Search

The purpose of this study was to perform an equity analysis of the dual credit program at one central Texas community college, considering student demographic factors including gender, race/ethnicity, economic disadvantage, and school district type, to determine potential gaps in level of participation, frequency of completion, and academic achievement among targeted student subpopulations. Three elements of the program were examined. First, proportional dual credit enrollment for the demographic groups at each participating high school were assessed by means of a ratio between each school's dual credit cohort (involving total students for fall semesters 2017, 2018, and 2019) and its total student population. Student completion rates and academic achievement in two basic, first-year courses were also examined using aggregate data for public high school students who enrolled in HIST 1301, U. S. History I, and ENGL 1301, Composition I, for the fall semesters of 2017, 2018, and 2019. Completion rate data were analyzed by means of the chi-square test. A factorial analysis of variance (ANOVA) was employed to determine the significance of the relationship between the variables relating to academic achievement as measured by final course grade. The results of this study indicated lower proportional enrollment in the dual credit program for male, economically disadvantaged, and minority students. Further, statistically significant differences were discovered for course completion and academic achievement for HIST 1301 and ENGL 1301 related to economic disadvantage, race/ethnicity, school district type, and course delivery method. Recommendations proposed development of dual credit marketing materials focused on affordability, a pilot of Students of Color and First Generation Student programs for dual credit students, and expansion of Supplemental Instruction to online courses restricted to dual credit enrollment. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This thesis responds to calls for research into transnational investment decisionmaking by institutional investors and investment analysts. This issue has assumed a growing significanceo ver recent decadesd ue to the internationalisationa nd institutionalisationo f equity markets. Transnationald ecision-makingp rocessesa re also of import to the debate on the harmonization of international accounting. Using a questionnaires urvey and semi-structuredin terviews, the thesis investigates three related issues. First, it examines and compares the analysis techniques used by UK analystsa nd fund managersin analysingd omestica nd oversease quities. Second, it investigatesth e sourcesu sedb y fund managersa nd investmenta nalystst o inform domestic and transnational equity decisions, placing particular emphasis on accounting information and direct company contact. Finally, the research examines the attitude of the investment community to the international harmonization of accounting. In particular, it assessews hether fund managers'a nd investment analysts' decisions are affected by international accounting differences and gauges the views of these important users of accounting information on the harmonization process. The main findings are that fundamental analysis is the predominant appraisal technique in domestic and transnational equity analysis, irrespective of the country of domicile of the company being analysed. However, in order to facilitate international accountingc omparisons,a nalystsa nd fund managersu se ratios and measuresw hich are largely unaffected by international accounting differences when analysing overseasc ompanies. In particular, earningsb efore interest,t ax, depreciationa nd amortisation (EBITDA) is perceived as useful for cross-border comparisons. In respect of the information sources used to analyse overseas companies, direct company contact and the annual report remain the most influential overall. The financial statementsa rc clearly the most heavily utilised componentso f the annual report, yet there is some evidence that qualitative information in the annual report is more useful in overseasc ompanya nalysis. UK fund managersa lso rely on locallybaseda nalystsi n overseasc ompanya nalysisi n order to overcomep ossible information asymmetriesa nd for assistancew ith internationala ccountingd ifferences. However,f und managerse xpressedc oncerno ver analysts' independenced ue to analysts'r eluctancet o j eopardiseth eir relationshipw ith companym anagement. Although international accounting differences are not perceived to have a material effect on transnational investment decisions, the findings demonstrate substantial support from the financial community for harmonization. Indeed, analysts' and fund managersd' ecisionsa re themselvesa market-basedfo rce for harmonization,a s companiesu sing opaquea nd 'unfamiliar' accountings tandardsa re penalisedt hrough higher capital costs imposed to allow for additional accounting risk.

In 1973, the state of Florida implemented the Florida Education Finance Program (FEFP). The program was developed to distribute available funds to public school districts in a more equitable manner than the Minimum Foundation Plan it replaced. Almost immediately, the Florida Education Finance Program came under attack as less equitable and unnecessarily complex. The Florida Education Finance Program provides funding to local school districts based on the number students and the types of educational programs in which they are enrolled. Through a system of program cost factors and district cost differentials the Florida Education Finance Program adjusts for the type of students and the economic environment the district serves. But does it distribute funds equitably? This study was designed to answer one central research question: "Does the Florida Education Finance Program equitably distribute available funding to public school districts in the state?" In order to answer this question, a three phase method was implemented. In the first phase a chronological development of Florida's school finance plan was developed. The second phase employed research based statistical tools to gauge the distributional equity of Florida's mechanism for distributing available funds for Florida public school districts. The final phase used Pearson product-moment correlations to gauge the fiscal neutrality of the system. The study is patterned after a similar study completed in 1982 that called into question the distributional equity of the Florida Education Finance Program. The coefficient of variation, federal range ratio, McLoone index, Verstegen index, and Gini coefficient are all research based measures of distributional equity that are resistant to inflationary pressure. Each of these measures of distributional equity yielded the same results. They showed a high level of equity in the distribution of available funding to Florida's public school districts through the Florida Education Finance Program. This study left open the questions of adequacy and the role of the state in funding public education in the state of Florida to future study. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Metropolitan Planning Organizations (MPOs) regularly perform equity analyses for their long-range transportation plans, as this is required by Environmental Justice regulations. These regional-level plans may propose hundreds of transportation infrastructure and policy changes (e.g. highway and transit extensions, fare changes, pricing schemes, etc.) as well as land-use policy changes. The challenge is to assess the distribution of impacts from all the proposed changes across different population segments. In addition, these agencies are to confirm that disadvantaged groups will share equitably in the benefits and not be overly adversely affected. While there are a number of approaches used for regional transportation equity analyses in practice, approaches using large scale travel models are emerging as a common existing practice. However, the existing methods used generally fail to paint a clear picture of what groups benefit or do not benefit from the transportation improvements. In particular, there are four critical shortcomings of the existing transportation equity analysis practice. First, there is no clear framework outlining the key components of a transportation equity analysis at the regional-level. Second, the existing zonal-level group segmentation used for identifying target and comparison groups are problematic and can lead to significant biases. Third, the use of average equity indicators can be misleading, as averages tend to mask important information about the underlying distributions. Finally, there is no clear guidance on implementing scenario ranking based on the equity objectives. In addressing the first shortcoming of existing equity analysis practices, we present a guiding framework for transportation equity analysis that organizes the components of equity analysis in terms of transportation priorities, the model, and the equity indicators. The first component emphasizes the need to identify the priority transportation improvement(s) relevant for communities, as this guides the transportation benefits (or costs) to be evaluated. The second component is the model to be used for facilitating scenario analysis and measuring the expected transportation and land-use changes. The third component refers to the selection of equity indicators (ideally selected based on the transportation priorities identified), and the evaluation of these indicators. This three-part framework is also useful for outlining the research needs for transportation equity analysis. Among other key research needs, the literature indicates that the development of meaningful distributional comparison methods for transportation planning and decision-making and the use of more comprehensive measures of transportation benefit (for use as equity indicators) are critical.The primary contributions of this dissertation relate to the third component; we develop an advanced approach for evaluating transportation equity outcomes (as represented by the equity indicator(s)). Our proposed analytical approach to transportation equity analysis addresses the existing shortcomings with respect to zonal-level group segmentation and average measures of transportation equity indicators. In addition, our approach emphasizes the importance of scenario ranking using explicit equity criteria. Our approach leverages the disaggregate functionality of activity-based travel demand models and applies individual-level data analysis to advance the existing equity analysis practices.There are four steps in our proposed equity analysis process. The first step is to select the equity indicators to be evaluated and segment the population into a target group and comparison group(s). In this case we advocate for an individual -unit of segmentation and therefore individual-level equity indicators. This minimizes the biases associated with aggregate group segmentation and average equity indicators. The second step is to calculate the indicators from the model data output, which involves determining the exact measures (formulas) for the selected equity indicators. Here we advocate for measures that are comprehensive and sensitive to both transportation system changes and land-use factors, such as the logsum accessibility and consumer surplus measure. The third step in the process is to generate and evaluate distributions of the individual-level equity indicators. In particular, we advocate for the use of what we refer to as the "Individual Difference Density" comparison, which compares distributions of individual-level changes for the population segments across the planning scenarios. This comparison allows for the "winners" and "losers" resulting from the transportation and land-use plans to be identified. The fourth and final step in the process is to identify equity criteria (associated with the chosen equity standard (objective)) and rank the planning scenarios based on the degree to which they meet the equity criteria.We present two conceptual demonstrations of the advantages of distributional comparisons, relative to average measures. The first case uses a synthetic data set and simple binary mode choice model to show and the second case uses an empirical data set (the 2000 Bay Area Travel Survey) and more sophisticated mode choice model. These demonstrations show that distributional comparisons are capable of revealing a much richer picture of how different population segments are affected by transportation plans, in comparison with average measures. Further, distributional comparison provides a framework for evaluating what population's characteristics and conditions lead to certain distribution transportation outcomes.Our proposed process for regional transportation equity analysis is subsequently applied in a case study for the San Francisco Bay Area. We evaluate joint transportation and land-use scenarios modeled using the Metropolitan Transportation Commission's state-of-the-art activity-based travel demand model. We demonstrate the power of individual-level data analysis in a real-world setting. We calculate individual-level measures of commute travel time and logsum-based accessibility/consumer surplus using the model output and compare the scenario changes across income segments. We generate empirical distributions of these indicators and compare the changes associated with the planning scenarios for low and high income commuters. Further, we apply criteria for a set of equity standards (which represent alternative equity objectives) and rank the planning scenarios. There are four key takeaways from this case study. First is that our results show a significant difference in equity outcomes when using the individual-level population segmentation approach, compared to using the zonal segmentation approach done in practice. In fact we find opposite results. For average commute travel time, the Metropolitan Transportation Commission's zonal segmentation approach indicates that low income commuters are worse off than all other commuters, while the individual segmentation approach (in our case) indicates that low income commuters are significantly better off than high income commuters. While the underlying causes for these results warrant further investigation, we hypothesize that this difference is due to the fact that the zone-based approach only captures 40% of the target (low income) group. The individual-level segmentation approach is able to capture 100% of the target group. Second is regarding the equity indicators evaluated. The commute travel time indicator results indicate that low income commuters are better off than high income commuters, while the accessibility/consumer surplus results indicate that low income commuters are worse off than high income commuters. The underlying causes for these results warrant further investigation, but we hypothesize that this difference in results to due to the fact that the logsum accessibility/consumer surplus measure by design is able to capture transportation and land-use related factors, while the travel time measure only captures one dimension of transportation user factors. Focusing on travel time may be misleading because it does not fully capture the true benefits of the transportation scenarios. Third is regarding the use of distributional comparisons, relative to average measures. We find that distributional comparisons are much more informative than average measures. The distributional measures are capable of providing a much richer picture of individuals-level transportation impacts, in terms of who gains and who loses due the transportation planning scenarios. Using the accessibility/ consumer surplus measure, the Individual Difference Densities show that as many as 33.3% of low income commuters experience losses, compared to 13.4% for high income commuters. Finally, we make the case that the use of equity standards for scenario ranking plays an important role in the equity analysis process. Our results show that different equity standards result in different rankings for the transportation planning scenarios. This points to the need for agencies (and communities) to make conscious decisions on what equity standard(s) should be used and apply this/these in the scenario ranking process.This dissertation work includes the first known full-scale application of a regional activity-based travel model for transportation equity analysis that involves distributional comparisons of individual-level equity indicators and scenario ranking based on equity criteria. We find that while the existing practice is to use average measures to represent how difference are affected by transportation plans, distributional comparison are able to provide for a richer evaluation of individual-level transportation impacts. Distributional comparisons provide a framework for quantifying the "winners" and "losers" of transportation plans, while average measures and be misleading and uninformative. We make significant progress with regard to evaluating equity indicators (part three of the guiding framework). However, our proposed process is flexible and can be extended to include a number of additional advances, including more environmental and long-term land-use related equity indicators (e.g. emissions exposure, gentrification and displacement risk, employment participation, etc.) and additional population segments (e.g. age, ethnicity, household type, auto-ownership class, etc.). Among other important research directions, our analytical framework for regional transportation equity analysis can be applied to investigating why certain groups are more likely to be "losers" and what factors of transportation planning scenarios to modify in order to arrive at a more equitable transportation and land-use plan.

