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As road conditions worsen, users experience an increase in fuel consumption and vehicle wear and tear. This increases the costs incurred by the drivers, and also increases the amount of greenhouse gases that vehicles emit. Pavement condition can be improved through rehabilitation activities (resurfacing) to reduce the effects on users, but these activities also have significant cost and greenhouse gas emission impacts. The objective of pavement management is to minimize total societal (user and agency) costs. However, the environmental impacts associated with the cost-minimizing policy are not currently accounted for. Preliminary research has shown that the optimal policies for minimizing total costs and for minimizing total emissions do not coincide. Instead, there exists a range of potentially optimal decisions, known as the Pareto curve, in which it is not possible to decrease total emissions without increasing total costs and vice versa. The first part of the dissertation explores these tradeoffs for a system of pavement segments, taking the approach of an agency who looks to minimize their total costs subject to an emissions constraint. This expands on the existing literature where environmental aspects and costs had only been examined at a single facility level. Other pavement management optimization techniques either did not include costs, or did not examine tradeoffs between costs and environmental aspects. For a case study, a network was created from a subset of California’s highways using available traffic data. The first step was to look at policies that have been used in practice. Caltrans applies a universal trigger roughness policy, which is a policy where all roads in the network are treated the same. Whenever any road reaches the designated trigger roughness value, it will be rehabilitated, independent of road geometry and traffic. The past policy by Caltrans used a trigger roughness value of 3.5 m/km and the current value is 2.7 m/km. Moving from th

This dissertation presents a methodology for the joint optimization of a variety of pavement construction and management activities for segment and system problems under multiple budget constraints. The objective of pavement management is to minimize the total discounted life time costs for the agency and the highway users by finding optimal policies. The scope of the dissertation is focused on continuous time and continuous state formulations of pavement condition. We use a history-dependent pavement deterioration model to account for the influence of history on the deterioration rate. Three topics, representing different aspects of the problem are covered in the dissertation. In the first part, the subject is the joint optimization of pavement design, maintenance and rehabilitation (M&R) strategies for the segment-level problem. A combination of analytical and numerical tools is proposed to solve the problem. In the second part of the dissertation, we present a methodology for the joint optimization of pavement maintenance, rehabilitation and reconstruction (MR&R) activities for the segment-level problem. The majority of existing Pavement Management Systems (PMS) do not optimize reconstruction jointly with maintenance and rehabilitation policies. We show that not accounting for reconstruction in maintenance and rehabilitation planning results in suboptimal policies for pavements undergoing cumulative damage in the underlying layers (base, sub-base or subgrade). We propose dynamic programming solutions using an augmented state which includes current surface condition and age. In the third part, we propose a methodology for the joint optimization of rehabilitation and reconstruction activities for heterogeneous pavement systems under multiple budget constraints. Within a bottom-up solution approach, Genetic Algorithm (GA) is adopted. The complexity of the algorithm is polynomial in the size of the system and the policy-related parameters.

In 2012, over 25% of the bridges in the United States were rated as structurally deficient or functionally obsolete. Moreover, 35% of bridges are serving beyond their theoretical design lifespan and the number has been projected to increase over the next decade. The imperative needs of improving the overall condition of the bridge system has been impeded by the shortage of funding available for bridge repairs and maintenance. In 2006 the gap between Federal Highway Administration's (FHWA) estimates to eliminate the bridge maintenance backlog and the actual appropriations to bridges for repairs and maintenance from the Highway Bridge Program was $43.4 Billion. In 2009, the gap increased to $65.7 Billion. Such conflict has made effective bridge management more critical than ever. In bridge management, agencies collect bridge condition data and develop deterioration models that predict the bridges' future conditions and associated costs, based on which maintenance, rehabilitation and reconstruction (MR&R) decisions are made. It is therefore critical to have accurate deterioration models. However, limited availability of data and incomplete understanding of the deterioration process result in inaccurate models, which lead to sub-optimal MR&R decisions and significant cost increases. To address the inaccuracy stemming from limited bridge condition data, researchers have proposed Adaptive Control (AC) methods that update the deterioration models successively as new data become available. The underlying belief is that agencies can obtain more accurate deterioration models through updating and subsequently improve their MR&R decisions and achieve cost savings. State-of-the-art bridge management systems, such as Pontis, use a class of AC procedures known as Certainty Equivalent Control (CEC). The procedure used in Pontis updates the transition probabilities (i.e., the parameters of the component deterioration models) after each condition survey, and uses the upda

