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Climate change vulnerability and coping mechanisms among farming communities in Northern GhanaNti, Frank Kyekyeku January 1900 (has links)
Master of Science / Department of Agricultural Economics / Andrew Barkley / This study examines the effect of extreme climatic conditions (drought, flood, and bushfires) on the livelihood of households in the Bawku West district of Ghana. The research identified the mechanisms with which households cope in such situations, and analyzed factors influencing the adoption of coping strategies for flood, coping strategies for drought, and coping strategies for bushfires. Data for the study were collected in selected villages across the district in the aftermath of the 2007/2008 extreme climatic events (a prolonged drought period followed by an erratic rainfall). A binary logit regression (BLR) model was then specified to estimate factors that influence the adoption of a given coping mechanisms. Results from the BLR model indicate that literacy level, membership with an FBO, household income, and location of households had positive and significant impacts on adaptation to drought. Similarly, source of seeds for planting, membership with an FBO, household income, and farm size had positive significant influence on adaptation to flood. Adaption to bushfire was positively influenced by radio ownership, seed source and income. The main effect of these climatic extreme events on households included destruction of crops, livestock and buildings; food and water shortage; poor yield or harvest and limited fields for livestock grazing. Therefore, government policies should be geared towards creating revenue generating channels and in strengthening institutions that provide access to farm credit, readily available improve seeds and extension. Additionally, policies that expedite information dissemination through radio and other public media will enhance households’ adaptive capacity.
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Characteristics and contributory causes related to large truck crashes (phase-II) - all crashesKotikalapudi, Siddhartha January 1900 (has links)
Master of Science / Department of Civil Engineering / Sunanda Dissanayake / In order to improve safety of the overall surface transportation system, each of the critical areas needs to be addressed separately with more focused attention. Statistics clearly show that large-truck crashes contribute significantly to an increased percentage of high-severity crashes. It is therefore important for the highway safety community to identify characteristics and contributory causes related to large-truck crashes. During the first phase of this study, fatal crash data from the Fatality Analysis Reporting System (FARS) database were studied to achieve that objective. In this second phase, truck-crashes of all severity levels were analyzed with the intention of understanding characteristics and contributory causes, and identifying factors contributing to increased severity of truck-crashes, which could not be achieved by analyzing fatal crashes alone. Various statistical methodologies such as cross-classification analysis and severity models were developed using Kansas crash data. Various driver-, road-, environment- and vehicle- related characteristics were identified and contributory causes were analyzed.
From the cross-classification analysis, severity of truck-crashes was found to be related with variables such as road surface (type, character and condition), accident class, collision type, driver- and environment-related contributory causes, traffic-control type, truck-maneuver, crash location, speed limit, light and weather conditions, time of day, functional class, lane class, and Average Annual Daily Traffic (AADT). Other variables such as age of truck driver, day of the week, gender of truck-driver, pedestrian- and truck-related contributory causes were found to have no relationship with crash severity of large trucks. Furthermore, driver-related contributory causes were found to be more common than any other type of contributory cause for the occurrence of truck-crashes. Failing to give time and attention, being too fast for existing conditions, and failing to yield right of way were the most dominant truck-driver-related contributory causes, among many others.
Through the severity modeling, factors such as truck-driver-related contributory cause, accident class, manner of collision, truck-driver under the influence of alcohol, truck maneuver, traffic control device, surface condition, truck-driver being too fast for existing conditions, truck-driver being trapped, damage to the truck, light conditions, etc. were found to be significantly related with increased severity of truck-crashes. Truck-driver being trapped had the highest odds of contributing to a more severe crash with a value of 82.81 followed by the collision resulting in damage to the truck, which had 3.05 times higher odds of increasing the severity of truck-crashes. Truck-driver under the influence of alcohol had 2.66 times higher odds of contributing to a more severe crash.
