Data-driven methodologies for supporting decision-making in roadway safety and pavement management

dc.contributor.advisorBhasin, Amit
dc.contributor.committeeMemberLi, Jenny
dc.contributor.committeeMemberCaldas, Carlos H
dc.contributor.committeeMemberBoyles, Stephen D
dc.creatorXu, Yang, M.S. in Engineering
dc.date.accessioned2024-04-15T23:26:58Z
dc.date.available2024-04-15T23:26:58Z
dc.date.issued2023-08
dc.date.submittedAugust 2023
dc.date.updated2024-04-15T23:26:58Z
dc.description.abstractThere has been a significant rise in the utilization of data-driven methods within the contemporary realm of transportation engineering. This trend is primarily attributed to the limitations associated with experience-based methods, such as subjectivity and non-reproducibility. In contrast, data-driven methods have proven to offer a more objective and effective approach to problem analysis, thereby providing decision-makers with a reliable basis for informed decision-making. This present research focuses on two types of data-driven methodologies: geostatistical analyses utilizing geographic information systems (GIS) and cutting-edge algorithms associated with artificial intelligence (AI). In numerical analysis, data provides a means to gain valuable insights into a problem of interest. While AI-oriented methods have been shown in many studies to be more effective than traditional approaches, the accuracy of the analysis still heavily depends on the quality of the data. This dissertation endeavors to shed light on the pivotal role that data plays in both roadway safety analysis and pavement management. To accomplish this, four distinct studies are proposed that examine different aspects of data-driven methods. The studies encompass an evaluation of data consistency in motor vehicle crash databases, the identification of crash hot spots within a road network, a synthesis of advancements in the application of AI algorithms to various activities of pavement management, and an exploration of the relationship between pavement conditions and roadway safety using AI-oriented methods. The knowledge acquired from these studies serves as a foundation for future research, advancements, and the adoption of innovative approaches to enhance the efficiency of safety analysis and pavement management. This research ultimately facilitates informed decision-making, effective resource allocation, and the implementation of cost-effective interventions to enhance roadway safety and optimize pavement management practices.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/2152/124825
dc.identifier.urihttps://doi.org/10.26153/tsw/51427
dc.language.isoen
dc.subjectHighway safety
dc.subjectDeep neural networks
dc.subjectHorizontal curves
dc.subjectCrash hot spots
dc.subjectPavement management
dc.subjectData analysis
dc.subjectArtificial intelligence (AI)
dc.subjectData-driven methods
dc.titleData-driven methodologies for supporting decision-making in roadway safety and pavement management
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.nameDoctor of Philosophy
thesis.degree.programTransportation Engineering

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