Background: Access to quality public transportation is critical for employment, especially for low-income and minority populations. This research contributes to previous work on equity analysis of the U.S. public transportation system by covering the 45 largest Metropolitan Statistical Areas (MSAs) and their counties. Objective: This study analyzes job accessibility of transit commuters in the 45 largest MSAs to assess the existing differences in accessibility between Census-defined socioeconomic status (SES) categories. Method: 2014 Census demographic data were matched to a previously published 2014 dataset of transit job accessibility at the Census Block Group level. Transit equality and justice analyses were performed based on population-weighted mean job accessibility and SES variables. Results: The findings suggest that within individual MSAs, the low-income populations and people of color have the highest transit job accessibility. However, in certain MSAs with high job accessibility, such as New York, Washington, D.C., Chicago, and Houston, there is a significantly disproportionate access to public transportation based on income. Variables such as income, and the use of personal vehicle, are found to have a statistically significant negative impact on job accessibility in almost all MSAs. The percentage of White workers has a significant impact on job accessibility in upper-mid-density MSAs and high-density MSAs. The percentage of the population with limited English speaking ability is not a significant determinant of job accessibility except in lower-mid-density MSAs. Disparities by income are greater than disparities by race. Racial disparities increase by MSA size and density controlling for income. The findings suggest that planning for public transportation should take into account risks, benefits, and other equally important aspects of public transportation such as frequency, connectivity, and quality of service.

This report assesses the potential economic, environmental, and social impacts of the Let’s Move Nashville transit plan on the North Nashville neighborhood by modeling and comparing implementation scenarios for 2020 and 2030 versus predicted business as usual trends for the same years. The Nashville Metropolitan Transit Authority currently provides North Nashville with a higher level of service relative to the rest of the city. However, system features such as long headways, huband-spoke routes, and a lack of regional connectivity impose limitations on residents’ mobility. Let’s Move Nashville seeks to address each of these issues with the goal of connecting more people to educational, employment, and recreational opportunities. There is much at stake for North Nashville, a historically neglected African-American community beginning to experience redevelopment and an influx of new residents. Our analysis examines the extent to which these goals are achievable for the neighborhood while laying out a methodology that can be replicated for areas throughout Nashville.

This research details the application of a public transportation accessibility model using travel time as the accessibility measure. The primary objective was to develop a multi-modal network model for detecting change in travel time at the household level as the result of the implementation of new rapid bus routes. This method is situated in the accessibility measures of transportation equity, a focus of research in the broader field of transportation geography. The level of detail that current accessibility studies use to evaluate public transit are not detailed enough to capture travel time changes at the household level and through varying time periods throughout the day. The result of this research is the successful application of this network to highlight the travel time changes at the household level of new transit investments. The multi-model network was developed in ArcGIS and accounts for the walking time from home to the bus stop, the waiting time at a bus stop, the travel time on the bus, and any necessary transfers. Both peak and off-peak time periods were modeled. The application of this network to a case-study of the Westside of Albuquerque, NM demonstrates how household-level effects of new transit investments can be modeled, with implications for a finer level of detail for social justice studies. Specifically, the Northwest quadrant of Albuquerque benefits through greater time savings compared to the Southwest quadrant, though both sections are in the service area of two new significant public transportation investments. This is despite the greater need in the Southwest quadrant for public transportation based on socio-economic characteristics.