In response to the growing climate change problem, many governments around the world are seeking ways to reduce the greenhouse gas (GHG) emissions of various sectors of the economy. The trucking sector is important in meeting this challenge in the US because it is responsible for a share of emissions that is significant and rapidly growing. For governments to intervene in this sector smartly, they need models that capture its key incentives, constraints and dynamics, while making the most out of the limited data available. However, existing models fall short of this ideal. This dissertation first introduces the Trucking Sector Optimization Model (TSO) as a tool for studying the decisions that carriers and shippers make within a short-run time horizon--modeling the dynamics of truck fleets, penetration rate of Fuel Saving Technologies (FSTs) such as aerodynamic improvements and low rolling resistance tires, and changes in the demand of trucking. In addition to estimating tailpipe GHG emissions, the model also estimates emissions from upstream fuel production sources, vehicle manufacturing, and pavement rehabilitation activities. This model is then used to evaluate the effectiveness of various incentives-based and regulation-based strategies that California's government could implement in the trucking sector to help achieve the objectives of the Global Warming Solutions Act of 2006 (AB 32). The strategies analyzed are: fuel taxation, mileage taxation, truck purchase taxation, FST subsidies, FST regulations, increases in the allowed weight of trucks, and the Low Carbon Fuel Standard recently introduced in California. Results indicate that there presently exist significant economic incentives for carriers to invest in FSTs beyond what is currently commonplace. The correction of market mechanisms that are responsible for this apparently suboptimal behavior, would lead to significant reductions in emissions, and would also allow for incentive-based strategies to have thei

Public transit is often touted as a "green" transportation option and a way for users to reduce their environmental footprint by avoiding automobile emissions. Many transit systems, however, have considerable emissions, and when vehicles run with ridership significantly below capacity, the per-passenger-kilometer emissions can be greater than for automobile. Efforts to reduce public transit emissions have centered on shifting users from more polluting modes and improving technology either by retrofitting existing vehicles or replacing them with more efficient models. I explore an approach to optimizing the design and operations of transit systems for both costs and emissions using continuum approximation models. The research identifies the Pareto frontier for designing an idealized transit network, and compares transit modes, including bus, bus rapid transit, light rail, and metro heavy rail, over four city scenarios. The slope at any point on the Pareto curve represents the cost of decreasing emissions by another unit, and this can be used to identify an emissions level that is equal to the market value of carbon. Further, I explore how the level of service for users impacts emissions: first, comparing modes at a given emissions level to see which provides the best service to users in terms of average travel time; second, incorporating travel time elasticities into the optimization to allow demand to reduce subject to increases in the travel time. Results of the parametric analysis suggest that a BRT system is a low cost and low emissions transit option for many types of cities. Choosing GHG reduction levels based on the market price of carbon has a small impact on user travel time, so further reductions may be reasonable. In general, the lowest-cost mode will provide the fastest travel time to users at a given emissions level. When shifting demand is accounted for, emissions reductions are moderated, but not eliminated, by the increase in automobile emissions when

An urban transit system can be made more cost-efficient by improving the access to it. Efforts in this vein often entail the provision of greater mobility, as when high-speed feeder buses are used to carry commuters to and from trunk-line stations. Other efforts have focused on the creation of more favorable land-use patterns, as occurs when households within a Transit-Oriented Development (TOD) are tightly clustered around trunk stations. The efficacy of these mobility and land-use solutions are separately examined in the present work. To this end, continuum approximation models are used to design idealized transit systems that minimize the generalized costs to both the users and the operators of those systems. The assessments unveil how the choice of rolling-stock for the trunk-line portion of a transit network can be influenced by its access mode. If transit is accessed solely (and slowly) on foot, then the optimal spacings between lines, and between the stations along those lines, are small. This can place capital-intensive rail systems at a competitive disadvantage with transit systems that feature buses instead. When access speeds increase, the optimal spacings between lines and stops expand as well. Hence, if accessed by fast-moving feeder buses, Metro-rail or bus-rapid transit can become preferred trunk-line options. By comparison, the influence of altered land use patterns brought by TODs was found to be less dramatic. While clustering households around Metro-rail stations justifies larger spacings between the stations, it yields only modest reductions in generalized costs because the larger spacings penalize transit users who reside outside of the TODs.

In previous research (Chung et al., 2009), the potential of the continuous risk profile (CRP) to proactively detect the systematic deterioration of freeway safety levels was presented. In this paper, this potential is investigated further, and an algorithm is proposed for proactively detecting sites where the collision rate is not sufficiently high to be classified as a high collision concentration location but where a systematic deterioration of safety level is observed. The approach proposed compares the weighted CRP across different years and uses the cumulative sum (CUSUM) algorithm to detect the sites where changes in collision rate are observed. The CRPs of the detected sites are then compared for reproducibility. When high reproducibility is observed, a growth factor is used for sequential hypothesis testing to determine if the collision profiles are increasing over time. Findings from applying the proposed method using empirical data are documented in the paper together with a detailed description of the method.