Besides traditional practices like providing adequate traffic signs, ensuring proper lane markings, provision of rumble strips and elevated medians, use of technology to develop and implement intelligent countermeasures were recommended. These include Automated Truck Rollover Warning System to mitigate truck-crashes involving rollovers, Lane Drift Warning Systems (LDWS) to prevent run-off-road collisions, Speed Limiters (SLs) to control the speed of the truck, connecting vehicle technologies like Vehicle-to-Vehicle (V2V) integration system to prevent head-on collisions etc., among many others. Proper development and implementation of these countermeasures in a cost effective manner will help mitigate the number and severity of truck-crashes, thereby improving the overall safety of the transportation system.
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How to make the most of open data? A travel demand and supply model for regional bicycle paths / Hur får man ut det mesta av öppna data? En modell för utbud och efterfrågan för planering av regionala cykelvägarCazor, Laurent January 2021 (has links)
Detta examensarbete syftar till att svara på ett av Trafikverket fastställt problem: en gemensam regional cykelplanerings process skulle göra dem billigare och mer jämförbara. De erbjuder för närvarande planerarna en modell som utvecklades av Kågeson 2007. Denna modell har formen av en rapport som ger råd om när man ska bygga en cykelväg mellan städer eller platser i en region. Ändå används den bara i endast 6 av de 21 svenska länen. Trafikverket kräver ett nytt planeringsstödverktyg, mer interaktivt och komplett än Kågeson-modellen. Några nya önskade funktioner är separationen av efterfrågan per syfte, införandet av e-cyklar, olika resesyfte och en prioritering av investeringarna. Examensarbetet är att designa och implementera det här verktyget, även kallat Planning Support System (PSS), som syftar till att jämföra utbud och efterfrågan på cykelväg till prioritering av infrastrukturförbättringar. En huvudbegränsning för modellen är att den måste vara billig datavis, men så komplett och exakt som möjligt. Det baseras på flera öppna dataleverantörer, till exempel OpenStreetMap, den svenska nationella vägdatabasen (NVDB) eller reseundersökningar från Sverige och Nederländerna. Resultatet är en modell, uppdelad efter turändamål och typ av cykel. Del för efterfrågeuppskattning anpassar en klassisk fyrsteg transportmodell till cykelplanering och begränsad data. För olika resändamål genereras och distribueras resor tack vare en ursprungs begränsad gravitationsmodell. Valet av cykelläge är anpassat till det faktiska resebeteendet genom logistisk regression med en binär logit-modell. Resorna tilldelas sedan nätverket med tilldelnings metoden "allt-eller-ingenting" genom Dijkstras algoritm. För att utvärdera cykelförsörjningen använde vi ett mått som heter Level of Traffic Stress (LTS), som uppskattar den potentiella användningen av en nätverkslänk för olika delar av befolkningen som en funktion av vägnätvariablerna. Prioriteringsrankningen är då förhållandet mellan mått på efterfrågan och utbud. Detta nya verktyg implementeras med opensource Geographic Information System (GIS) som heter QGIS och med Python 3 och testas i Södermanlands län / This Master Thesis main objective is to answer a problem set by the Swedish Transport Administration: a common regional bicycle planning process would them cheaper and more comparable. They currently offer the planners a model developed by Kågeson in 2007. This model takes the form of a report which advises on when to build a bicycle path between cities or places of a region. Still, it is only used in only 6 of the 21 Swedish counties. Trafikverket requires a new planning support tool, more interactive and complete than the Kågeson model. Some new desired features are the separation of demand per purpose, the inclusion of e-bikes, different trip purposes, and a prioritization of the investments. The Degree Project work is to design and implement this tool, also called Planning Support System (PSS), which compares supply and demand for bicycle path to prioritizing infrastructure improvements. A main constraint for the model is that it needs to be cheap data-wise, but as complete and precise as possible. It bases on several open data providers, such as OpenStreetMap, the Swedish National Road Database (NVDB), or Travel Surveys from Sweden and the Netherlands. The result is a model, disaggregated by trip purpose and type of bicycle. The demand estimation part adapts a classic four-step transportation model to bicycle planning and limited data. For different trip purposes, trips are generated and distributed thanks to an origin-constrained gravity model. Bicycle mode choice is fit to actual travel behaviour through logistic regression with a binary logit model. The trips are then assigned to the network using the "all-or-nothing" assignment method through the Dijkstra algorithm. To evaluate bicycle supply, we used a metric called Level of Traffic Stress (LTS), which estimates the potential use of a network link by different parts of the population as a function of the road network variables. The prioritization ranking is then the ratio between demand and supply metrics. This new tool is implemented with the opensource Geographic Information System (GIS) called QGIS and with Python 3, and it is tested on Södermanland County.
