Estimation of the Tax Rates Based on Vehicle Miles Traveled Using Stochastic Models

In this thesis, we shall study the alternative revenue collection system which is based on the vehicle miles traveled (VMT). In various studies, it has been found that the existing revenue collection system based on gas/fuel tax is not an appropriate option in the longer run. The main reasons include no effective tax process for vehicles based on alternative fuel vehicle, no effective changes in tax due to economical inflation, and more highway expenditure than generated revenue. Our main objective is to estimate the VMT tax rates that should be charged in order to generate same amount of revenue generated by gas tax. It is apparent that the amount of gas consumed is dependent on the behavior of gas prices which fluctuate daily. Also, VMT is dependent upon the amount of gas consumed and thus it is also dependent on the gas prices. Different mathematical models based on stochastic deferential equations shall be developed for gas prices, the amount of gas consumed, and VMT. Parameters for all the proposed models shall be estimated by using maximum likelihood principle technique and the historical data. As result of our simulation, we have found that VMT tax rate should be approximately 2.5 cents per mile in order to generate same amount of revenue as generated by current system. This VMT tax rate is close enough to the estimated value of 2 cents per mile by Nevada Department of Transportatio

Behavioral responses and policy evaluation: Revisiting water and fuel policies

In my dissertation, I examine how policies regulating agricultural production and clean technology impact the environment. I focus on policies affecting water depletion, water pollution, and fuel consumption. I assess their cost-effectiveness by modeling and quantifying the behavioral responses of farmers and households. My first essay focuses on decreasing groundwater depletion through increasing irrigation efficiency in Mexico. I quantify the impacts of different sources of inefficiency on groundwater extraction, and I evaluate the effectiveness of alternative policies that aim to reduce the over-extraction of groundwater. I find that mechanisms of electricity cost-sharing implemented in many wells have a sizable impact on the inefficiency of irrigation applications; thus, policies eliminating electricity cost-sharing mechanisms have a substantial effect on decreasing groundwater depletion. In contrast, price-based policies are less effective, and policies targeting well-sharing do not have significant effect on reducing irrigation application and groundwater depletion. In my second essay, I assess policies which attempt to reduce water pollution by reducing fertilizer application. Input- and output-based economic policies designed to reduce water pollution from fertilizer runoff by adjusting management practices are theoretically justified and well-understood. Yet, in practice, adjustment in fertilizer application or land allocation may be sluggish. I incorporate time cost as a new dimension in the assessment of these policies and simultaneously quantify the magnitude of the policy effectiveness and the speed at which the policies take effect. I find that while both input- and output-based policies are able to induce a significant reduction in fertilizer application, input-based policies are more cost-effective than their output-based counterparts. Further, input- and output-based policies yield adjustment in fertilizer application at the same speed, and most of the adjustment takes place in the short-term. Due to the rapid adjustment in land allocation between corn and soybeans, the long-term effects of the policies can also be rapidly achieved. Though the time cost does not constitute a major concern in my research area, the time dimension may be important in research areas in which there are different crops that may not be easily substituted between. In my third essay, I explore household adoption of gasoline-electric hybrid vehicles and the impact of hybrid ownership on annual miles traveled in order to understand how hybrid ownership impacts fuel savings. I focus on issues of identification in light of several behavioral factors that are believed to influence both hybrid adoption and miles traveled. I measure two types of rebound effects associated with hybrid adoption. The first one is a traditional rebound effect in which a hybrid owner drives more due to the lower travel cost from higher fuel efficiency; the second one is a social status driven rebound effect in which a hybrid owner drives more to signal his environmental friendliness through driving a hybrid. I find a statistically significant traditional rebound effect on miles traveled. However, this rebound effect is only 3% of the average annual miles traveled and only slightly offsets the fuel savings from the higher fuel efficiency of the hybrid. I do not find evidence of a status-driven rebound effect. I estimate that hybrid adoption induces substantial fuel savings that amount to about half of the average fuel consumption of regular vehicles