It is generally agreed that the relationship between school district size and educational costs is U-shaped with higher costs found in very small and very large districts. For small districts, higher costs are a function of their inability to take advantage of economies of scale, primarily in the area of staffing. Several scholars argue that these higher costs are a vertical equity issue, and that small district enrollment is a legitimate factor warranting additional state funding. While many scholars and policy makers object to providing additional funding based on small enrollment, over half of the states provide supplemental funding to districts with low enrollments or districts located in geographically isolated areas. Although Michigan provides substantial funding each year to districts aimed at improving vertical equity for children, the State School Aid Act does not recognize the potentially higher costs incurred by small districts in rural areas of the state. If costs are indeed higher for these districts, serious questions arise about the equity of Michigan's school funding program for children attending small rural school districts. This study examines school finance equity for children attending districts with low enrollments in rural Michigan. Data covering a ten-year period (1984-85 through 1993-94) are analyzed to determine whether there has been systematic and substantial differences in resource levels and outcomes between small rural districts and other types of districts. The analysis examines fiscal and human resource inputs, student achievement outputs, and patterns of resource allocation. Results suggest that children attending small rural school districts in Michigan were discriminated against under the state funding program during the ten-year period. Although there appears to be no inequity in fiscal inputs, data on human resources and student achievement indicate that children attending small rural schools have access to lower quality professional staffs and achieve at lower levels than those in other types of districts. In addition, the analysis on resource allocation patterns indicates that small rural districts allocate lower proportions of current operating expenditures to support areas which may negatively influence student achievement. Recommendations for policy remedies and future research are presented.

This study of school finance equity in Michigan had three purposes: (1) to develop an underst and ing of equity aspects in the Michigan school finance system, (2) to assess the effects of school finance reform in the 1970's in Michigan in terms of fiscal equity, and (3) to identify a series of factors that need to be taken into account by policy makers committed to developing a more equitable school finance system in the late 1980's. Three equity principles were employed in this study: horizontal equity, equality of opportunity, and vertical equity. For the period studied, three questions were addressed: (1) Has school finance equity in Michigan improved, worsened, or stayed the same over the years studied? (2) Does the equity measure make a difference when holding the equity object constant? and (3) Does the equity object make a difference when holding the equity measure constant? The study generated a series of empirical findings. When holding the equity object constant, the equity measure did make a difference in terms of horizontal equity, equality of opportunity and vertical equity. The only two exceptions were equality of opportunity with respect to district wealth for the dollar-input-equity objects, and equality of opportunity with respect to race for the human-resource-input-equity objects. When holding the equity measure constant, the equity object also made a difference for all three equity principles. These findings lead to the conclusion that equity trends in Michigan school finance over the years from 1978-79 to 1986-87 were determined largely by the equity measure or the equity object employed. Different combinations of equity measures and equity objects portrayed different equity pictures. and different values are inherent in both the choice of equity objects and the choice of equity measures. Since this is the case, we should leave much freedom of choice to researchers and policy makers, urging only that they set forth clearly the values inherent in their choices.

The following research completes several statistical analyses of per pupil expenditure data in the Commonwealth of Virginia to assess the degree of fiscal equity in the statewide finance model for public elementary and secondary education. Five years, between 2004 and 2020, were selected for analysis to examine whether trends noted in a 2005 study of fiscal equity in Virginia have remained constant or whether the degree of equity has increased or decreased. A historical overview of the funding of public schools in Virginia and revisions to the Virginia Constitution and its Education Articles provide information about the development of public education in Virginia. This commentary is followed by an explanation of the current funding model, Standards of Quality formula, and legislative criticism of the design elements thereof. School finance reform litigation from across the nation is then reviewed to demonstrate how the constitutionality of state public school finance models has been challenged in both federal and state courts over time. The school finance litigation discussion begins with the broad topic of equal protection guarantees in the federal Constitution and how those guarantees shaped early equity lawsuits. A survey of school finance reform cases is presented to show a progression from equity suits to adequacy suits. The litigation commentary concludes with a discussion of the most recent school finance case in Virginia, Scott v. Commonwealth (1994). After establishing the precedents for the analysis of state funding models, a series of dispersion statistics are calculated based on per pupil expenditures for each of the 132 school divisions in Virginia. These statistics include Range, Restricted Range, Coefficient of Variation, Gini Coefficient, and McLoone Index. The findings of the 2004-2020 analyses are compared to the findings of the 2005 study of fiscal equity in Virginia. The noted results of the analyses have implications for policy makers in the Commonwealth.

In recent years, the popularity of "resilience" has exploded in both academic and policy discourse. However, current transit resilience research only focuses on infrastructure resilience, giving little consideration to its interaction with passengers. Moreover, even for transit infrastructure resilience, there is no comprehensive framework for assessing resilience to reflect its scale-sensitive attributes. Therefore, to make cities and transit systems move from a vulnerable state to a more resilient one, this thesis seeks to advance our understanding of transit resilience performance from macro, meso and micro levels, focusing on the interactions between infrastructure and passengers. Additionally, this research is motivated by the need to bridge the theoretical gap between the transport network and complex network science disciplines. This research draws on a multiple-level framework to investigate stations' roles in maintaining London transit resilience at macro, meso and micro levels from a network perspective. At macro level, this research examines the overall transit resilience performance by using percolation modelling and fault tolerance. At meso level, resilience as a sub-network feature is examined by unravelling the travel structure and identifying the stations' roles in maintaining resilience within and beyond metropolitan levels through community detection and Z-P scores. This research evaluates each station's role in maintaining resilience based on resilience and criticality matrix at micro level. In addition, this research seeks to answer for whom we should build resilience by including the transport equity analysis. In line with the conceptual framework, this research develops an R package1, ResilienceNet, which provides a reproducible and accessible tool to examine transit resilience at multiple levels. Three research findings can be derived from this research. First, the London transit is resilient against random disruption but relatively vulnerable to centrality-based attacks. The conventional network approach would understate or overstate the resilience performance at the macro level. Second, the empirical results justify the necessity to include an evaluation of resilience at the meso level, as the role of stations for ensuring resilience varies differently within and beyond sub-city communities. Third, at the micro-level, this research identifies 25 stations and some vulnerable groups, such as young and low-income groups, which are more likely to be affected by transit disruptions and need more attention. The results derived through analysis could be used to facilitate transport and urban planning at multiple levels, thus moving cities and transit systems from a vulnerable to a more resilient state.

Shared Micromobility consists of a variety of shared, non-automotive personal modes, from traditional station-based bike-sharing to cutting-edge dockless electric scooters (e-scooters). The rise of shared micromobility in the U.S. was attributed to scooter-share programs in many cities such as Austin and Los Angeles. Research gaps exist in shared micromobility travel behavior for many reasons. First, it has been well integrated with other high-tech innovations such as smartphones and mobile payment. Thus, the travel data is passively collected from location-based devices rather than travel surveys. Second, it is a privatized business in which private vendors own, operate, and manage fleets based on profit-driven business models. As a result, transportation planners have limited data access and oversight power in decision-making. The booming yet under-regulated market has led to adverse social outcomes threatening city aesthetics and transportation equity due to overcrowding vehicles on streets and irresponsible riding and parking behaviors. The dissertation uses the scooter-share program in Austin, Texas, and applies data-driven approaches in urban informatic to examine e-scooter travel behavior at a small scale as well as its transportation equity implications. Research findings show that scooter travelers choose shorter routes based on when and where they travel. In addition, stores with higher spatiotemporal correlation with scooter use tend to attract visitors who reside in lower-income neighborhoods and spend less time during their visits. Interestingly, statistical results show an insignificant difference in visitors’ home distance. The equity analysis suggests that Austin scooter-share has the potential to benefit carless populations by deploying more vehicles in their neighborhoods. However, public space obstruction by overcrowding scooters imposed more mobility difficulties on people with physical difficulties and older populations. The dissertation contributes to the field by shedding light on the opportunities and challenges in shared micromobility planning under a larger umbrella of transportation planning.

