ORCID Identifier(s)

0000-0002-4517-2725

Graduation Semester and Year

Fall 2024

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Urban Planning and Public Policy

Department

Urban and Public Affairs

First Advisor

Qisheng Pan

Second Advisor

Jianling Li

Third Advisor

Julene Paul

Abstract

In North American cities, the land use and transportation systems are associated with low-density, suburban-type urban development, where the built environment contributes to a mobility pattern that is extremely reliant on automobiles. Several studies in the past decades have documented noticeable and concerning patterns of unjust and unsustainable transportation in this context. As a result, for those population groups with limited access to private transportation, this form of urban structure is equivalent to limited access to opportunities, leading to various environmental and social problems. These problems exacerbate inequities in access to transportation, disproportionately affecting marginalized communities. From a sustainable development perspective, this pattern contributes to ever-increasing energy consumption, air pollution, and traffic congestion.

Given the fast pace of evolution in urban transportation systems, with the emergence of newer technologies and mobility options, it is highly timely and relevant to develop state-of-the-art methods. These methods should address existing transportation equity disparities and the underlying causes. In this regard, this dissertation explores three sets of interrelated questions concerning existing multimodal transportation equity issues and their association with transportation quality factors, the built environment, and socio-demographics in Texas metropolitan areas, specifically Dallas and Houston.

The first chapter introduces the broader transportation equity research background, introduces the related topics being investigated, and provides the outline for the dissertation.

The second chapter attempts to propose a three-step transportation equity analysis framework in which a new method of measuring transit and car accessibility measure is introduced. This framework incorporates the concept of job matching and competition into the measure. In the next step, underserved populations groups are identified using environmental justice regional thresholds. Finally, common inequality measures-Gini coefficient and Atkinson measures- are utilized to perform a horizontal and vertical distributional impact analysis with respect to race and income in the Dallas-Fort Worth (DFW) area. The results of this study demonstrate the limited accessibility of jobs by transit compared to car and higher inequalities of transit access. Furthermore, the study reveals that minorities, particularly African Americans and Hispanics, as well as low-income groups, experience higher levels of inequality in job access via public transit. The results also show that inequity with respect to race is more pronounced than income. The proposed equity analysis framework can help planners to gain a profound understanding of existing as well as future inequalities under different scenarios and transportation improvement plans.

The third chapter investigates the association between the distribution of employment density and transit access, separated by service type (local bus and light rail), job type (blue- and white-collar), and access mode (walking and driving). This detailed analysis is designed to unravel more details about existing equity issues and provide fresh insights into which type of transit infrastructure can better alleviate transportation equity problems. Using advanced spatial statistical models, this study shows that bus services have a significant relationship with job density across all industrial sectors, while light rail affects job distribution in only a few industries. Furthermore, the results show that access to transit, especially for light rail service, plays an important role in influencing job distribution. The results also clearly show that the distribution of jobs is more affected by proximity to highway and transit infrastructure, while the impact of transit accessibility is minimal. These findings together offer interesting insights about the relationship between transportation amenities and distribution of opportunities and suggest several policy implications for realizing a more equitable transportation and land use setting.

The fourth chapter performs a longitudinal analysis on the causal relationships between transit quality factors, built environment, and sociodemographic characteristics and modal split of low-income versus general population at the zonal level. Documented by several studies in the past, the relationship between transit quality and built environment and commuting mode choice is complex and non-linear. As a result, this study develops and compares various modeling techniques, such as pooled, panel, spatial panel, and machine learning models, to identify the most robust methods for predicting zonal modal split. The study highlights the significantly improved accuracy of machine learning models compared to traditional statistical models. Regarding transit quality factors, the results show that the general population has a stronger aversion (compared to low-income riders) to transit disutility factors and that access to transit plays the most important role in affecting both groups’ modal split. Additionally, the results demonstrate that the general populations prefer the service when it is more efficient, single modal and with fewer transfer and wait times. On the other hand, low-income populations prefer the service when it offers better coverage and mobility even with higher transit fares. Reliability seems to be a more influential factor for low-income modal split while built environment factors are more influential for general groups. The in-depth and longitudinal analysis of this study guides transit agencies and planners on which policy measures can effectively encourage a shift from private cars to public transit for each group. The final chapter summarizes the policy implications derived from this research and provides directions for future research on transportation equity.

Keywords

Job accessibility, Transportation equity, Spatial statistical models, Transit level-of-service, First/Last mile, Machine learning

Disciplines

Environmental Studies | Geographic Information Sciences | Human Geography | Social Justice | Social Statistics | Spatial Science | Sports Studies | Transportation Engineering | Urban, Community and Regional Planning | Urban Studies and Planning

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