Robust Observability, Control, & Economics of Complex Cyber-Physical Networks

This dissertation deals with various aspects of cyber-physical system. As an example of cyber physical systems, we take transportation networks and solve various problems, namely: 1) Network Observability Problem, 2) Network Control Problem, and 3) Network Economics Problem. We have divided the dissertation into three parts which solve these three problems separately. First part of the dissertation presents a novel approach for studying the observability problem on a general network topology of a traffic network. We develop a new framework which investigates observability in terms of flow information on arcs and the routing information. Second part of the dissertation presents a feedback control design for a coordinated ramp metering problem for two consecutive on-ramps. We design a traffic allocation scheme for ramps based on Godunov’s numerical method and using distributed model. Third part of the dissertation presents a novel approach to model Vehicle Miles Traveled (VMT) dynamics and establish a methodology for designing an optimal VMT tax rate. An Optimal control problem is formulated by designing a cost function which aims to maximize the generated revenue while keeping the tax rate as low as possible. Using optimal control theory, a solution is provided to this problem. To the best knowledge of authors all the three problems have not been solved using the methods proposed in this dissertation, and hence they are a novel contribution to the field

Net Effects of Gasoline Price Changes on Transit Ridership in U.S. Urban Areas, MTI Report 12-19

Using panel data of transit ridership and gasoline prices for ten selected U.S. urbanized areas over the time period of 2002 to 2011, this study analyzes the effect of gasoline prices on ridership of the four main transit modes—bus, light rail, heavy rail, and commuter rail—as well as their aggregate ridership. Improving upon past studies on the subject, this study accounts for endogeneity between the supply of services and ridership, and controls for a comprehensive list of factors that may potentially influence transit ridership. This study also examines short- and long-term effects and non-constant effects at different gasoline prices. The analysis found varying effects, depending on transit modes and other conditions. Strong evidence was found for positive short-term effects only for bus and the aggregate: a 0.61-0.62 percent ridership increase in response to a 10 percent increase in current gasoline prices (elasticity of 0.061 to 0.062). The long-term effects of gasoline prices, on the other hand, was significant for all modes and indicated a total ridership increase ranging from 0.84 percent for bus to 1.16 for light rail, with commuter rail, heavy rail, and the aggregate transit in response to a 10 percent increase in gasoline prices. The effects at the higher gasoline price level of over $3 per gallon were found to be more substantial, with a ridership increase of 1.67 percent for bus, 2.05 percent for commuter rail, and 1.80 percent for the aggregate for the same level of gasoline price changes. Light rail shows even a higher rate of increase of 9.34 percent for gasoline prices over$4. In addition, a positive threshold boost effect at the $3 mark of gasoline prices was found for commuter and heavy rails, resulting in a substantially higher rate of ridership increase. The results of this study suggest that transit agencies should prepare for a potential increase in ridership during peak periods that can be generated by substantial gasoline price increases over$3 per gallon for bus and commuter rail modes, and over $4 per gallon for light rail, in order to accommodate higher transit travel needs of the public through pricing strategies, general financing, capacity management, and operations planning of transit services

A methodological framework for quantifying impacts of truck traffic on regional network with implications to transport policy

Increased global trade has promoted the importance of shipping industry and the introduction of mega-ships has created an opportunity to be more cost-effective. Because of this, the expected change in freight transportation influences the operating regimes and schedules at the port terminals. Trucks being the predominant mode of transportation used to carry the freight transport, there is a growing concern about the impact of trucks in the region. The problems are further expected to grow as the improvements to resolve them are hindered by funding shortfalls. Public agencies are therefore involved in developing comprehensive state freight plans that outline immediate and long-range plans for freight-related transportation improvements. However, for states to develop and implement investment policies that can adequately address challenges, there is a need for a policy framework that can evaluate the impact of freight. The lack of the framework makes it difficult for state/metropolitan planning organizations to implement investment strategies in the best possible way. The proposed framework in the dissertation tries to fill the gap by developing a methodological framework, which can help agencies to evaluate multiple policies and their impact on local communities. Additionally, the framework can ascertain the magnitude of impacts that the infrastructure or policy in conjunction with the change in truck traffic might have on a regional level. The developed framework thus can help decision makers to prioritize policies that will benefit both public and freight transportation needs. Three demand models are used in the framework, which is built on the principle of behavioral route choice and mode-choice assignment problem. The outputs from the demand models are further used to quantify the impact in terms of cost-benefit analysis. The dissertation includes a real-world case study demonstrating how the framework can be used to evaluate alternative policies and its impact on a regional level. To this end, the developed framework in the dissertation addresses the research questions to present stakeholder\u27s complex implications that policy can have on the region. It also answers the question of how much the change in truck demand affects the region regarding monetary costs such as safety, congestion, environment, and pavement damage. The research further provides an insight of the change in travel behavior as a result of policy decision and its effect on communities