The digitization and automation of transportation systems is fundamentally transforming the transportation landscape, creating important opportunities and challenges for improv- ing the sustainability of transportation worldwide. There are more travel options to choose from than ever before, with real-time information on the cost and time trade-offs between alternative routes and modes of travel available in the palm of a hand. In particular, shared on-demand mobility services such as transportation network companies (TNC) (e.g., Lyft, Uber), bikesharing, and microtransit offer affordable and convenient alternatives to personal auto ownership that can complement or supplement existing public transit services. In addition, these services may aid in reducing existing inequities in access to fast and reliable transportation. However, despite the potential that innovative shared mobility service mod- els bring forth to improve the sustainability of transportation, the inefficiencies of fleet-based services such as TNCs and the low adoption rates of pooled rides that transport multiple travelers in the same vehicle have contributed to worsening congestion in several regions across the United States. Meanwhile, the design and deployment of transportation demand management (TDM) strategies has not kept pace of disruption nor the corresponding evolution in travel behavior.In light of the pressing need to improve the sustainability of a rapidly evolving transportation ecosystem, this dissertation contributes to the theory, methodology, and state of knowledge of optimal mechanism design for multi-faceted TDM strategies. With a focus on congestion pricing strategies, this research aims to facilitate the design and analysis of data-driven TDM strategies that incorporate a multitude of policy levers (e.g., congestion pricing, multi-modal incentives, public transit operations) using a simulation-based multi-objective optimization approach to inform decision-makers about the inherent trade-offs between congestion and emission reductions, economic feasibility, and transportation equity. In particular, I focus on the optimization of congestion pricing and targeted incentive schemes in multi-modal transportation networks including pooled ride options. Congestion pricing aims to reduce congestion by charging road users for driving on congested roads. A review of the various aspects of the transportation pricing optimization problem as studied in the literature is presented in Chapter 2, including the specification and analysis of various dimensions of demand sensitivity, congestion pricing structure and charging zone design, optimization objectives, optimization approaches, and transportation equity analysis. Studies of the optimization of pricing structures and charging zones for congestion pricing schemes have established that greater toll levels and charging zone coverage produce greater reductions in driving, which is deemed beneficial with respect to total system-wide travel time reductions. However, as is pointed out by public acceptance studies and literature on the equity implications of congestion pricing, the cost burden of congestion mitigation is disparately borne by lower-income individuals who are the most likely to be financially incentivized to adopt less desirable alternatives to driving. Few congestion pricing optimization studies have incorporated transportation equity objectives; none have included equity in addition to other efficiency and environmental objectives. The contributions of this dissertation to the literature on congestion pricing optimization span the theory of optimal mechanism design for multi-objective congestion pricing strategies, methodology for simulation-based multi-objective optimization of multi-faceted TDM strategies, and empirical understanding of congestion pricing strategies optimized with respect to multiple policy objectives.Mechanism Design for Optimal Congestion Pricing PoliciesChapter 3 establishes that equity-based objectives and the inclusion of monetary incentives for the adoption of driving alternatives are feasible strategies for tackling the equity issues inherent in congestion pricing optimization (i.e., the disparate distributions of increased costs for lower income drivers and reduced travel times for higher income drivers). In this chapter, I formulate a bi-level optimization problem to compute optimal link- and mode-specific tolls and targeted mode-specific incentives (i.e., direct monetary transfers) using aggregate-level information on the flows of network users using various modes of transportation. This work contributes to the theory of congestion pricing optimization by proving the existence of optimal multi-modal congestion pricing schemes including both tolls and monetary incentives that are optimized with respect to equity-focused objectives defined on the basis of the distributional impacts of the pricing scheme across travelers. Several functions inspired by different theories of justice are presented as alternative social objective functions, including: 1. Utilitarian: maximize the sum of individual utility (i.e., a quasilinear function of travel time, cost, and other factors weighted by the individual demand sensitivity to each) of travel, 2. Egalitarian: maximize the sum of individual utility using the average demand sensitivity to weight travel time cost, 3. Equality of Opportunity: maximize a weighted sum of the individual utility, again using the average demand sensitivity, with weights applied to the overall utility that are scaled according to a societal ordering of disadvantage across network users (e.g., weights decrease with income, weights increase for residents of historically underserved neighborhoods or members of historically underserved communities), and 4. Rawlsian: maximize the minimum individual utility of travel, again using the average demand sensitivity. Consideration of the three latter social choice functions offers a deviation from the traditionally utilitarian view on congestion pricing optimization. Implementation of the bi-level optimization problem presented in chapter 3 and analysis of the impacts of each of the alter- native objective functions on the optimal traffic assignment, toll, and incentive scheme are under development for future work.The Berkeley Integrated System for TRansportation Optimization (BISTRO)In chapter 4, I present a methodological framework for regional-scale multi-objective optimization of transportation systems using agent-based simulation (ABS), called the Berkeley Integrated System for TRansportation Optimization (BISTRO). BISTRO is an open source transportation planning decision support system that enables the simultaneous optimization of multiple transportation system interventions, including adjustment to public transit operations and vehicle fleet mixes as well as pricing strategies, using state-of-the-art activity- based travel models and ABS to explore the individual- and system-level impacts of packages of TDM strategies. In contrast to the state of the practice in transportation planning of analyzing a select few strategies across a discrete set of scenarios, the simulation-based multi- objective optimization approach that I explore in this dissertation using BISTRO leverages the machine learning principles of data exploration and exploitation to explore the interactions of policy design parameters and their outcomes to a much greater extent, thereby producing richer insights about the optimal trade-offs between the various competing objectives of policy design and implementation. BISTRO was developed in partnership with researchers at the Institute of Transportation Studies at UC Berkeley and Lawrence Berkeley National Laboratory as well as a project team at Uber, Inc. I led the development of the objective function design for BISTRO by translating municipal and regional transportation goals into quantitative optimization objectives with consideration for social, environmental, and eco- nomic sustainability implications and testing the efficacy with which these key performance indicators (KPIs) functioned when applied in simulation-based multi-objective optimization. In addition, I led the design of the output analysis and visualization platform for BISTRO.My individual contributions are presented in the Scoring function design, Inputs, and Output analysis and visualization sections of chapter 4 in addition to the corresponding sections of Appendix A. Chapter 4 also presents the results of a pilot study of BISTRO that was conducted as a machine learning competition hosted within Uber Technologies, Inc. The key lessons learned from the design and execution of this pilot study demonstrated BISTRO’s utility as a human-in-the-loop cyber-physical system: one that uses scenario-based optimization algorithms as a feedback mechanism to assist urban planners with iteratively refining objective function and constraint specification on multi-faceted intervention strategies (e.g., pricing, public transit scheduling, vehicle fleet mix). The remainder of my dissertation research focuses on the application of BISTRO for the design of congestion pricing strategies with mode-specific incentives.Congestion Pricing Optimization Case Study I: Sioux FauxThe fifth chapter of this dissertation presents a case study of the optimization of congestion pricing policy design using BISTRO and the Sioux Faux benchmark model presented in Chapter 4. The study exemplifies how the granularity offered by activity-based travel models can be leveraged to enhance the interpretability of multi-objective transportation policy optimization through rich analyses of the effects of policy design on both individual- and system-level outcomes. The location and size of a circular charging zone as well as two different pricing schemes (a cordon fee and a cordoned mileage fee) were encoded as inputs to an ABS with an activity based travel model of 15,000 travelers. Through an analysis of the effects of various weighting schemes across KPIs representing congestion- (i.e., total VMT, average vehicle hours of delay (VD) per passenger trip, and total GHG emissions), social- (i.e., average generalized travel cost burden of work and secondary trips), and revenue-based (i.e., total toll revenue) objectives, I developed a method for interpretation of the inherent trade-offs in transportation policy optimization and demonstrate the importance of cultivating transparency in policy decision support systems that use black-box optimization in order to produce explainable, defensible policy strategies.I find that cordoned mileage fees Pareto-dominate cordon tolls, meaning they produce greater improvements across all objectives studied. I estimated an empirical 3-dimensional Laffer curve for each pricing strategy, representing the concave relationship between the main components of the pricing scheme design - the average toll paid per driving/TNC trip and the coverage of the cordon (in % of trips affected) - with the toll revenue and the driving/TNC mode share. I analyzed the relationships between these design parameters and the other KPIs, demonstrating the utility of simulation-based multi-objective optimization for human- in-the-loop transportation policy design. I find that the prioritization of congestion reduction poses a challenge for congestion pricing optimization in that it may result in unnecessarily large mode shifts away from driving. This significantly worsens the cost burden of travel (i.e., the total generalized cost of travel - including both travel cost and VoT-weighted travel time - as a portion of individual income) by disproportionately increasing the travel times of lower income individuals in a manner that may arguably be incommensurate with the benefits of congestion mitigation. Finally, I explore the role of weighting schemes in shifting the priority of an optimal congestion pricing scheme to social equity while maintaining congestion mitigation and toll revenue, the latter of which may be used to further improve the transportation system.Congestion Pricing Optimization Case Study II: The San Francisco Bay AreaIn chapter 6, I build upon the Sioux Faux case study by designing a Pareto-based optimization of a cordoned mileage fee and incentive scheme for the City and County of San Francisco using BISTRO. I developed and calibrated an activity-based model of 50,000 commuters in the San Francisco Bay Area for the case study, including both ride alone and pooled on-demand rides in 7-seater vehicles and a multinomial logit mode choice model with coefficients estimated from a general population stated preference survey of San Francisco Bay Area residents that I conducted in 2018. I applied the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm to optimize the hypervolume of the Pareto frontier of various transportation system objectives, including the minimization of the total VMT, average VD per passenger trip, total GHG and PM2.5 emissions, average cost burden, and net public revenue resulting from the congestion pricing and incentive scheme. Rather than produce a single ’optimal’ policy design, the approach taken in this study produces rich insights regarding the correlations between policy design parameters and the corresponding outcomes and produces a set of policy design options representing the optimal trade-offs between policy objectives.I find that the inclusion of monetary incentives for pooled TNCs and public transit in a congestion pricing scheme for San Francisco improves all KPIs under consideration except for the net public revenue produced by the scheme, which may still be improved with respect to a baseline with neither tolls nor incentives. Incentives serve to amplify the operational and environmental benefits of congestion pricing by offering an additional positive financial incentive for the use of pooled modes of transportation (in addition to the negative incentive posed by the congestion charge), while reducing the average cost burden of travel across the region. This is particularly important in the San Francisco Bay Area where a majority of commuters traveling in the City of San Francisco either live or work in one of the other eight counties of the region where there is comparatively poor access to alternatives to driving. Incentives aid in compensating the most cost-sensitive drivers for the considerable travel time or cost increases they experience when shifting to public transit or pooled on-demand rides, respectively. This study demonstrates the value of Pareto-based optimization using ABS for developing dynamic TDM strategies capable of adapting to ever-changing travel behavior and policy priorities. I conclude this chapter with a discussion of actionable insights regarding the potential of congestion pricing and incentive schemes to achieve a variety of policy objectives, particularly in the presence of high-capacity pooling services such as microtransit.Summary of Key Findings and Research DirectionsThis dissertation develops theoretical, methodological, and empirical contributions to the field of transportation engineering, specifically for the design and optimization of sustainable and equitable congestion pricing strategies. The multi-objective simulation-based optimization framework I have developed and applied using BISTRO lays the foundation for the continued development of algorithmic transportation policy decision support tools.The use of activity-based travel models and ABS enables the examination of the network effects of pricing strategies, which are particularly relevant when considering 1) the regional impacts of pricing on mode shifts, since behavioral responses in one sub-region can influence those of another through the secondary effects of the pricing strategy on travel times and the level of service of alternative modes (e.g., reliability of public transit or on-demand ride services), 2) on-demand vehicle fleet operations, which are designed to optimize the revenue generated from the strategic positioning and dispatching of vehicles across a market, and 3) the economies of scale of pooling, which generate a virtuous cycle of higher pooled ride match rates, better pooled ride service reliability and affordability, and reduced costs with respect to greater density of pooled rides in any given area and time period. This dissertation presents the first multi-objective congestion pricing and incentive scheme optimization studies using ABS with an activity-based travel model that includes numerous travel options to access/egress public transit and, in the second case study, includes pooled on-demand ride services with survey-based estimates of the sensitivity of demand for pooling incorporated into the behavioral dynamics of the model.This research has produced applicable insights for both the immediate- and long-term. While congestion pricing policies are currently under development in cities across the globe, there is also a broad trend toward ’smart city’ technologies that has been underway for over a decade, with increasing integration of sensors and connected control mechanisms to automate the operation of certain civil systems such as water, energy, and traffic management. In addition to updating traffic control systems (i.e., automated ramp metering, traffic lights, etc.), transportation agencies are integrating fare payment across transportation services and facilities and considering mobility as a service (MaaS) models that bundle transportation services and enable the seamless allocation of credits and incentives to nudge travel behavior.The results of the two congestion pricing optimization case studies (chapters 5 and 6) con- tribute valuable insights for the ongoing development of transportation pricing strategies such as congestion pricing and MaaS. The San Francisco Bay Area case study in chapter 6 suggests that low-income incentive schemes can drastically improve the efficacy of congestion pricing to achieve congestion mitigation and environmental emissions reductions objectives while also reducing the average cost burden of travel. These benefits come at the cost of reduced public revenues that could otherwise be used to expand and improve public transit access. Targeted public transit improvements may also serve to reduce the cost burden of congestion pricing schemes by reducing the travel time for individuals who shift away from driving due to the congestion charges. However, in a region as large and varied as the San Francisco Bay Area, strategizing which investments to make and where to make them is a challenging task. The optimization of targeted reinvestment of congestion charging revenue into public transit is an area ripe for future work using BISTRO. As was demonstrated in the pilot BISTRO study (chapter 4), BISTRO can be used to optimize the public transit vehicle fleet mix, route schedules and headways, and fares in addition to the pricing parameters optimized in the congestion pricing case studies.In addition to generating practical insights for the design of congestion pricing schemes, this dissertation demonstrates the challenges and opportunities for algorithmic policy design. Building upon the lessons learned from the BISTRO pilot study presented in chapter 4, the methodological approach developed in chapters 5 and 6 focuses on improving the explainability and interpretability of simulation-based multi-objective optimization by using BISTRO to explore the various aspects of the transportation system optimization problem, including the:• design of policy input parameters and an appropriate search space that is both large enough to enable exploration of potentially unexpected solutions and constrained enough to avoid the unnecessary use of resources exploring extreme solutions with undesirable outcomes, • specification of KPIs that are both mathematically sound and representative of realistic policy objectives (spanning congestion mitigation, equity, and financial feasibility) with which to guide the optimization algorithm to find insightful solutions, • development of a realistic and meaningful travel model that includes key behavioral, physical, and operational dynamics relevant to the optimization problem at hand (e.g., configuring the mix of fuel consumption and seating capacity in the public transit and on-demand vehicle fleets), • implications of scalarization schemes and the impacts of weighting conflicting policy objectives on the results of optimization, and • application of nuanced analysis to interpret and communicate the implications of the optimization results at both the individual- and system-levels.The research presented in this dissertation offers a launching point for future work in the field of transportation policy design and optimization. BISTRO offers the capability to expand the scope of the congestion pricing optimization problem I have studied thus far to include aspects such as induced demand, vehicle-based charging, dynamic pricing structures, parking pricing, and public transit investment. In addition, BISTRO may be used as a tool to develop methodological approaches for robust transportation system optimization that produces strategies that perform optimally across a variety of scenarios. Such scenario-based optimization may be conducted across multiple baselines (as opposed to a single BAU), each representing a potential variation in the distribution of land use, mobility preferences, or other factors that may affect the distribution of transportation supply or demand. BISTRO can also be used to develop algorithms for transfer learning of policies across varying scenarios and geographies. Such methodologies would characterize hyper-parameters representing factors in the transportation network, services, and demand profile that are significant across scenarios or geographies, thereby increasing the efficiency and robustness of the optimization.Lastly, a policy optimization framework relies heavily on the design of the objective function. The case studies in this dissertation applied just one of the three alternative social choice functions presented in chapter 3. More work is needed to continue developing an equitable approach for defining and operationalizing the notion of social optimality with respect to both equity and sustainability. The level of service KPIs implemented in BISTRO, including measures of public transit vehicle crowding, transportation cost burden, and accessibility are a first step in this regard, and the research I have conducted thus far on the impacts of weighted and Pareto-based optimization have illuminated the challenges in automating public policy decision-making.

