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FINITE ELEMENT MODELING OF TIDES AND CURRENTS OF THE PASCAGOULA RIVER
Title
FINITE ELEMENT MODELING OF TIDES AND CURRENTS OF THE PASCAGOULA RIVER.
Creator
Wang, Qing, Hagen, Scott, University of Central Florida
Abstract / Description
This thesis focuses on the simulation of astronomic tides of the Pascagoula River. The work is comprised of five steps: 1) Production of a digital elevation model describing the entire Pascagoula River system; 2) Development of an inlet-based, unstructured mesh for inbank flow to better understand the basis of the hydrodynamics within the Pascagoula riverine system. In order to assist in the mesh development, a toolbox was constructed to implement one-dimensional river cross sections into the...
Show moreThis thesis focuses on the simulation of astronomic tides of the Pascagoula River. The work is comprised of five steps: 1) Production of a digital elevation model describing the entire Pascagoula River system; 2) Development of an inlet-based, unstructured mesh for inbank flow to better understand the basis of the hydrodynamics within the Pascagoula riverine system. In order to assist in the mesh development, a toolbox was constructed to implement one-dimensional river cross sections into the two-dimensional model; 3) Implementation of a sensitivity analysis of the Pascagoula River two inlet system to examine the inlet effects on tidal propagation; 4) Improvement of the inlet-based model by performing a preliminary assessment of a spatially varied bottom friction; 5) Implementation of an advection analysis to reveal its influence on the flow velocity and water elevation within the domain. The hydrodynamic model employed for calculating tides is ADCIRC-2DDI (ADvanced CIRCulation Model for Shelves, Coasts and Estuaries, Two-Dimensional Depth Integrated). This finite element based model solves the shallow water equations in their full nonlinear form. Boundary conditions including water surface elevation at the off-shore boundary and tidal potential terms allow the full simulation of astronomic tides. The improved astronomic tide model showed strong agreement with the historical data at seven water level monitoring gauge stations. The main conclusions of this research are: 1) The western inlet of the Pascagoula River is more dominant than the eastern inlet; however, it is necessary to include both inlets in the model. 2) Although advection plays a significant role in velocity simulation, water elevations are insensitive to advection. 3) The astronomic model is sensitive to bottom friction (both global and spatial variations); therefore, a spatially varied bottom friction coefficient is suggested. As a result of this successful effort to produce an astronomic tide model of the Pascagoula River, a comprehensive storm surge model can be developed. With the addition of inundation areas the surge model can be expected to accurately predict storm tides generated by hurricanes along the Gulf Coast.
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Date Issued
2008
Identifier
CFE0002291, ucf:47840
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002291
Arterial-level real-time safety evaluation in the context of proactive traffic management
Title
Arterial-level real-time safety evaluation in the context of proactive traffic management.
Creator
Yuan, Jinghui, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Cai, Qing, Wang, Liqiang, University of Central Florida
Abstract / Description
In the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and...
Show moreIn the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and freeways in terms of traffic flow characteristics, data availability, and even crash mechanism. Therefore, this study aims to deeply evaluate the real-time crash risk on arterials from multiple aspects by integrating all kinds of available data sources. First, Bayesian conditional logistic models (BCL) were developed to examine the relationship between crash occurrence on arterial segments and real-time traffic and signal timing characteristics by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted from four urban arterials in Central Florida. Second, real-time intersection-approach-level crash risk was investigated by considering the effects of real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Orange County. Third, a deep learning algorithm for real-time crash risk prediction at signalized intersections was proposed based on Long Short-Term Memory (LSTM) and Synthetic Minority Over-Sampling Technique (SMOTE). Moreover, in-depth cycle-level real-time crash risk at signalized intersections was explored based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). All the possible real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather conditions. Above all, comprehensive real-time safety evaluation algorithms were developed for arterials, which would be key components for future real-time safety applications (e.g., real-time crash risk prediction and visualization system) in the context of pro-active traffic management.
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Date Issued
2019
Identifier
CFE0007743, ucf:52398
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007743