Data-driven Methodologies and Applications in Urban Mobility

The world is urbanizing at an unprecedented rate where urbanization goes from 39% in 1980 to 58% in 2019 (World Bank, 2019). This poses more and more transportation demand and pressure on the already at or over-capacity old transport infrastructure, especially in urban areas. Along the same timeline, more data generated as a byproduct of daily activity are being collected via the advancement of the internet of things, and computers are getting more and more powerful. These are shown by the statistics such as 90% of the world’s data is generated within the last two years and IBM’s computer is now processing at the speed of 120,000 GPS points per second. Thus, this dissertation discusses the challenges and opportunities arising from the growing demand for urban mobility, particularly in cities with outdated infrastructure, and how to capitalize on the unprecedented growth in data in solving these problems by ways of data-driven transportation-specific methodologies. The dissertation identifies three primary challenges and/or opportunities, which are (1) optimally locating dynamic wireless charging to promote the adoption of electric vehicles, (2) predicting dynamic traffic state using an enormously large dataset of taxi trips, and (3) improving the ride-hailing system with carpooling, smart dispatching, and preemptive repositioning. The dissertation presents potential solutions/methodologies that have become available only recently thanks to the extraordinary growth of data and computers with explosive power, and these methodologies are (1) bi-level optimization planning frameworks for locating dynamic wireless charging facilities, (2) Traffic Graph Convolutional Network for dynamic urban traffic state estimation, and (3) Graph Matching and Reinforcement Learning for the operation and management of mixed autonomous electric taxi fleets. These methodologies are then carefully calibrated, methodically scrutinized under various performance metrics and procedures, and validated with previous research and ground truth data, which is gathered directly from the real world. In order to bridge the gap between scientific discoveries and practical applications, the three methodologies are applied to the case study of (1) Montgomery County, MD, (2) the City of New York, and (3) the City of Chicago and from which, real-world implementation are suggested. This dissertation’s contribution via the provided methodologies, along with the continual increase in data, have the potential to significantly benefit urban mobility and work toward a sustainable transportation system