In many countries across sub-Saharan Africa, health inequities are widely prevalent, particularly for poor children who are at higher risk of ill-health and death, if critical child survival interventions are not accessible. Although there has been previous research carried out on the existence of health inequities and the possible strategies required to address them, there is relatively limited research that has specifically focused on the implementation of these policies and programmes (Scott et al., 2012). The purpose of this study is to explore how preventive health care services have been structured for reaching the poor. The specific aim is to understand the extent to which equity issues have been considered in the planning and implementation of a key programme of the Ministry of Health of Ghana, the Community Growth Promotion (CGP) Programme. A qualitative case study of CGP was carried out using health policy analysis. In-depth interviews were conducted with stakeholders involved in CGP at the national, sub-national and community levels, and focus group discussions were undertaken with caregivers of children less than five years of age. The sub-national data were collected from the Central Region of Ghana. The interviews were transcribed and analysed using the framework approach (Ritchie & Spencer, 1994). The key findings of the study focused on three themes: philosophy of equity; analysis of equity issues in CGP, and uptake of health interventions amongst the poor. Health inequities were widely recognised by stakeholders; however there were different drivers and different responses to equity issues at the national and sub-national levels. Although equity was not the foremost consideration in planning and formulating the CGP, it was indirectly addressed in content, focus and structure of the programme. The study further revealed that there are household, community, health service provision and health policy implementation factors that affect the delivery and uptake of services among the poor. In doing so, this thesis contributes to the literature on health equity and provides guidance for developing countries on implementation of strategies to improve health outcomes for the poor.

