This research focuses on the cyber-attack of the smart grid and its retrieval to a normal state by estimating the smart grid's resilience. This study developed a theoretical model to estimate the resilience of the smart grid using choice modeling. A utility function is formulated based on various factors and sub-factors of resilience to estimate the resilience of the smart grid. Choice modeling is applied to estimate the model parameters in various fields such as marketing, energy, transportation, and health and to predict the outcome.

The High Occupancy Vehicle (HOV) lanes on Atlanta, Georgia’s radial I-85 had long been providing sub-optimal throughput in the peak traffic hours, as the two-person occupancy requirement allowed the lanes to become heavily congested. The Georgia Department of Transportation converted 15.5 miles of HOV 2+ lanes to High Occupancy Toll (HOT) lanes, one in each direction on I-85. The lanes use dynamic value pricing to set toll levels based on the volume and average speed of traffic in the lanes. The goal of this research was to investigate the responses to toll lane pricing and the factors that appear to inform lane choice decisions, as well as examining values of travel time savings and toll price elasticity for users of the Express Lanes. This study of the metropolitan Atlanta I-85 Express Lanes operates at the microscopic level to examine the impact of demographic characteristics, congestion levels, and pricing on users’ decisions to use or not use the I-85 Express Lanes. The dissertation examined the value of travel time savings distributions across income segments. The differences in these distributions among lower, medium, and higher income households were marginal at best. The results did not indicate that higher income households had the highest value of travel time savings results, as may have been expected. The modeling work performed here provided a number of insights into toll lane use. The determinants of lane choice decision-making in the morning peak had notable differences from the determinants of the afternoon peak. The initial analysis involved models which were estimated across three different income segments to examine differences in decision making between low, medium, and higher income households. The results indicated that the parameters were largely consistent across the three segments. Further segmenting the households showed that lane choice determinants varied more within the ‘Higher’ income segment than across the original three-segment structure. In particular, the five-segment models illustrated lower elasticities with regard to corridor segment counts and toll levels for the highest-income households in the sample, as well as higher household income level elasticities for afternoon trips by that same cohort. The research was among the first in the available literature to use revealed preference lane use data for both the toll lane users and the unpriced general purpose lane users. The use of household level marketing data, rather than census or survey data, was another unique characteristic of this research. The analysis of value of travel time savings with a demographic component that looks at household income has not yet been seen in the literature; similarly, the findings regarding differing behavior among very high income households appear to be unseen in the existing literature. The results from this analysis, such as willingness-to-pay values for different population segments, will be useful inputs to the decisions surrounding future HOT implementations in the Atlanta region. The use of new data sources, the evaluation of those types of data sources, and the application of methods that have previously been unused in this field make up the primary contributions of this dissertation.

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Activity-based travel demand model systems are increasingly being deployed to microsimulate daily activity-travel patterns of individuals. However, a critical dimension that is often missed in these models is that of vehicle type choice. The current dissertation addresses this issue head-on and contributes to the field of transportation planning in three major ways. First, this research develops a comprehensive vehicle micro-simulation framework that incorporates state-of-the-art household vehicle type choice, usage, and evolution models. The novelty of the framework developed is that it accommodates all the dimensions characterizing vehicle fleet/usage decisions, as well as accommodates all dimensions of vehicle transactions (i.e., fleet evolution) over time. The models estimated are multiple discrete-continuous models (vehicle type being the discrete component and vehicle mileage being the continuous component) and spatial discrete choice models that explicitly accommodate for multiple vehicle ownership and spatial interactions among households. More importantly, the vehicle fleet simulator developed in this study can be easily integrated within an activity-based microsimulation framework. Second, the vehicle fleet evolution and composition models developed in this dissertation are used to predict the vehicle fleet characteristics, annual mileage, and the associated fuel consumption and green-house gas (GHG) emissions for future years as a function of the built environment, demographics, fuel and related technology, and policy scenarios. This exercise contributes in substantial ways to the identification of promising strategies to increase the penetration of alternative-fuel vehicles and fuel-efficient vehicles, reduce energy consumption, and reduce greenhouse gas emissions. Lastly, this research captures several complex interactions between vehicle ownership, location, and activity-travel decisions of individuals by estimating 1) a joint tour-based model of tour complexity, passenger accompaniment, vehicle type choice, and tour length, and 2) an integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. The methodology used for estimating these models allows the specification and estimation of multi-dimensional choice model systems covering a wide spectrum of dependent variable types (including multinomial, ordinal, count, and continuous) and may be viewed as a major advance with the potential to lead to redefine the way activity-based travel model systems are structured and implemented. / text