The Elephant in the Road: An Economic Analysis of the Indian Car Market

Chapter 1 To investigate how fuel economy is valued in the Indian car market, I compute the cost to Indian consumers of purchasing a more fuel-efficient vehicle and compare it to the benefit of lower fuel costs over the life of the vehicle. I estimate hedonic price functions for four market segments (petrol hatchbacks, diesel hatchbacks, petrol sedans, and diesel sedans) to compute 95% confidence intervals for the marginal cost to the consumer for an increase in fuel economy. I find that the associated present value of fuel savings falls within the 95% confidence interval for most specifications in all market segments for the years 2002 through 2006. Thus, I fail to consistently reject the hypothesis that consumers appropriately value fuel economy. Also, I look at vehicle models available in both petrol and diesel form (i.e., twins). Diesel vehicles are generally more expensive than their petrol twins, but, due to higher fuel economy and lower fuel price, have sufficiently lower fuel costs to more than offset the difference. Net savings from purchasing a diesel twin are substantial. Diesel hatchback owners save the equivalent of 50% of the purchase price of their chosen vehicle; diesel sedan owners save 18% of the purchase price of theirs. In 2006, 74% of twin hatchback owners and 59% of twin sedan owners realized these savings by buying the diesel twin. Due to their lower monthly driving distance, forgone savings by owners of petrol twins are lower, but still substantial. Petrol hatchback owners could have saved 24% of the purchase price of their chosen vehicle and petrol sedan owners could have saved 10%. Owners of petrol twins are apparently willing to forgo these substantial savings in order to drive their preferred vehicle. Chapter 2 The Indian car market is the fastest growing in the world. With increased mobility, however, has come increased foreign oil dependence, fuel consumption, and associated externalities. In response to this, the Indian government is contemplating fuel economy standards, but at the same time continues to subsidize diesel fuel. The result of this policy has been a diesel discount of 30%, relative to petrol, and \emph{dieselization}, the increasing market share of diesel cars. This chapter uses a model of vehicle choice and vehicle use to compare the welfare impacts of two possible policy responses: diesel fuel taxation and diesel vehicle taxation. Using data comprised of household-level vehicle purchase and driving distance observations from the 2006, 2008, and 2010 JD Power APEAL survey, I estimate a theoretically consistent model of discrete-continuous choice which explicitly accounts for unobserved household and vehicle characteristics and correlation between vehicle choice and driving distance. I find the effect of a diesel fuel tax that eliminates the petrol/diesel price gap to be 4.6 percentage point reduction of the market share of diesel cars based on results from 2006, a 7.9 percentage point reduction based on results from 2008, and an 8.6 percentage point reduction based on results from 2010. On average, a diesel car tax of 21.9% would achieve the same result. The diesel car tax option, however, does relatively little to change intensive margin incentives. A smaller diesel fuel tax, sufficient to yield the same total fuel conservation as the diesel car tax, compares favorably to both policies on efficiency grounds. While the subsidy eliminating diesel fuel tax is more efficient than the diesel car tax in terms of deadweight loss per liter of fuel conserved, the smaller diesel fuel tax actually results in a net welfare gain. This result comes from the fact that the pre-existing tax on petrol fuel raises enough revenue from those would-be diesel car buyers who are compelled to buy a petrol instead to more than compensate them back to their pre-tax utility levels. Comparing compensating variation of the subsidy eliminating diesel fuel tax to the diesel car tax, neither policy imposes a consumer welfare cost of more than 2% of new car buyers' average annual income. However, the welfare burden as a share of household income is found to be greater for the poorest households