With the focus on universal health coverage (UHC) and the inclusion of an equity target in the newly adopted Sustainable Development Goals, equity analysis is becoming prominent in the evaluation of health policies in low-income countries. Focusing on Cambodia, the overall aim of this thesis is to examine spatial and socio-economic equity patterns in maternal health services between 2000 and 2014, a period characterised by extensive health systems reforms. Inequities of maternal health services are examined on one UHC dimensions, population coverage in terms of use and quality of services. Using household surveys and the population census, inequities are measured using econometric analysis, logistic multilevel models and small area estimation. Results show that irrespective of residency, inequities in the use of services decrease over time as population coverage increases. However, use remains pro-rich in 2014. The pro-rich bias in urban areas is particularly strong when examining inequities of the quality of services received. In rural areas, inequities in quality by socio-economic status are low as most of the health services provided fail to meet the quality criteria applied. Moving beyond the urban/ rural dichotomy, large spatial inequities in the utilisation of services are for the first time observed at small areas, suggesting that health system characteristics and other socio-economic determinants manifest themselves geographically. Findings are discussed in the context of inverse equity hypothesis, health system reform and socio-economic development. The thesis concludes that there is partial support for the inverse equity hypothesis and that the hypothesis may have a spatial dimension that has previously not been captured. It also concludes that health system reforms targeting the supply-side of service provision probably had an effect on reducing inequities, but that socio-economic development including increased household wealth cannot be excluded as a major contributor to increased service uptake.

E-bike incentive programs are being utilized across the United States to encourage the adoption of active transportation. This study assesses the impacts of three e-bike rebate programs in Northern California using survey results by the three agencies that administered the programs. Through this research, I answer three research questions: “How has e-bike ownership impacted the mode choices, trip purpose, and travel frequency of new adopters”, “How much do e-bike rebate recipients reduce their transportation-related GHG emission?”, and “How did the design of each program impact who was able to participate and the program outcomes?”. To answer these questions, we explored survey responses through descriptive statistics and undertook an estimation of GHG emissions reductions. I decided against more complex data analysis given data quality issues that arose during the cleaning process. Despite that, our analysis revealed changes in travel behavior, car travel replacement, the impact of program designs, and various equity impacts. E-bike recipients reported more regular bike use after getting their e-bike, although their frequency of bicycle use began to decline in the long-term while remaining above previous rates. Respondents also reported high rates of occasional car trip replacement (1-3 times per week and 1-3 times per month), indicating that e-bikes substituted occasional car trips. While there was evidence of regular car trip replacement, the vast majority of e-bike use in our sample was for recreational travel. Given that this data was collected during the COVID-19 pandemic when many restrictions were still in place, these high rates of reported recreational travel were unsurprising. Our GHG reduction analysis estimated a monthly diversion of 12-44 kilograms of CO2 per rebate participant, similar to the GHG emissions reductions observed in other research. We conclude with an equity analysis that explores how program design influenced who participated in these rebate programs. This found that low-income requirements successfully target those with the most need for financial assistance. However, these requirements do not help meet other equity metrics (a just age, gender, racial, and ethnic distribution) by association.

Mobility services, such as ridesourcing and e-scooters, are changing the transportation landscape. These services are often unregulated and their impact on traffic or alternative modes is not well understood. In this dissertation, we study two features of mobility services: (1) we analyze the management of ridesourcing platforms through policies aimed at minimizing their congestion externality; in particular, we investigate policies that reduce idling drivers and improve operational efficiency, (2) we explore the interaction between mobility services and alternative modes; in this case, we study the relationship between e-scooter ridership and transit. While the first part of the dissertation develops mathematical models to understand time-dependent policies for ridesourcing management, the second component focuses on statistical and equity analysis of observed e-scooters data in Austin, TX.

Urban transport projects redistribute accessibility and environmental quality across the city, potentially creating disadvantages for some social groups. This thesis investigates whether these effects are cumulative or compensatory in the case of the Lisbon Metropolitan Area, analysing inequalities in the light of competing principles of social justice. The novelty of this research lies in the interpretation of local environmental effects as factors restraining the mobility of pedestrians. We propose a series of GIS-based indicators, including community severance and noise exposure of pedestrians on the way to work and walking around their neighbourhoods. We found that projects giving priority to private transport have a disproportionate effect on the pedestrian environment of the elderly and low-qualified populations. The analysis addresses two of the most pressing issues in transport equity analysis. The first is the spatial heterogeneity in patterns of inequality. We estimate relationships between socio-economic variables and indicators of the local effects of transport using alternative comparison areas, defined in terms of centrality and commuting destinations. We found that the social distribution of those effects is sensitive to location and spatial scale. The second issue is the nature of the processes leading to inequalities. We show that accessibility and pedestrian mobility have an influence on neighbourhood socio-economic recomposition and on patterns of settlement in newly developed areas. We also analyse the implications of integrating distributive concerns in transport planning. In the design of the optimal route alignment for a new road, these concerns may increase aggregate community severance costs. In the application of traffic restriction policies, there are trade-offs between the welfare of different groups of concern in terms of time to work and pedestrian exposure to noise. In both cases, the achievement of equity may not be compatible with the party-political interests of the policy-maker.

Exercise Referral Schemes (ERSs) are a widespread multi-agency intervention in which patients are referred to a programme of supervised sessions of subsidised exercise at a local leisure centre. National guidance states that schemes should employ strategies to engage people from disadvantaged groups. While people from such groups are known to attend primary care more frequently than those from more advantaged socioeconomic groups, research suggests that they are less likely to use preventive and specialist health services. This thesis aims to evaluate whether access to and use of ERSs is equitable through an examination of socioeconomic differences in referral, uptake and completion of the service. Firstly, the thesis presents a case study of key research, policy and practice events concerning the development of ERSs. Secondly, the thesis details findings of a scoping review undertaken across all ERSs in Greater London to identify schemes with suitable routine data collection to participate in the equity analysis. Thirdly, the thesis presents a cross-sectional analysis of 7985 patients referred by general practices to ERSs operating in six PCTs between April 2004 - March 2006. The main outcome measures were i) risk ratios for referral by general practice deprivation quintile ii) odds ratios for uptake of ERSs and iii) odds ratios for completion of ERSs by patient deprivation quintile. Fourthly, an exploration of the added value of using a geodemographic segmentation tool to enhance understanding of socioeconomic inequalities in service utilisation at small-area level is described. This research found that general practices within deprived areas were more likely to refer patients to ERSs than their counterparts in more advantaged areas. There was no evidence of an association between socioeconomic circumstance and likelihood of either taking up or completing the scheme. The implications of this research for policy, practice and future research are discussed.