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Evaluating The Impact Of Oocea's Dynamic Message Signs (dms) On Travelers' Experience Using A Pre And Post-deployment SurveyFlick, Jason 01 January 2008 (has links)
The purpose of this thesis was to evaluate the impact of dynamic message signs (DMS) on the Orlando-Orange County Expressway Authority (OOCEA) toll road network using a Pre and Post-Deployment DMS Survey (henceforth referred to as "pre and post-deployment survey") analysis. DMS are electronic traffic signs used on roadways to give travelers information about travel times, traffic congestion, accidents, disabled vehicles, AMBER alerts, and special events. The particular DMS referred to in this study are large rectangular signs installed over the travel lanes and these are not the portable trailer mount signs. The OOCEA have been working over the past two years to add several fixed DMS on their toll road network. At the time of the pre-deployment survey, only one DMS was installed on the OOCEA toll road network. At the time of the post-deployment survey, a total of 30 DMS were up and running on the OOCEA toll road network. Since most of the travelers on the OOCEA toll roads are from Orange, Osceola, and Seminole counties, this study was limited to these counties. This thesis documents the results and comparisons between the pre and post-deployment survey analysis. The instrument used to analyze the travelers' perception of DMS was a survey that utilized computer aided telephone interviews. The pre-deployment survey was conducted during early November of 2006, and the post-deployment survey was conducted during the month of May, 2008. Questions pertaining to the acknowledgement of DMS on the OOCEA toll roads, satisfaction with travel information provided on the network, formatting of the messages, satisfaction with different types of messages, diversion questions (Revealed and Stated preferences), and classification/socioeconomic questions (such as age, education, most traveled toll road, county of residence, and length of residency) were asked to the respondents. The results of both the pre and post-deployment surveys are discussed in this thesis, but it should be noted that the more telling results are those of the post-deployment survey. The results of the post-deployment survey show the complete picture of the impact of DMS on travelers' experience on the OOCEA toll road network. The pre-deployment results are included to show an increase or decrease in certain aspects of travel experience with relation to DMS. The results of the pre-deployment analysis showed that 54.4% of the OOCEA travelers recalled seeing DMS on the network, while a total of 63.93% of the OOCEA travelers recalled seeing DMS during the post-deployment analysis. This showed an increase of almost 10% between the two surveys demonstrating the people are becoming more aware of DMS on the OOCEA toll road network. The respondents commonly agreed that the DMS were helpful for providing information about hazardous conditions, and that the DMS are easy to read. Also, upon further research it was found that between the pre and post-deployment surveys the travelers' satisfaction with special event information provided on DMS and travel time accuracy on DMS increased significantly. With respect to formatting of the DMS, the following methods were preferred by the majority of respondents in both the pre and post-deployment surveys: ● Steady Message as a default DMS message format ● Flashing Message for abnormal traffic information (94% of respondents would like to be notified of abnormal traffic information) ● State road number to show which roadway (for Colonial - SR 50, Semoran - SR 436 and Alafaya - SR 434) ● "I-Drive" is a good abbreviation for International Drive ● If the distance to the international airport is shown on a DMS it thought to be the distance to the airport exit The results from the binary logit model for "satisfaction with travel information provided on OOCEA toll road network" displayed the significant variables that explained the likelihood of the traveler