Evident similarities and links between the outcomes of traffic crashes and stranded (or constrained) mobility have been identified and are reported in this research. Generally, a high level of travel activities is an indicator of high crash exposure. However, studies have shown that the highest rates of traffic fatalities occur in low- and middle-income regions, where many citizens experience relatively low levels of motorized travel. This ironic observation reveals serious challenges facing transport mobility systems in the less privileged regions of the world. Studies on traffic crashes and mobility constraints also reveal that they both have individual and regional variations in their occurrence, effects, and severities. Consequently, the outcomes of traffic crashes and constrained mobility are serious public health concerns worldwide.
As public health problems, their study is analogous to the study of diseases and other injuries and thus, suitable for the application of epidemiological techniques. This dissertation therefore explores the use of epidemiological techniques to analyze traffic crashes and mobility/accessibility constraints from a human-centered perspective. The dissertation therefore consists of two major focus areas. The first part of the study applies widely used epidemiology/public health – based statistical tools to analyze traffic crashes with the aim of gaining better understanding of the human-centered causes and factors that influence these causes, and how these ultimately affect the severity of crashes. This part is further divided into two sub-sections. The first sub-section used latent class analysis to identify homogeneous clusters of human-centered crash causal factors and then applied latent class logit and random parameters logit modeling techniques to investigate the effects of these factors on crash outcomes. The second sub-section of the first part of the dissertation applies multilevel regression analysis to understand the effects of driver residential factors on driver behaviors in an attempt to explain the area-based differences in the severity of road crashes across sub-regions. Both studies are necessary to develop potential human-centered mitigations and interventions and for the effective and targeted implementation of those countermeasures. The second part of the study provides an epidemiological framework for addressing mobility/accessibility constraints with a view to diagnosing symptoms, recommending treatment, and even discussing the idea of transmission of constrained mobility among city dwellers. The medical condition, hypomobility, has been used to connote constrained mobility and accessibility for people in urban areas. In transportation and urban studies, hypomobility can result in a diminished ability to engage in economic opportunities and social activities, hence deepening poverty and social exclusion and increasing transport costs, among other negative outcomes. The condition is especially pronounced in poor urban areas in developing countries. The framework proposed in this study is expected to help identify and address barriers to mobility and accessibility in the rapidly growing cities throughout the developing world, with particular applicability to the rapidly developing cities in Sub-Saharan Africa.
Ultimately, this dissertation explores the application of epidemiological techniques to two major transportation problems: traffic safety and constrained mobility. The techniques presented in this dissertation provide policy makers, agencies, and transport professionals with tools for evidence-based policies and effective implementation of appropriate countermeasures.
|Commitee:||Appiah-Opoku, Seth, Hainen, Alexander, Lindly, Jay, Walsh, Joseph|
|School:||The University of Alabama|
|Department:||Civil, Construction and Environmental Engineering|
|School Location:||United States -- Alabama|
|Source:||DAI-B 79/02(E), Dissertation Abstracts International|
|Subjects:||Civil engineering, Transportation, Epidemiology|
|Keywords:||Constrained mobility, Crash severity, Human factors, Latent class analysis, Multilevel analysis|
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