This dissertation seeks to better understand how an urban policy, creative placemaking, is adopted within a localized context. Creative placemaking is an ascendant urban policy borne out of a global pressure to adapt to industry changes within the 21st Century knowledge-based economy (Florida, 2002; Howkins, 2001; Jackson, 2012; Nicodemus, 2013). However, there is a need to better understand how a globalized policy response trend is adapted within a localized context, especially with respect to creative placemaking, which is based within a localized identity formation. Additionally, there is a need to understand how areas already dedicated to the knowledge-based economy are adopting creative placemaking for the 21st Century. With this need in mind, higher education institutions and their urban environs are a useful case study for investigating how creative placemaking policy is adopted within an area that has a knowledge-based economy focus.This dissertation research was a qualitative, within case study approach to answer the following sub-questions: (Q1) a policy content question of how does Ohio State University’s 15th+High Art District, and its predecessor plans, score in terms of meeting the criteria for being creative placemaking plans?, (Q2) a policy context question of how are current university campus creative placemaking practices perceived by stakeholders?, and (Q3) a policy process question of how equitable is the manifested 15th+High Arts District design? A multiple methods qualitative, within case study research approach was undertaken to answer these sub-research questions through: (M1) a content evaluation of Ohio State University’s planning documents pertaining to creative, and proto-creative,iiiplacemaking policy adoption using a modified American Planning Association’s Comprehensive Plan Standards for Sustaining Places evaluation instrument, (M2) a content analysis of stakeholder perspectives on the 15th+High Arts District providing local context of this policy adoption, and (M3) an equity analysis of 15th+High Arts District manifestation of the district’s design to consider the implementation processes of creative placemaking.

Air pollution emitted by transportation sources such as cars, trucks, buses, trains, and ships negatively impacts human health, and the exposure burden is not shared evenly across populations. Therefore, working towards environmental justice, or the equitable distribution of environmental risks and benefits, requires accurate modeling of air pollution at the community scale. High-resolution air pollution models can help identify local pollution hotspots within cities, assess environmental health risks of residential developments near polluting transportation corridors, and predict or evaluate the impact of regulatory interventions or other pollution mitigation measures. The studies presented in this dissertation broadly relate to how air pollution emissions from transportation sources can contribute to local-scale heterogeneities in pollutant concentrations and resulting inequities in environmental health impacts, with a focus on how improved community-scale modeling can help assess these inequities. Chapter 1 describes how near-source air pollution impacts have historically been approximated by the distance from the source and argues that this approach should be replaced with a more nuanced modeling approach to better capture local-scale concentration gradients, particularly when evaluating the impact of transportation emissions on new residential developments. Some of the challenges and gaps in current modeling approaches are elaborated, including the need for accurate and spatiotemporally specific emission factors. To mitigate inequities in transportation planning and funding allocations, some metropolitan planning organizations (MPOs) have adopted equity analysis frameworks for their regional transportation plans (RTPs). Air pollution exposure related to transportation can exacerbate other transportation-related inequities, but in most equity analyses it is only addressed superficially, if at all. Chapter 2 assesses how three Californian MPOs consider traffic-related air pollution (TRAP) burden in equity analyses of RTPs. Patterns of exurbanization and gentrification have, in some regions, displaced communities outside of urban core public transit networks, and therefore excluded them from the benefits of reduced local vehicle miles traveled (VMT). In these exurban communities, investments tend to be skewed towards freeway expansion and improvement rather than public transportation. An analysis of the RTPs of three Californian MPOs shows that current equity metrics do not adequately capture the historical and current burdens on people in outer suburban and exurban communities. One notable way that MPOs miss the inequities experienced by communities outside the urban core is by assessing compliance with air pollution standards and VMT reduction targets solely at the regional level, despite significant local variation in dimensions of transportation equity such as TRAP exposure and long car commutes. The MPOs that did consider more local variations approximated TRAP exposure solely by distance from a roadway. However, it is argued in this chapter that this is not sufficient for comparing urban core communities living near transit hubs to exurban communities living near commuter freeways. This study suggests that equity analyses of transportation projects would benefit from improved methods for modeling TRAP exposure at the community scale. Ships contribute substantially to air pollution in port communities, but most modeling studies in these areas use emission inventories developed from decades-old measurements of ship engines. Chapter 3 updates brake-specific particulate matter (PM) mass emission factor (EFPM) estimates from ships via a meta-analysis of 55 lab and field studies. This meta-analysis incorporates newer studies that capture changing fuel standards and incorporates more measurements of operating modes and ship types associated with near-shore emissions that contribute to urban air pollution in port communities. The primary influence examined is the dependence of EFPM on fuel sulfur content (FSC), which has been targeted by the International Maritime Organization as well as local jurisdictions in efforts to reduce PM emissions from shipping. The results support the assumption that limiting FSC has the potential to significantly reduce EFPM and demonstrate a statistically significant linear dependence of EFPM on FSC, although the incorporation of new studies weakens the relationship relative to an earlier meta-analysis. Across the included studies, switching from a low-grade, high sulfur fuel to a high-grade, low sulfur fuel reduces EFPM by 50-80%. The updated emission factors for high sulfur fuels and low sulfur fuels are not significantly different than those derived from earlier studies and corroborate the literature. Some studies of EFPM and engine load show increased EFPM at very low engine loads relative to medium engine loads, likely due to reduced combustion efficiency. The greater heterogeneity of EFPM as compared to emission factors for gaseous pollutants suggests the need for additional studies to investigate the influence of individual factors. Additionally, more studies of ship types (such as tugs and pilots) and operating conditions (such as low speeds and idling) typical of near-shore ship operation would better support high-resolution modeling of ship contributions to PM pollution in port communities. The results of this chapter provide evidence to validate policy interventions that lower the maximum allowable FSC to mitigate PM emissions from shipping, corroborate EFPM estimates used in studies based on previous measurements, and identify areas where further measurements are warranted.

This dissertation seeks to better understand how an urban policy, creative placemaking, is adopted within a localized context. Creative placemaking is an ascendant urban policy borne out of a global pressure to adapt to industry changes within the 21st Century knowledge-based economy (Florida, 2002; Howkins, 2001; Jackson, 2012; Nicodemus, 2013). However, there is a need to better understand how a globalized policy response trend is adapted within a localized context, especially with respect to creative placemaking, which is based within a localized identity formation. Additionally, there is a need to understand how areas already dedicated to the knowledge-based economy are adopting creative placemaking for the 21st Century. With this need in mind, higher education institutions and their urban environs are a useful case study for investigating how creative placemaking policy is adopted within an area that has a knowledge-based economy focus. This dissertation research was a qualitative, within case study approach to answer the following sub-questions: (Q1) a policy content question of how does Ohio State University's 15th+High Art District, and its predecessor plans, score in terms of meeting the criteria for being creative placemaking plans?, (Q2) a policy context question of how are current university campus creative placemaking practices perceived by stakeholders?, and (Q3) a policy process question of how equitable is the manifested 15th+High Arts District design? A multiple methods qualitative, within case study research approach was undertaken to answer these sub-research questions through: (M1) a content evaluation of Ohio State University's planning documents pertaining to creative, and proto-creative, placemaking policy adoption using a modified American Planning Association's Comprehensive Plan Standards for Sustaining Places evaluation instrument, (M2) a content analysis of stakeholder perspectives on the 15th+High Arts District providing local context of this policy adoption, and (M3) an equity analysis of 15th+High Arts District manifestation of the district's design to consider the implementation processes of creative placemaking. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