being satisfied. This satisfaction model was based on respondents who showed a prior knowledge of DMS on OOCEA toll roads. With the use of a pooled model (satisfaction model with a total of 1775 responses - 816 from pre-deployment and 959 from post-deployment), it was shown that there was no statistical change between the pre and post-deployment satisfaction based on variables thought to be theoretically relevant. The results from the comparison between the pre and post-deployment satisfaction models showed that many of the coefficients of the variables showed a significant change. Although some of the variables were statistically insignificant in one of the two survey model results: Either the pre or post-deployment model, it was still shown that every variable was significant in at least one of the two models. The coefficient for the variable corresponding to DMS accuracy showed a significantly lower value in the post-deployment model. The coefficient for the variable "DMS was helpful for providing special event information" showed a significantly higher value in the post-deployment model. The final post-deployment diversion model was based on a total of 732 responses who answered that they had experienced congestion in the past 6 months. Based on this final post-deployment diversion model, travelers who had stated that their most frequently traveled toll road was either SR 408 or SR 417 were more likely to divert. Also, travelers who stated that they would divert in the case of abnormal travel times displayed on DMS or stated that a DMS influenced their response to congestion showed a higher likelihood of diversion. These two variables were added between the pre and post-deployment surveys. It is also beneficial to note that travelers who stated they would divert in a fictitious congestion situation of at least 30 minutes of delay were more likely to divert. This shows that they do not contradict themselves in their responses to Revealed Preference and Stated Preference diversion situations. Based on a comparison between pre and post-deployment models containing similar variables, commuters were more likely to stay on the toll road everything else being equal to the base case. Also, it was shown that in the post-deployment model the respondents traveling on SR 408 and SR 417 were more likely to divert, but in the pre-deployment model only the respondents traveling on SR 408 were more likely to divert. This is an expected result since during the pre-deployment survey only one DMS was located on SR 408, and during the post-deployment survey there were DMS located on all toll roads. Also, an interesting result to be noted is that in the post-deployment survey, commuters who paid tolls with E-pass were more likely to stay on the toll road than commuters who paid tolls with cash. The implications for implementation of these results are discussed in this thesis. DMS should be formatted as a flashing message for abnormal traffic situations and the state road number should be used to identify a roadway. DMS messages should pertain to information on roadway hazards when necessary because it was found that travelers find it important to be informed on events that are related to their personal safety. The travel time accuracy on DMS was shown to be significant for traveler information satisfaction because if the travelers observe inaccurate travel times on DMS, they may not trust the validity of future messages. Finally, it is important to meet the travelers' preferences and concerns for DMS.
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投資型購屋者機率預測模型之建立 / The Probability predictive model of housing investors邱于修, Chiou,Yu Shiou Unknown Date (has links)
