<p> Highways have become a symbol of modern America (Levinson, 2004), and infrastructure investment plays a pivotal role both in short-term and long-term economic growth and in job creation. In the US, it represents 16% of the gross national product, and every dollar of public investment in highways has a net rate of return of 22 cents, and every billion dollars of federal highway investment generates 47,500 jobs (AASHTO 2003). In response to the inabilities to raise government revenues in the US, aging infrastructure systems, and high construction and O/M costs, infrastructure development has steadily become a collaboration work between the public and private sector. In liberalized infrastructure markets, various governance structures are being tested for application of public-private partnerships (PPPs or P3s) strategies in infrastructure development (Estache, 2004). </p><p> This thesis aims to review the key complex PPP issues in transportation renewal projects in the US that adopt PPPs. While PPPs can be applied to a range of agreements, the PPP projects to be studied and analyzed in this paper will be limited to those involving complex financing, design, construction and long-term operation and maintenance of transportation infrastructure of at least 10 years. These issues are examined in the context of six case studies in six different state across the US by means of interview and archival record. Findings resulting from this work suggested that PPPs have been increasingly implemented by departments of transportation in the US as a mean to tape into private resources. In addition, this research identified four key complex PPP issues in transportation projects as such Economic issue, Procurement issue, Risk Issue, and Governance issue. States have established a dedicated organizational unit to facilitate the use of PPPs, for example High Performance Enterprise (HPTE) in Colorado and Innovative Project Delivery Division in Virginia, but there exist no standards or best practices in the United States for procurement, concession terms, or risk-sharing.</p>

<p>Given the complexity of transportation systems, generating optimal routing decisions is a critical issue. This thesis focuses on how routing decisions can be computed by considering the distribution of travel time and associated risks. More specifically, the routing decision process is modeled in a way that explicitly considers the dependence between the travel times of different links and the risks associated with the volatility of travel time. Furthermore, the computation of this volatility allows for the development of the travel time derivative, which is a financial derivative based on travel time. It serves as a value or congestion pricing scheme based not only on the level of congestion but also its uncertainties. In addition to the introduction (Chapter 1), the literature review (Chapter 2), and the conclusion (Chapter 6), the thesis consists of two major parts: </p><p> In part one (Chapters 3 and 4), the travel time distribution for transportation links and paths, conditioned on the latest observations, is estimated to enable routing decisions based on risk. Chapter 3 sets up the basic decision framework by modeling the dependent structure between the travel time distributions for nearby links using the copula method. In Chapter 4, the framework is generalized to estimate the travel time distribution for a given path using Gaussian copula mixture models (GCMM). To explore the data from fundamental traffic conditions, a scenario-based GCMM is studied. A distribution of the path scenario representing path traffic status is first defined; then, the dependent structure between constructing links in the path is modeled as a Gaussian copula for each path scenario and the scenario-wise path travel time distribution is obtained based on this copula. The final estimates are calculated by integrating the scenario-wise path travel time distributions over the distribution of the path scenario. In a discrete setting, it is a weighted sum of these conditional travel time distributions. Different estimation methods are employed based on whether or not the path scenarios are observable: An explicit two-step maximum likelihood method is used for the GCMM based on observable path scenarios; for GCMM based on unobservable path scenarios, extended Expectation Maximum algorithms are designed to estimate the model parameters, which introduces innovative copula-based machine learning methods. </p><p> In part two (Chapter 5), travel time derivatives are introduced as financial derivatives based on road travel times&mdash;a non-tradable underlying asset. This is proposed as a more fundamental approach to value pricing. The chapter addresses (a) the motivation for introducing such derivatives (that is, the demand for hedging), (b) the potential market, and (c) the product design and pricing schemes. Pricing schemes are designed based on the travel time data captured by real time sensors, which are modeled as Ornstein-Uhlenbeck processes and more generally, continuous time auto regression moving average (CARMA) models. The risk neutral pricing principle is used to generate the derivative price, with reasonably designed procedures to identify the market value of risk. </p>