Transition to Green Mobility

학위논문(박사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2022. 8. 구윤모.신고전파의 유인된 혁신 접근법은 혁신이 수요와 상대요소가격 변화에 따라 그 속도와 방향이 결정된다고 보았으며, 기술 혁신에 있어서 수요의 역할을 강조하였다. 즉, 신기술이 도입되면 소비자의 수요로 혁신이 확산된다. 그러나 시장에서의 기존 기술의 상대적 우위, 높은 진입 비용 및 불확실성 등으로 인해 소비자의 의사결정 만으로는 사회적으로 최적의 수준까지 확산이 일어나지 않을 수 있다. 이로 인해 정부는 시장의 중재자로서 혁신의 확산을 위해 개입을 하게 되며 구체적인 정책 수단을 설계한다. 그렇다면 이러한 정부 개입이 소비자 선택과 시장에 어떤 영향을 미치며 어떤 결과를 초래하는가? 본 연구에서는 그린 모빌리티를 연구 대상으로 하여 시장유인적 (규제) 수단에 집중하였다. 자동차 산업은 대표적인 B2C 시장으로 소비자의 선호를 파악하여 신기술의 확산을 예측할 수 있으며, 연쇄 효과가 크기 때문에 산업 및 경제에 미치는 영향이 크다. 정부는 그린 모빌리티로 인해 야기되는 긍정적 외부효과 (환경 개선 및 신 산업 창출을 통한 경제 성장 등)를 기대하며 다양한 정책수단으로 신기술의 확산을 지원하고 있다. 본 연구에서는 친환경차 보급 정책수단 중 대표적으로 조세 및 보조금, 충전 인프라 설치 투자에 대하여 규제와 성장, 정책 효과성 그리고 형평성 측면에서 파급 효과를 분석하였다. 이산선택모형은 개인의 선호에 따라 제품 및 기술의 수요를 예측할 수 있는 대표적인 방법론이다. 그러나 제품 및 기술 간 대체효과에 치중하여 다른 산업과 경제 간의 연쇄효과를 파악하기 어렵다. 한편 계산가능한 일반균형모형은 경제 주체 간의 관계를 고려하여 경제 변수(가격 및 수요 등)의 변화를 광범위하게 분석한다. 그러나 일반균형모형은 기술에 대한 설명이 제한적이며, 시장 변화가 재화의 가격 및 수량에만 의존한다. 두 모형을 통합함으로써 이산선택모형은 일반균형모형의 결과를 내생적으로 반영하여 속성 수준의 보다 탄력적인 변화를 포착하고, 일반균형모형은 이산선택모형의 구체적인 기술 사양을 반영한 대체 관계를 구현할 수 있다. 따라서 본 연구에서는 구축된 통합모형을 바탕으로 개인 단위의 소비자 선호에 따른 수요 변동이 신기술의 확산과 국가 전체에 미치는 영향을 규명하고자 하였다. 결과적으로 전기차와 수소차의 확산은 경제 성장으로 이어졌다. 환경적인 측면에서 전기차 및 수소차로의 수요 전환에 따라 수송 부문의 배출량이 크게 감소하였다. 그러나 전 산업의 배출량은 총 생산 증가로 인해 오히려 증가하여, 수송 부문의 배출 저감 효과를 상쇄하는 반등 효과가 나타났다. 게다가 그린 모빌리티가 초기에 급증하는 경우 석탄 화력 발전 및 LNG 개질 위주의 수소 생산으로 인해 오히려 배출량이 증가하게 된다. 따라서 그린 모빌리티의 급진적인 수요 확산 이전에 친환경 발전이 전제 되어야 바람직한 환경 개선 효과를 관찰할 수 있다. 그린 모빌리티 보급을 위한 정책 수단의 주요 결과는 다음과 같다. 첫째, 조세가 부과됨에 따라 기업의 생산 비용은 증가할 수 있으나, 학습률에 따라 혁신이 이 비용을 상쇄하는 가능성은 훨씬 더 빠르게 증가할 수 있다. 즉, 조세와 같은 환경정책과 기업의 생산성을 높이는 기술정책을 동시에 시행할 때 보다 효과적인 결과를 얻을 수 있다. 둘째, 소비자를 대상으로 한 직접적인 경제적 인센티브인 보조금 정책 보다 보완재 시장으로서 인프라에 대한 투자가 신기술 확산 및 경제 성장에 더 긍정적인 영향을 준다. 즉, 보조금을 더 많이 주어 현재 시장을 확대하기 보다는 충전 인프라에 투자하여 미래 시장 환경을 개선하는 것이 장기적으로 국가 경제에 도움이 될 것으로 보인다. 마지막으로 보조금의 차등 지급은 단기적으로 저소득층의 소득 향상에 긍정적인 영향을 주지만, 장기적으로는 국가 경제 성장에 부정적인 영향을 초래한다. 보조금의 차등 지급은 궁극적으로 신기술의 보급을 늦추기 때문에 장기적으로 가계 소득 증가에 덜 도움이 되기 때문이다. 본 연구에서는 두 모형을 결합함으로써 개인의 기술 채택(technology adoption)에서부터 사회 전체를 대상으로 한 기술 확산(technology diffusion)의 혁신 과정을 관찰할 수 있었다. 게다가 이산선택모형 혹은 일반균형모형만 사용하여 정책을 테스트하는 경우 보다 두 모형을 통합한 현재의 프레임워크가 정부 정책의 영향을 더 정확하게 예측할 수 있으며, 정부의 의사결정에서 명확한 근거를 제시할 수 있을 것으로 보인다.The neoclassical-induced innovation approach views the speed and direction of innovation as being determined by changes in demand and relative factor prices and emphasizes the role of demand in technological innovation. In other words, innovation spreads from consumer demand with the introduction of new technologies into the market. However, the diffusion on a socially optimal level may not fully occur solely based on the decision-making of consumers due to the relative superiority of existing technologies, high entry costs and uncertainty. Consequently, the government intervenes in the diffusion of innovation and acts as a mediator in the market by designing specific policies to address the shortfalls. This study explored how the government’s intervention affects consumer choices and markets, as well as the consequences thereof. This study examined green mobility and focused on market-inducing (regulatory) measures. The automobile industry is a representative business-to-consumer market, and therefore, it is possible to predict the spread of new technologies by understanding consumer preferences. In anticipation of positive externalities (environmental improvement and economic growth through new industry creation), the government supports the diffusion of green mobility through various policy instruments. This study analyzed the ripple effects of regulation and growth, policy effectiveness and equity on tax and subsidy as well as investment in infrastructure as representative of green mobility dissemination policy measures. The discrete choice (DC) model is a representative methodology that can predict demand for products and technologies according to individual preferences However, it is difficult to grasp the cascading effect between other industries and the economy because it focuses on the substitution effect between products and technologies. On the contrary, the computable general equilibrium (CGE) model broadly analyzes changes in economic variables such as price and demand through considering the relationship between economic agents; however, the CGE model has a limited explanation of technology and market changes, depending on the price and quantity of goods. Through an integration of both models, it can be noted that the DC model captures more elastic changes in the attribute level by