taken together, these attributes determine buyer’s preferences for a wild horse. This study reveals that characteristics of buyers have significant effects on their preferences for wild horses. Their gender, age, knowledge about wild horse care, and the number of horses previously adopted influence the importance that buyers place on physical attributes of a wild horse in their decision to purchase a wild horse.

Les crises monétaires sont les premières crises financières de l'histoire économique. Elles se traduisent par l'élimination ou la substitution des monnaies nationales. L'objectif de cette thèse est de poser les fondements théoriques et empiriques des crises monétaires, mais également de proposer un cadre de prévention de ce type de crise qui sévit en zone euro depuis 2008. Les débats économiques et politiques actuels autour des questions de désintégration monétaire témoignent de la persistance et de l'ampleur de la crise, où la légitimité et la souveraineté de la monnaie unique est menacée à moyen long terme. Les divers plans de sauvetage et les politiques d'austérité dans les pays-membres en difficulté ne sont que les conséquences et les coûts directs d'une telle crise. Ces derniers doivent interpeller les autorités de supervision à une plus grande vigilance, ainsi qu'à une politique de prévention plus avisée. L'objectif de cette thèse est double : dans une première partie, nous analysons le concept, les fondements historiques et théoriques des crises monétaires, puis dressons une typologie de celles-ci. Dans une seconde partie, nous apportons une contribution empirique relative aux déterminants des crises monétaires en zone euro et proposons un outil de prévention des crises monétaires, grâce à la mise en place d'un système d'alerte avancée (Early Warning System), par l'approche économétrique de type logit multinomial. Pour ce faire, la détection et la mesure des mésalignements des taux de change réels à l'intérieur de la zone euro est cruciale, puisqu'il constitue l'indicateur premier des crises monétaires. L'estimation des taux de change d'équilibre permettent ainsi d'apprécier la sur ou sous-évaluation des monnaies, indispensable à la mise en place d'un système d'alerte avancée, à des fins de prévention des crises monétaires. / Monetary crises are the first financial crises in economic history, which result in the elimination or substitution of national currencies. The aim of this thesis is to study the theoretical and empirical foundations of monetary crises. Furthermore, a framework for the prevention of such crises, raging in the Euro zone since 2008, is provided. The current economic and political debates about this issue reflect the persistence and the extent of this crisis, in which the Euro's legitimacy and sovereignty is threatened in the medium term. The various rescue plans and austerity policies in troubled member states are direct consequences and costs of this crisis. Consequently, supervisory authorities need to be more vigilant in strengthening their prevention policy. The purpose of this thesis is twofold: in the first part, we analyze the concept, the historical and theoretical foundations of monetary crises, and then develop a typology of them. In the second part, we provide an empirical contribution on the determinants of monetary crises in the euro area and propose a tool for preventing currency crises by setting up an Early Warning System, through the econometric approach of the multinomial logit model. As the primary indicator of monetary crises, the detection and measurement of real exchange rate misalignments within the euro area is decisive. The equilibrium exchange rates estimation allows the assessment of currency over- or undervaluation, which is essential for the implementation of an early warning system

Artificial Intelligence in form of Machine Learning classifiers is increasingly applied for travel choice modeling issues and therefore constitutes a promising, competitive alternative towards conventional discrete choice models like the Logit approach. In comparison to traditional theory-based models, data-driven Machine Learning generally shows powerful predictive performance, but often lacks in model interpretability, i.e., the provision of comprehensible explanations of individual decision behavior. Consequently, the question about which approach is superior remains unanswered. Thus, this paper performs an in-depth comparison between benchmark Logit models and Artificial Neural Networks and Decision Trees representing two popular algorithms of Artificial Intelligence. The primary focus of the analysis is on the models’ prediction performance and its ability to provide reasonable economic behavioral information such as the value of travel time and demand elasticities. For this purpose, I use crossvalidation and extract behavioral indicators numerically from Machine Learning models by means of post-hoc sensitivity analysis. All models are specified and estimated on synthetic and empirical data. As the results show, Neural Networks provide plausible aggregate value of time and elasticity measures, even though their values are in different regions as those of the Logit models. The simple Classification Tree algorithm, however, appears unsuitable for the applied computation procedure of these indicators, although it provides reasonable interpretable decision rules for travel choice behavior. Consistent with the literature, both Machine Learning methods achieve strong overall predictive performance and therefore outperform the Logit models in this regard. Finally, there is no clear indication of which approach is superior. Rather, there seems to be a methodological tradeoff between Artificial Intelligence and discrete choice models depending on the underlying modeling objective.

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