African American, Latinx, and economically disadvantaged students are underrepresented and under-identified for gifted programs. The problem addressed in this study was the identification of gifted students of color and economically disadvantaged students were not proportionate to their representation in the student population in a Title I school in Louisiana. The purpose of this qualitative case study was to investigate the role teacher perceptions played in the under-representation of students of color and economically disadvantaged students in gifted identification in a Title I school in Louisiana. The primary goal was to explore how teacher perceptions and systemic practices impacted the teacher nomination process of African American, Latinx, and economically disadvantaged students in gifted identification. The guiding framework for this study was a merging of critical theory and transformational leadership theory. The single case study participants were 23 general, special, and gifted education teachers in one public school in Louisiana. Data collected included an online survey, in-depth questionnaire, and archival data. Data analysis involved descriptive, interpretive analysis using open coding and NVivo software relating the triangulated data to the research questions and corresponding categories. Six themes emerged: identification of high-performing observable student characteristics, identification affected by motivational factors, perceptions of inequitable recruitment practices, perceptions of stereotypes and bias, perceptions of inequitable identification practices, and the need for professional development. The implications included perceptions of African American students as not pursued and valued for gifted identification, identification recruitment and process perceived as inequitable and not reflective of the ethnic population and lack of professional development for teachers and parents. Four recommendations for practice were to conduct an equity analysis, screen the highest 50% of students in primary grades, incorporate professional development and consistent communication and create accelerated opportunities for high potential, high achieving students. Recommendations for future research were to conduct a qualitative study on teacher referrals after receiving professional development, investigate district protocols in addressing inequity in gifted identification practices, and conduct a study to explore parent perceptions of gifted identification. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Accessibility, the ease to reach destinations, is a fundamental concept in transportation science, planning, and policy. Accessibility provides the ability for individuals to participate in essential and leisure activities, acquire valuable resources, and interact with other people. Therefore, the lack of effective accessibility can be a key contributor to socio-economic and health problems within urban areas. Also, the inequitable distribution of accessibility can exacerbate disadvantages from poor social and health outcomes and exclude the vulnerable populations within a community. Unreliable accessibility due to factors such as lack of a well-functioning automobile, traffic congestion combined with long commutes, public transit delays can create serious impacts on individuals, such as losing jobs and missing necessary health appointments. To improve accessibility, and its equity and reliability, many cities are developing transportation innovations such as new public transportation services (e.g., bus rapid transit, light rail), revamping existing transit networks, and experimenting with microtransit. There are needs for careful measurement and analysis of the impacts of these new services on accessibility to ensure they enhance neighborhood accessibility, equity and reliability. Similar to many policy interventions in cities, new transport services and technologies may not yield the intended outcomes. Robust and sensitive accessibility measures can help guide the planning, design, and implementation of these projects. This dissertation develops new concepts and methods that expand a core time geographic accessibility measure, the space-time prism (STP), to support the scientific evaluation of the new mobility service impacts on neighborhood accessibility and its equity and reliability. The STP is a sensitive, individual-level representation of potential mobility in geographic space with respect to time with a well-developed theoretical foundation. The first study develops the concept of a multimodal network-time prism (MNTP) to measure individuals’ accessibility to jobs and healthcare services within a transport network comprising a transit system and a first/last-mile network (such as a sidewalk network for walking). The second study develops analytical methods for computing an average space-time prism (ASTP) as a summary, representative STP consistent with a group of individual STPs; this is a useful indicator for space-time accessibility equity analysis. The third study develops a general analytical framework for measuring accessibility considering travelers’ different risk-taking attitudes (e.g., risk-seeking, risk-averse) as well as risk-managing strategies (e.g., safety margin plan, routing strategy) for dealing with travel time uncertainty. This dissertation demonstrates the applicability and usefulness of these new accessibility methods developed in the three studies above using an empirical example: a new Bus Rapid Transit (BRT) system specifically intended to improve accessibility in a deprived neighborhood of Columbus, Ohio, USA. The new accessibility measures indicate accessibility improvements. A fourth study follows from the three accessibility analyses to ask the question – did the new mobility service increase ridership in the corridor? This study compares simple pre/post ridership measurements with a quasi-experimental design based on difference-in-differences and propensity matching. We find that quasi-experimental controls on other factors in the public transit system and community results in a more modest estimate of ridership impacts than pre/post analysis. The comprehensive conceptual and analytical package developed in this dissertation will enable a more realistic, robust, and scientific evaluation regarding the impacts of new mobility services on people’s physical accessibility as well as possible associated behavioral changes (e.g., ridership increase).

The rapid emergence and growth of transportation network companies (TNCs) such as Uber, Lyft, and Didi Chuxing, operating app-based on-demand ride-sourcing services, has led to a debate over the role of TNCs in the urban transport system. The growth of the ride-sourcing business has brought significant challenges for planners, engineers, and policy makers, due to the magnitude and uncertainty of its impacts. This dissertation focuses on several aspects of on-demand mobility, mostly related to equity and behavior, and answers some of the most debated questions about ride-sourcing to provide important evidence for engineers, planners and policy makers on future ride-sourcing related policy decisions.The equity analysis investigates the effects of ride-sourcing fare changes to passengers with different socio-economic backgrounds. Using a large GPS-based travel dataset from 2015 in Shanghai, I conducted panel analysis of how ride-sourcing demand was related to average property value, as a proxy for socioeconomic status, measured at small spatial scale at trip origins and destinations. I modeled the ride-sourcing demand (for pick-ups and drop-offs separately) as a product of several spatial and temporal characteristics, using a negative binomial regression with fixed effects whose functional form is appropriate for dispersed count data. The results imply that a decrease in ride-sourcing fares would likely benefit middle to high income travelers more than low-income travelers, by making ride-sourcing an economically competitive mode for those groups. Usage is much higher in neighborhoods with higher property values when fares are lower. At the same time, however, there is still significant though lower use of ride-sourcing in lower-income neighborhoods, and usage in those locations is less responsive to the fare. I conclude that ride-sourcing policy which results in fare increases would likely to pose a substantial burden for lower-income travelers, although the number of such lower income travelers may be small compared to the number of middle-to-high income travelers.The behavioral analysis focuses on three questions: (1) Is parking supply associated with lower ride-sourcing demand? (2) Does better transit access reduce or increase the use of ride-hailing? and (3) Does higher congestion affect ride-sourcing demand? I modeled the ride-sourcing demand (for pick-ups and drop-offs separately) using a generalized additive mixed model (GAMM). The results suggest that first, parking would not necessarily reduce the demand for ride-sourcing unless the parking supply is large enough. Second, whether ride-sourcing compete or complement bus transit depends on the coverage of bus services: they tended to compete when the density of bus stops is high, and complement each other when there are fewer bus stops. Third, ride-sourcing demand was positively correlated with congestion, except that when congestion is severe, there were fewer pick-ups. In addition to the panel study, I used Google Map API to figure out if each actual ride-sourcing trip has transit alternative. I found that over 90% of the actual ride-sourcing trips have transit alternatives, but transit compete poorly with ride-sourcing because of much longer travel time, need multiple transfer and longer walking. Finally, I discussed policy implication based on these empirical findings.

“Vision Zero,” first adopted by Sweden in 1997, is a road safety policy that aims to achieve a transportation system having zero fatalities or serious injuries for all modes of transportation. It takes a proactive approach to road safety system by identifying risk and taking steps to prevent injuries. Historically, traffic related crashes have disproportionately impacted vulnerable communities and system users including people of color, low income individuals, seniors, children, and pedestrians, bicyclists, and transit users (who typically walk to and from public transport). These inequities are addressed in the Vision Zero framework by prioritizing interventions in areas that need safety improvements the most. In 2016, the Alexandria City Council voted unanimously to develop a “Vision Zero” policy and program as a part of its updated transportation master plan. It required an initial equity analysis to assess the impact of traffic crashes on the traditionally underserved communities / groups (groups from at least one of these categories: low-income; minority; elderly; children; limited English proficiency; persons with disabilities; and/or pedestrians/ bicyclists/ transit users). This study combines three different methods to investigate the equity issues regarding traffic safety: 1) descriptive analysis of the spatial pattern of crashes and their relationship with the demographic profiles of neighborhoods at census block group level (for 2010-2014 period); 2) descriptive analysis of the crash trends in Alexandria; and 3) exploratory regression analyses for two different units of analysis (an aggregate regression analysis of crashes at census block group, and a disaggregate regression analysis of the individual level crash reports of traffic crashes). The analysis found that the elderly, school aged children, rail/subway users, and pedestrians had a higher risk of fatalities and severe injuries in traffic crashes. Higher job densities, alcohol impairment, and speeding were significantly related to higher KSI, whereas, smaller block sizes (higher number of street segments per sq. mile area of census block group), higher housing density, and use of safety equipment were related to lower KSI.