There has been a noticeable swing towards school pupils being driven to and from school, and away from active modes like walking and cycling, in recent decades. This has had a number of side effects. Less reliance on active modes of transport has been a contributing factor in the reducing levels of physical activity for school children. Traffic volumes associated with school trips have also increased. This increased has tended to contribute to an increase in traffic congestion, adverse environmental effects and reductions in levels of sustainability. School trip traffic contributes specifically to congestion at school gates. Schools have been identified as having significant effects on the transportation system adjacent to them. Schools which seek Resource Consents for new or changed activities are often being required to take measures to mitigate their adverse effects The purpose of this study is to explore the factors contributing to primary school pupils' travel choices. This will help to identify travel choice patterns which may, in turn, be useful in developing policies and planning initiatives which contribute to achieving an efficient and sustainable transport system. A range of literature relevant to school and general commuting travel demand was reviewed. A case study involving the pupils of twenty two Christchurch primary schools was carried out. Pupils and their parents were surveyed to establish mode choices and the factors influencing those choices. The study found that between 55% and 60% of pupils surveyed travel to and from school by car. 30% to 35% walk or scooter, and 5% to 7% cycle. This compares with 34% travelling by car in the late 1980s. In addition, a greater proportion of those pupils who walk, scooter or cycle to school are accompanied by an adult than in the past. The results of the study also suggested that School Travel Plans, when combined with the energy and commitment to implement them can have a significant effect on school travel choices. As part of the case study, parents were asked to rank the importance of a number of factors which could influence choices regarding their children's school travel. The responses from parents identified safety concerns, regarding both road and personal safety, as the major factor behind decisions regarding their children's travel choices. Time constraints coupled with the complexity of travel requirements of many families were identified as significant factors. Multinomial Logit Models for both mode choice and pupils travel independence were then produced for both the journey to and from school. These models were based on the results of the case study. The models produced indicate that, at a school level, there is a correlation between increasing school roll and an increasing proportion of pupils travelling by car. A slight negative correlation between school decile and car usage was also indicated. This is contrary to the normally accepted understanding that in most transport situations there is a positive correlation between increasing affluence and car usage. Superior model results were obtained at a disaggregated individual level, using nine variables relating to the school, the neighbourhood, and the home, than the results obtained using the school based variables of. However, it is not considered that the effort required to obtain information on the additional variables is justified when estimating mode choices of pupils at an individual school. It is therefore recommended that a model using Decile, Average Age, and School Roll variables be used to estimate mode choices at an individual school. At a family level, there was a strong positive correlation between distance from school, age of the pupils, and the number of major roads between school and home, and car usage. It became apparent that the decisions made regarding children's school travel are very complex. Families juggle a number of factors, many of which are in conflict with one another. For example a desire to care for the environment may be in conflict with the demand to get the children to school, and get to work on time. This complex interrelationship between factors has resulted in some instances where normally accepted "Rules of Thumb", such as the understanding that increased car usage is generally associated with increasing wealth, do not appear to be applicable to school travel. The complexity of interrelationships has further meant that it has not been possible to quantify the impact of any one factor on its own.

This research presents the first North American route-choice model for cyclists developed from a large sample of GPS data. These findings should encourage all interested municipalities to implement cycling as part of their transportation planning by determining key designing and planning factors to encourage cycling. The analysis is based on processing revealed preference data obtained from 415 self-selected cyclists in Waterloo, Ontario, which corresponded to 2000 routes. Cyclists' route decisions were modeled using multinomial logit framework of discrete choice theory. The main finding involved in capturing two different behaviour groups, namely experienced and inexperienced cyclists. This was subsequently reflected in the two developed models. The key factors impacting route-choice were found to be trip length, speed, volume, bicycle lane presence and percent of uphill gradient that cyclists face. The predictive power of the best model was 65%. The outlier analysis found that the relative significance of uphill gradient coefficient in one circumstances and perhaps the exclusion of unobserved variables, in other circumstances could be the cause why probability of actual choice was not predicted by both models all the time. In addition, this research involved in the development of a transferability study involving route-choice modeling for cyclists. The analysis is based on the revealed preference data obtained from 255 self-selected cyclists in Peel Region, Ontario, which corresponded to 425 unique routes. The choice set contained actual routes and a combination of alternatives obtained by labeling and impedance rules. The transferability of Waterloo's model to Peel Region was 37%. This means that cyclists behaviour in the Peel Region can be predicted correctly by travel length, bicycle lane presence and percent of uphill gradient for every third cyclist.