endogenously reflecting the results of the CGE model, whilst the CGE model implements a substitution relationship reflecting the specific technical specifications of the DC model. Therefore, using the integrated model, this study investigated the effect of demand fluctuations according to individual consumer preferences on the diffusion of new technologies within the whole country. Consequently, the proliferation of electric vehicles and hydrogen cars has led to economic growth. From an environmental point of view, the transport sector's CO2 emissions decreased significantly because of the shift in demand for electric and hydrogen vehicles. However, emissions from other industries increased owing to the increase in production output, resulting in a rebound effect that offset the emission reduction effect in the transport sector. In addition, if green mobility surges in the early stages, emissions will increase because of coal-fired power generation and hydrogen production centered on liquefied natural gas reforming. Therefore, an environmental benefit will only be observed when a clean power mix is a prerequisite before the demand for green mobility spreads. The impact of policy measures on green mobility dissemination is as follows. Firstly, the imposition of a tax may cause the cost of production for many companies to increase; however, depending on a learning rate, innovation may offset this cost rapidly. In other words, more effective results can be obtained when environmental policies such as taxation and technological policies that increase corporate productivity are implemented simultaneously. Secondly, investment in the complementary goods (infrastructure) market to improve the future market environment has proven to have a longer-term beneficial effect on the national economy than direct economic incentives (subsidies) for consumers. Finally, the differential payment of subsidies has a positive effect on the income improvement of the low-income class in the short-term; however, it is less beneficial to household income growth and national economic growth in the long-term as it slows the adoption of new technologies. By combining the two models in this study, it was possible to observe the innovation process from individual technology adoption to technology diffusion, targeting the entire economy. In addition to the above, the current framework that integrates the two models can more accurately predict the impact of government policies and provide a clear rationale for government decision-making than when testing policies using only an independent model.Abstract iii Contents vii List of Tables x List of Figures xi Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objectives 9 1.3 Research Outline 15 Chapter 2. Literature Review and Theoretical and Methodological Background 18 2.1 Theoretical Background 18 2.1.1 Debates on Environmental Regulation and Innovation 18 2.1.2 Transport Policy for the Diffusion of Green Mobility 21 2.2 Methodological Background 25 2.2.1 Demand Forecasting on Individual Level 25 2.2.2 General Equilibrium Theory 30 2.3 Assessment of the Effects of Technology Diffusion: Green Mobility 31 2.3.1 Environmental Effects 31 2.3.2 Economic Effects 34 2.4 Integrated Studies of Consumption Behavior in the Transport Sector 36 2.5 Limitations of Previous Studies and Contribution of the Dissertation 40 Chapter 3. Methodology 43 3.1 Discrete Choice Model 43 3.1.1 Conceptual Background 43 3.1.2 Method 45 3.2 CGE Model 52 3.2.1 Social Accounting Matrix 52 3.2.2 Model Structure 60 3.3 Model Linkage 79 3.3.1 Choice Probability 81 3.3.2 Household Sector 83 3.3.3 Industry (Private Car Service) Sector 88 Chapter 4. Empirical Analysis 92 4.1 DC and Integrated Model Results 92 4.1.1 DC Estimation Results 92 4.1.2 Comparison of DC Model and Integrated Model 95 4.2 Baseline Scenario Analysis 99 4.2.1 Scenario Description 99 4.2.2 Validation 106 4.2.3 Scenario Results 110 4.3 Scenario Analysis 1: Fuel Tax and Learning Effects 124 4.3.1 Scenario Description 124 4.3.2 Scenario Results 126 4.4 Scenario Analysis 2: Subsidy and Charging Infrastructure Investment 138 4.4.1 Scenario Description 138 4.4.2 Scenario Results 141 4.5 Scenario Analysis 3: Differential Subsidy Payment 148 4.5.1 Scenario Description 148 4.5.2 Scenario Results 150 Chapter 5. Conclusion 160 5.1 Concluding Remarks and Contributions of This Study 160 5.2 Limitations and Suggestions for Future Research 165 Bibliography 169 Appendix 1: Respondent’s Demographics in Conjoint Survey 190 Appendix 2: Classification of Industry in the CGE Model 191 Abstract (Korean) 192박