住宅為兼具消費及投資之雙重功能財貨,因此若從購屋動機劃分購屋族群,可以分為自住者及投資者,近年來受到國內房市呈現生氣蓬勃之景象及利率持續走低等總體經濟因素影響之下,出現越來越多以投資為主要目的之投資型購屋者,對於金融機構之購屋貸款業務來說,投資者之還款行為相較於自住者是比較不穩定的。故本文之研究目的即藉由探討自住者及投資者之購屋特徵異同,建立投資者之機率預測模型,預測某購屋者成為投資者之機率,提供一較為客觀之機率預測模型,供作金融機構放貸參考準則。接著進一步探討在不同機率界限(cutoff point)下之預測準確率,找出預測準確率最高之機率界限值,提高本模型之預測準確度;並探討金融機構在不同經營方針下之較適機率界限值。 / 本文使用台灣住宅需求動向季報之已購屋者問卷,建立二元羅吉特模型。研究結果顯示,區位在中心都市、高單價、小面積產品及大面積產品、預售屋及拍賣屋市場屬於投資型產品,而搜尋時間短、搜尋間數少、年齡較長、男性、無固定職業及家庭平均月收入較高者成為投資者之機率較高。接著,運用貝氏定理計算出預測準確率最高之機率界限值,結果當機率界限值為0.70時預測準確率最高,投資者達72.22%,自住者達80.07%。此外,並使用2007Q4的資料作樣本外驗證,投資者命中率為65.52%,自住者命中率為84.51%。最後,為提供金融機構運用,本文模擬兩種預測誤差在不同權重下對於金融機構所造成的損失,找出損失最少的機率界限值,結果皆是以0.70為最適機率界限值。 / Housing is dual function goods, consumption and investment, so if we separate the home buyers by their motives, they can be defined as two groups, owner-occupiers and investors. Recently, because the housing market is vigorous inland and the rates are fairly low, there are more and more home buyers buying houses for investment. To financial institutions, their payment behaviors are more instable, compare to owner-occupiers. So this article is aim to build a probability predictive model of housing investors by discussing the different home buying characters between owner-occupiers and investors. Therefore we can provide financing institutions a more objective method evaluating if they should lend money to the home buyers. Then we discuss the predictive accuracy with different cutoff points, finding the cutoff point with highest predictive accuracy, therefore we can elevate the model`s predictive accuracy. Besides, we also discuss the most optimal cutoff point for financial institutions under different administration principles. / This article builds binary logit model by the data of “Housing Demand Survey in Taiwan”. Our results suggests that if the houses in downtown、high unit price、big and small acreage、presale and court auction housing market belong to investing houses. And short search duration、few search items、older、male、non-constant job、higher income are getting higher probability to be housing investors. Then, we use Bayesian Theorem to figure out the cutoff point with highest predictive accuracy, and Our results suggests that 0.70 cutoff point with highest predictive accuracy , at that time, investor predictive accuracy is 72.22%, owner-occupier is 80.07%. Besides, we also do the out-sample test by the 2007Q4 data, the investor`s hit-rate is65.52%, the owner-occupier`s hit-rate is 84.51%. At the end, in order to provide financial institution to use, we give two predictive deviation different weights, to find the smallest loss cutoff point, the result all suggest that 0.70 is the most optimal cutoff point.
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跨界投資與在地再投資區位選擇研究 / A study on location selection of trans-border investment and reinvestment in home country王冠斐 Unknown Date (has links)
本研究著眼於台灣經濟轉型、中國經濟的崛起與台灣企業組織的變化,從台灣企業集團的總部設立、跨界投資的區位選擇及在地再投資三個面向進行討論,期望在既有的研究基礎上,就台灣廠商在兩岸投資區位佈點的考量提出完整性的觀察,並強化既有的研究。
首先,以台灣1000大製造業為研究樣本,選擇包括純辦公室使用、研發設計、台商一千大、跨國生產網絡、外資企業、員工人數、資本總額、知識密集型、傳統型製造業等變項分別代表總部功能、跨界治理能力及企業屬性三大類變數,透過二元羅吉特模型以及集群分析方法,探討台灣企業在首都、都會區以及生產性服務業及創新氛圍同質性地區的總部設立區位選擇行為。實證的結果發現,代表企業屬性變數的資產總額、員工人數和產業別明顯影響台灣製造業廠商在首都設立總部的區位選擇,而總部功能為純辦公室使用或設有研發機構者更傾向將總部設立於首都或都會區,跨界治理能力的影響則未能獲得證實。另外,過去國內在研究企業總部地點選擇研究上較少從創新氛圍角度出發,而本研究實證的結果發現,台灣製造業廠商企業總部的區位選擇不僅受到地區生產性服務業的影響,也受到地區創新氛圍的影響。
在跨界投資區位選擇部分,本研究以台灣250大企業集團中的知識密集型製造業集團為研究對象,以台灣、環渤海地區、長江三角洲地區、珠江三角洲地區為研究場域,選擇企業特性與投資區位條件變數,並以多項羅吉特模型進行實證分析。其中,企業特性變數為產業類別、投資經驗、投資時間等三項因子,而投資區位條件則有勞工薪資、市場規模、區域創新強度及外資投資強度等因子。實證結果發現,代表經濟發展階段的投資時間變項確實會影響企業集團的區位選擇行為,產業的類別不同其區位選擇也會不同,先前的投資經驗雖然影響區位選擇。但是與過去研究不同的是,本次實證發現對台灣企業來說面對相似而且鄰近的市場,進入新市場的動機可能比過去的投資經驗來得重要的多,同時投資區位條件亦會影響區位選擇行為。另外,過去較少直接連結廠商生產面的區域創新能力亦明顯影響企業集團的區位選擇,因此本研究認為區域創新活動對於跨國企業在地化取得知識及技術亦具有相當重要的意義。
在地再投資部分以台灣製造業1000大廠商中知識密集型製造業為研究對象,並以工業地域觀點所劃分的台灣地區北、中、南三大區域為研究場域,選擇包括在台投資經驗、總部區位、第一次投資決策、路徑依循等企業廠商組織決策之屬性變數,以及包含區域中科學園區的設立、產業專業化係數、雜異化指標等區域環境變數,透過多項羅吉特模型進行實證分析。實證的結果發現,總部區位確實影響後續再投資的工廠區位選擇,第一次的投資決策經驗對於第二次投資的區位選擇行為影響比總部區位的影響明顯,代表時間演進而產生路徑相依的地區經濟型態差異變項也確實會影響區位選擇行為。而當區域內科學園區的發展相較未臻成熟時,其區域的賦能仍不足以吸引企業廠商進駐,至於台灣企業的再投資區位選擇基於對區域特性的了解較偏好區域內工業地域的地方化經濟,而不偏好區域內工業地域的都市化經濟。 / Stressed on the Taiwanese economical transition, the up-rising of Chinese economy and the change of Taiwanese enterprise organization as well as based on the past research, this study explores the factors affecting location selection behavior of Taiwanese firms across Taiwan Strait from three aspects including the establishment of enterprise headquarter, cross-border investment and local re-investment.