The thesis investigates the development of short-sea shipping (SSS) in the Southern African Development Community (SADC) region by studying the determinants of SSS, the stated choice preference of shippers and freight forwarders and the stated intentions of maritime carriers for SSS. It is purported the introduction of SSS in SADC could reduce socio-environmental problems currently faced such as road damage, road congestion, pollution and transport related accidents. Discrete choice modeling (DCM) is employed as the main methodology to study shipper and carrier behavior. Discrete choice modeling permits the construction of general utility functions incorporating various decision maker characteristics and choice attributes to elicit preference of respondents. The general postulate in DCM is that utility is derived from the properties of things rather than the actual thing per se. A particular benefit of DCM in this study is the elicitation of preference for services and interventions that have not been introduced by SSS. The first step in the study is a theoretical investigation of the potential of SSS in the SADC region. It highlights the policy initiatives, the barriers and enablers related to the development of SSS. The proposed SSS system would have three main roles: to offer an alternative mode of freight transport service between port cities, to serve as the main leg in an intermodal transport network, and to serve feeder services between hub-and-spoke ports. The findings reveal that, SSS has the theoretical potential to work in the SADC region, given the large geographic region, projected freight volumes and customs and trade policies the SADC region is pursuing. The second step in the study involves an a-priori study conducted to develop a general understanding of freight transport in SADC. For this purpose, a uniquely developed online survey was conducted across the SADC region to ascertain in particular: who the decision maker is in terms of freight mode choice; and what the significant attributes that influence freight mode choice are. The results reveal that both the shipper and the freight forwarder are involved in mode choice decisions, however the shipper being the dominant decision maker. Furthermore, the results of the exploded logit model reveal that the top five modal attributes that shippers consider most important are: reliability, transport cost, risk of damage, frequency of service and transit time. These results were subsequently employed to inform the shipper and carrier behavior studies. The third step entails the assessment of shipper behavior, where trip specific mode choice decisions are studied along five intra-urban origin-destination (O-D) paired routes (which would form the study corridors). Three of these corridors considered unimodal SSS, and the two considered intermodal SSS. Unimodal SSS was studied along the following corridors: Cape Town (South Africa)~ Walvis Bay (Namibia), Walvis Bay (Namibia) ~ Luanda (Angola) and Durban (South Africa) ~Beira (Mozambique); and intermodal SSS was studied along the following corridors: Durban (South Africa) ~ Harare (Zimbabwe) and Cape Town (South Africa) ~ Windhoek (Namibia). To develop the choice scenarios, d-efficient stated choice experiments were uniquely developed for each of the corridors with the following key modal attributes systematically varied and analyzed across respondents: service frequency, reliability in terms of arriving on time, expected delay, transport cost and transport time. Subsequently, the following choice models were developed: Binary Logit, Mixed Logit and Integrated Choice and Latent Variable Structure models for the unimodal corridors; and Multinomial Logit, Nested Logit and Cross Nested Logit models for the intermodal corridors. The results highlight that in addition to the modal attributes, mode choice decisions are driven by shipper characteristics and situational characteristics. Moreover, the unimodal SSS study reveals that underlying latent perceptions also influence freight mode choice decisions; while the intermodal SSS study reveal strong correlations in the intermodal SSS alternatives, which requires improved intermodal capability if SSS is to become competitive. The fourth step in the study entail the assessment of maritime carriers preference for SSS given varying levels of maritime conditions that include: dedicated freight volumes, income from freight, port dues discount, terminal handling fees discount and ship registration requirements. The results of an ordered logit model reveal that ship registration provisions and terminal handling charges are the most important to the development of SSS from a carrier side. Moreover, ship registration and maritime cabotage provisions require visitation to boost the participation of carriers in SSS. The last step of the study revisits the modeling results and considers their implications through the estimation of willingness-to-pay and attribute elasticities. The results were then employed to suggest policy actions and interventions to develop SSS.