Spatial Transportation Modeling

Transportation modeling is both one of the valuable job skills offered by scientific geography and a topic that can serve to develop analytic intuition. This book is designed for the student receiving a first exposure to the transportation problem as well as an introduction to the formal modeling of geographic phenomena. Transportation modeling is a good and particularly useful example of the sharing of paradigms and methodologies between scientific geography and other sciences. Professor Christian Werner’s geographical approach should be of particular interest to students and followers of the literature not only in human geography but also in operations research, transportation engineering, urban and regional economics, regional science, city and regional planning, and management science. SCIENTIFIC GEOGRAPHY SERIES, Grant Ian Thrall, editor.https://researchrepository.wvu.edu/rri-web-book/1017/thumbnail.jp

Development of a Decision Support Framework for the Planning of Sustainable Transportation Systems

With the rapid increase in economic development throughout the world, there is stress on the resources used to support global economy, including petroleum, coal, silver, and water. Currently, the world is consuming energy at an unprecedented rate never seen before. The finite nature of such non-renewable natural resources as petroleum and coal puts pressure on the environmental system, and ultimately reduces the availability of resources for future generations. Hence, it is critical to develop planning and operational strategies that seek to achieve a sustainable use of existing natural resources. With this motivation, this dissertation focuses to develop a decision support framework based on multiple performance measures for the planning of sustainable transportation systems. A holistic approach was adopted to compute performance indices for a System of Systems (SOS) including the Transportation, Activity, and Environmental systems. The performance indices were synthesized to calculate a composite sustainability index to evaluate the sustainability of the overall SOS. To help make better design and policy decisions at an aggregate level, a suitable modeling approach that captures the dynamic interactions within the SOS was formulated. A method of system of ordinary differential equations was chosen to model the aggregated performance indices and their interdependencies over time. In addition, systems and control methodology was used in the development of optimal policies (with respect to investments in various systems) for decision making purposes. The results indicated that the Transportation and Activity system both follow positive trend over the years whereas the Environmental system follows an overall negative trend. This is evident as continuous increase in growth and transportation will result in decreased performance of Environmental system over time. The results also highlighted periodic behavior with a phase lag for the performance of Transportation and the Activity system; the performance of Environment system decayed with time. In addition, the results demonstrated that it is possible to formulate an optimal control to predict investment decisions over time. Furthermore, the results from this research provided an alternate, cost-effective method to rank and prioritize projects based on sustainability index values. The major contributions of this research are fourfold. The first contribution of this research is the development of a framework to generate sustainability indices for policy making considering, explicitly, multiple interdependent systems. This research is first of its kind to study the dynamical interactions between the three systems: Transportation, Activity, and Environment. The second contribution of this research is a detailed analysis to understand the dynamics of the three interdependent systems. Multiple insights were obtained from this research. The techniques learnt can be applied to perform multi-city network modeling through the concept of interconnected networks. In addition, the need to conserve the environment and preserve the resources is highlighted. The third contribution of this research work is development of control mechanisms to evaluate investment policies for the design of sustainable systems. Investment decisions were derived from the design. The fourth contribution of this research is the development of a framework to estimate sustainability indices for the evaluation and prioritization of transportation projects. Projects are prioritized and ranked based on the sustainability index values. The greater the sustainability index value, the higher is the project priority. This provides a comprehensive mechanism to incorporate information beyond traditional techniques