On the establishment of enterprise headquarter, the top 1000 manufacturing firms in Taiwan were sampled and some factors were analyzed including office type, R&D, multinational production network, foreign enterprise, number of employee, total asset, knowledge-intensive business, and traditional manufacturing firms. However, these factors could be classed into three fields: headquarter function, cross-border management ability and firm characteristics. Then, the location selection behavior of Taiwanese enterprise headquarter was examined by the techniques of binary logit model and cluster analysis technique among capital area, urban area and homogenous area with productive service industry and innovation-based cluster.
The results of empirical analysis show that the factors represented firm characteristics including total asset, number of employee and enterprise type significantly affected the location selection of Taiwanese enterprise headquarter. Furthermore, it is also verified that the enterprise headquarter had been established in capital or urban area if the headquarter was provided with R&D or simply used as office, but the effect of cross-border management upon headquarter establishment is insignificant. The effect of innovation-based cluster upon location selection of enterprise headquarter is seldom studied in the past. However, according to empirical results in this study, they show that location selection of Taiwanese enterprise headquarter is affected not only by local Productive Service industry but also by regional innovation-based cluster.
On the location selection of cross-border investment, this study focused on the area of Taiwan, Bohai Economic Rim, Yangtze River Delta and Pearl River Delta. The top 250 Taiwanese enterprise groups were taken into consideration, and the multinomial Logit model was adopted for empirical analysis in which firm characteristics and location conditions were chosen as research variables. Where, firm characteristics contained industrial type, investing experience and investment time, and location conditions included labor cost, market scale, regional innovation intensity and foreign investment intensity.
The empirical results indicate that industrial type and investment time significantly affect the selection of investment locations. In contrast, investment experience only slightly influences the selection of investment locations. In addition, we find that entrepreneurial motivation to enter new markets may be much more influential than prior location investment experiences for Taiwanese enterprises functioning within similar markets. Regional differences shaping investment conditions in Taiwan and mainland China also affect the selection of investment locations. Our analysis shows a particularly strong linkage between regional innovation capacity and the selection of investment locations. This implies that regional innovation capacity plays a very important role in the selection of investment locations for multinational enterprises
On local re-investment, the top 1000 knowledge-intensive manufacturers in Taiwan were the samples divided by region into the northern, central and southern Taiwan groups by administrative region. The factors affecting organizational decisions were the attribute variables, including Taiwan investment experience, headquarters location, first investment experience and path dependence; and the factors affecting location selection were the regional environment variables, including regional science park status, industry specialization coefficient and Hirschman-Herfindahl index (HHI). The multinomial Logit model was used for empirical analysis, and the results show that the headquarters location affects plant location selection in re-investment, and the first investment experience has a more significant effect on the plant location selection in the second investment than the headquarters location, suggesting that the path-dependent heterogeneity in regional economic style developed over time affects location selection. Also, the immaturity of regional science parks affects plant location selection when regional empowerment cannot attract enterprises. Lastly, Taiwanese enterprises prefer regions with localized economies to regions with urbanized economies for plant location selection.
