Graduate Thesis Or Dissertation


Incorporating Uncertainty and Social Influences Into Transportation System Decision Making Public Deposited
  • The initial motivation for this research was the incorporation of social influences into evacuations. Research began with a literature review of evacuation research. Results revealed the need to include more human-centric indicators into evacuation modeling. This motivated a study of the ability to analyze social influences in evacuation data. This analysis used mobile location data and observed traffic counts to analyze evacuation behavior during Hurricane Michael in 2018.Results demonstrate that social influences in evacuation can be observed using mobile location data. The results show that at certain traffic sites, certain social groups of different income and race exhibit different travel behavior during evacuation as compared to normal conditions. The variability of results across the traffic sites analyzed also highlights the uncertainty associated with determining social influences. The need to understand the uncertainty of social influences and combine diverse data sets, such as traffic counts and evacuee behavioral surveys, motivated research into alternative methods of uncertainty analysis. Probability Theory-based methods may not be well suited to address the uncertainty present in social data. One method well suited for the combination of uncertain data is Evidence Theory. A review of Evidence Theory computation and combination methods led to the development of a protocol for Evidence Theory applications. The protocol addresses assignment of belief mass, computational implication of combination methods, and the commonalities of different methods. The protocol facilitates sensitivity analysis of Evidence Theory output. The protocol, therefore, enables a secondary analysis of the results of Evidence Theory applications, highlighting uncertainty among possible outcomes. The protocol was then applied to predict pavement condition, demonstrating the concepts addressed by the protocol. The demonstration application also allowed a comparison of Evidence Theory to Probability Theory-based methods, such as Markov Decision Process (MDP). The comparison demonstrates the effectiveness of Evidence Theory and the value of sensitivity analysis. Overall, this dissertation contributes to the field of transportation system decision making by identifying needs and applicability of data in social analyses. This research applies Evidence Theory to combine uncertain data. The awareness of data needs and data combination methods support decision-making and communication with uncertain data.

Date Issued
  • 2022-07-19
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Last Modified
  • 2022-09-14
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