abstract: It is well understood that innovation drives productivity growth in agriculture. Innovation, however, is a process that involves activities distributed throughout the supply chain. In this dissertation I investigate three topics that are at the core of the distribution and diffusion of innovation: optimal licensing of university-based inventions, new variety adoption among farmers, and consumers’ choice of new products within a social network environment. University researchers assume an important role in innovation, particularly as a result of the Bayh-Dole Act, which allowed universities to license inventions funded by federal research dollars, to private industry. Aligning the incentives to innovate at the university level with the incentives to adopt downstream, I show that non-exclusive licensing is preferred under both fixed fee and royalty licensing. Finding support for non-exclusive licensing is important as it provides evidence that the concept underlying the Bayh-Dole Act has economic merit, namely that the goals of university-based researchers are consistent with those of society, and taxpayers, in general. After licensing, new products enter the diffusion process. Using a case study of small holders in Mozambique, I observe substantial geographic clustering of new-variety adoption decisions. Controlling for the other potential factors, I find that information diffusion through space is largely responsible for variation in adoption. As predicted by a social learning model, spatial effects are not based on geographic distance, but rather on neighbor-relationships that follow from information exchange. My findings are consistent with others who find information to be the primary barrier to adoption, and means that adoption can be accelerated by improving information exchange among farmers. Ultimately, innovation is only useful when adopted by end consumers. Consumers’ choices of new products are determined by many factors such as personal preferences, the attributes of the products, and more importantly, peer recommendations. My experimental data shows that peers are indeed important, but “weak ties” or information from friends-of-friends is more important than close friends. Further, others regarded as experts in the subject matter exert the strongest influence on peer choices. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2015

abstract: Hotel amenities and their influence on consumer choice have been extensively studied by academics. These have largely focused on consumer preferences vacation modes and the psychographic characteristics of travelers. Revenue managers make practical use of this information by attempting to match available hotel rooms with traveler demands for accommodations, setting prices that maximize profits for the hospitality company. The experienced revenue manger is able to determine the most profitable price schedule for a room types across many distribution channels. This study was conducted to test the use of choice modeling for objectively assessing dollar values of three basic amenities for consumers (room type, kitchen availability and price). Researcher used paired comparisons modeled as a conditional logit. This study used market segmentation and choice modeling to determine the value of amenities for an aggregate group and 16 more homogenous groups. Market segmentation and choice modeling allowed this study to segment markets into more homogenous groups, and by doing that allowed for calculation of customer willingness to pay for additional amenities. Results from this study confirm that customers are willing to pay for kitchen $65.43 on top of their room value. All responders generally agree to liking an extra bedroom in their hotel room and they are willing to pay $37.39 more than for a studio room. A surprising result is that it seems based on the results that responders generally do not like to have a second bedroom and they are not willing to pay for it. By knowing customer willingness to pay, it can be assured that customers always feel they are getting a high value out of the transaction and increase the likelihood of future transactions. The significance of this research is the concrete numbers that can be, and already have been, applied immediately in the hospitality industry, and is positively impacting business revenue and customer experience. / Dissertation/Thesis / M.S. Community Resources and Development 2014

Although data collection through discrete choice experimentsconducted using surveys are commonly used in research, aimingto improve data quality is still serviceable and necessary. Three distinct experiments were conducted with the objectives of improving data quality by better tailoring experiments to market conditionsas well as decreasing complexity and fatigue. First, consumer willingness-to-pay(WTP) for yogurt attributes was estimatedusing a survey targeted to be nationally representative of the US.A novel approach was used to allow for self-selection into the choice experiment for commonly purchased types of yogurt.On average, respondentswere willing-to-paya positive amount for requiring pasture access and not permitting dehorning/disbudding for both traditional and Greek yogurt. Respondents had positive WTPfor Greek yogurt labeled free of high fructose corn syrup, and were willing-to-pay morefor low-fat yogurt when compared to nonfat for both yogurt types.<div><br></div><div> Second, anew WTP data collection method, employing component discrete choice experiments in place of traditional larger experimental designs,was proposedand compared to the traditional method to elicit yogurt consumer’s WTP for attributes in yogurt. The new WTP data collection method was designed with the objective of decreasing complexity by having respondents participate in fewer choice scenarios. Incidences of attribute non-attendance (ANA), a potential simplifying heuristic that results from complexity, occurred less frequently for all attributes in the new WTP data collection method with one exception. Exhibiting ANA for any attribute was negatively correlated with the time respondents took to complete the choice experiment.<br></div><div><br></div><div>Finally, through the use of a newbest-worst scaling(BWS)data collection method,consumer preferences for fluid dairy milk attributes were elicited and results as well as measures of data quality were compared to the traditional method of BWS. Nine attributes of fluid milk were included in this study: container material, rbST-free, price, container size, fat content, humane handling of cattle, brand, required pasture access for cattle, and cattle fed an organic diet. The top (price) and bottom (container material) attributes in terms of relative ranking did not change between the new BWS data collection method and the traditional BWS method. The new BWS data collection method resulted in fewer incidences of ANA for all attributes except one. There was not a statistical difference in the number of transitivity (an axiom of consumer theory) violators,between the new and traditional BWS methods.<br></div>