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台北捷運聯合開發住宅選擇行為與旅運行為之研究 / The research of Taipei MRT joint development of residential choice behavior and travel behavior黃永漢 Unknown Date (has links)
近年來,國內有許多研究提倡大眾運輸導向發展 (Transit Oriented Development)的理念,政府也大力推動大眾運輸系統的建設,其中最為重要的是捷運的建設,在台北都會區,捷運路網的建設正逐步完成,與捷運建設息息相關的捷運聯合開發(Transit Jointed Development)也隨之蓬勃發展,同時,捷運聯合開發亦是我國推動大眾運輸導向發展普遍的作法之一。目前台北都會區目前共有82處聯合開發基地,已完工基地有35處,可容納6,317個家戶,以及755,773.69帄方公尺樓地板面積,對於減緩日益嚴重的都市住宅問題,有一定程度的幫助。但在規劃聯合開發住宅時,聯合開發住宅在不同類型、特性之捷運場站中,將面臨到許多問題,如:純住宅型態或住商混合型態較能符合民眾之需求?哪些家戶會選擇聯合開發住宅?又其家戶類型以及選擇原因為何?這些問題如何解決,係本研究欲探討之內容,因此,本研究以台北捷運聯合開發已完工且辦理租售作業之開發基地作為研究對象,並篩選出9處聯合開發基地進行實證研究,透過問卷調查的方式,瞭解民眾之聯合開發住宅選擇行為與旅運行為,並透過二項與多項羅吉特模型,探討影響民眾聯合開發住宅選擇行為之影響因素。
實證結果發現,在旅運行為方面,聯合開發住宅住戶之大眾運輸使用率大幅增加,在通勤時間與花費方面,通勤時間與花費均減少。除此之外,聯合開發住宅住戶之汽車持有率與使用頻率均大幅減少。在家戶特性方面,捷運聯合開發住宅住戶之家戶規模普遍較小,且家計負責人之年紀普遍較為年輕,進一步形成其他特性,如:就學人口比例較低、家戶月收入較低等。在影響因素方面,家戶規模、住宅帄均單價、住宅規模對民眾選擇不同類型之聯合開發住宅有顯著影響。最後依據實證結果,建議未來聯合開發住宅之規劃應加入TOD的規劃原則,對於聯合開發住宅之坪數、商業面積,應依捷運場站之類型進行調整,使聯合開發住宅之效益達到最大。 / In recent years, there are many researches promote the idea of the transit-oriented development. The government also vigorously promotes this infrastructure projects. One of the most important projects is the development of the MRT system. In Taipei metropolitan area, while the construction of the MRT network is gradually completed, the transit jointed dvelopment is also flourishing. Moreover, transit jointed development is the most common way in order to promote TOD. Currently, there are 82 Transit Jointed Development bases in Taipei metropolitan area. 35 of the bases have already completed, which can accommodate 6,317 household with 75,577,369 square meters of floor area. It certainly will help to alleviate the problem of urban housing. However, in planning of the Jointed Residential Development, it will face many problems due to the different types of characteristics of the MRT station. For example, which households will choose a jointed development dewilling? What is the reason of choosing jointed development dewilling? How to solve these problems? These are the contents of the study. Therefore, in this study, we target the bases that have already been completed and applied for rental operations in transit jointed development as the research object, and select 9 of them for the empirical research. In order to understand people’s choice behavior in jointed development dewilling, we use survey as a method, and explore the factors that affect people’s choice behavior by applying Binary Logit and Multinomial Logit Models.
The results of empirical research show that households in the jointed development dewilling increase their public transport usage and reduce their commuting time and costs. In addition, their car ownership rate and frequency of use are significantly reduced. In the aspect of household characteristics, the households in the jointed development in the household scale are generally small and relatively young age of the householder. Those characteristics are going to further the formation of the other features, such as: the lower the proportion of student population and lower income households. Impact factors, such as the size of the household, the average residential price, residential scale on the people choose different types of joint development dewilling have a significant effect. Finally, according to the empirical results, it is recommended that the TOD should be added to the planning principle of the future Joint Residential Development. For the Jointed Residential Development, the size of house and commercial area should be adjusted along with the MRT in order to maximize the efficiency.
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