Master of Science, The Ohio State University, 2021, Civil Engineering

To expand spatial-temporal coverage of data collection for estimating traffic volumes, it has been proposed to use video data from transit buses. Transit buses are already covering the major roadways in an urban network. Many buses already have cameras deployed for other purposes, but which observe vehicles on a roadway segment that can conceivably be used to estimate traffic volumes. The volumes estimated in this way are of short duration, however, and therefore are likely to give an unrepresentative estimate of traffic volumes over time periods of interest. To get a good estimate of traffic volume for an estimation period, the multiple volume estimates obtained from the individual passes within that period can be aggregated. This thesis proposes and evaluates different methods to aggregate the volumes derived from individual bus passes. Different aggregation approaches, which are categorized into “discrete time interval” and “continuous time'' approaches, are presented. The first category, representing the first general approach, aggregates individual bus pass volumes by taking their arithmetic average in discrete time intervals. The second category considers bus passes as flow rates that can be integrated over continuous time to determine a volume estimate in a specified time interval. In one implementation of this continuous time category, considered the second general approach, a benchmark interpolation of the individual bus pass flow rates is used to determine the flow rate function. A different implementation of continuous time aggregation, which is considered the third general approach, attempts to smooth the flow rate function by using a “moving median time window” technique before integrating the rates over time. Evaluation of the approaches is conducted using empirical video data collected from Campus Area Bus Service buses serving The Ohio State University and concurrently collected road tube counts provided by the Mid-Ohio Regional Planning Commissio (open full item for complete abstract)

Committee: Mark McCord (Advisor); Andre Carrel (Committee Member); Rabi Mishalani (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, The Ohio State University, 2006, Computer and Information Science

Although many advances have been achieved within the visualization community in the last decade, many challenging problems are still open in volume rendering. In this dissertation, we mainly study three challenging topics in advanced volume rendering on shadows, flows, and high-dimensional rendering. Shadows are essential to realistic and informative scenes. In volume rendering, the shadow calculation is difficult because the light intensity is attenuated as the light traverses the volume. We investigate a new shadow algorithm that properly determines the light attenuation and generates more accurate volumetric shadows with low storage requirements by using 2D shadow buffers. We have extended our shadow algorithm to deal with extended light sources and generate volumetric soft shadows with an analytic method and using a convolution technique. This shadow and soft shadow algorithm also has been applied to mixed scenes of volumetric and polygonal objects. Multiple light scattering is also modeled in our volumetric lighting model. Interval volume algorithm is a region-of-interest extraction algorithm for steady and time-varying three-dimensional structured and unstructured grids. We present several new rendering operations to provide effective visualizations of the 3D scalar field. This technique has been extended to four dimensions to extract time-varying interval volumes. The time-varying interval volumes are rendered directly, from 4-simplices to image space. We propose a high-dimensional rendering algorithm and solve this technical challenge. In this way, we can visualize the integrated interval volumes across time steps and see how interval volumes change over time in a single view. Three-dimensional flow visualization is a challenging topic. We propose an implicit flow field method to visualize 3D flow fields. An implicit flow field is first extracted using an advection operator on the flow, with a set of flow-related attributes stored. Two techniques are then em (open full item for complete abstract)

Committee: Roger Crawfis (Advisor) Subjects: Computer Science

Master of Science (MS), Ohio University, 2022, Civil Engineering (Engineering and Technology)

Roadway facilities have become an integral part of the growth of cities, countries, and nations. Transportation systems keep society operational and improve the quality of life by allowing businesses to grow and service more people, providing access to the delivery of items from far and near locations, and the freedom to explore new horizons. But unforeseen events such as the COVID-19 pandemic disrupted such operations by changing the utilization of these roadway networks. This research studied the effects of COVID-19 mandates on roadway travel in all 50 states, and the impact of volume changes by vehicle classification in the State of Ohio. In addition, this research generated a regression model to predict traffic volumes for each vehicle class group utilizing recent societal factors impacting the roadway network. Traffic volume data for all 50 states were collected from the Federal Highway Administration's traffic volume database, and Ohio's volume vehicle classification data was collected from the Ohio Department of Transportation's Traffic Monitoring Management System. Results showed that in 40 of the 50 states, significant changes in traffic volumes were observed. In Ohio, Class 1 to 4 vehicles were negatively affected, as a significant decline in traffic volumes (-17.35 percent) was observed from 2018 to 2020. The most increase recorded was among Class 8 to 13 vehicles from 2018 to 2021 (28.21 percent). Significant changes to traffic volumes and traffic composition have an impact on pavement design models which heavily rely upon accurate traffic forecasts. Future research efforts should continue to monitor if the traffic volume and composition changes are long-term impacts or simply associated with the pandemic.

Committee: Deborah S. McAvoy (Advisor) Subjects: Civil Engineering

MS, University of Cincinnati, 2005, Engineering : Industrial Engineering

Disassembly has achieved significant attention in the past few years for its pivotal role in waste recycling, remanufacturing and servicing of components for maintenance. With more emphasis on products to be environmentally safe, today, the need for efficient and cost effective disassembly process has gained momentum. This thesis studies the current approaches in sub-assembly detection, disassembly sequencing and path planning and proposes methodologies that tend to better approach disassembly processes applicable to any manufacturing industry. Literature review indicates less importance shed on sub-assembly detection that could expedite the process of disassembly sequencing several fold. An algorithm for automatic sub-assembly detection and its application in disassembly sequencing has been presented. The disassembly sequencing involves the use of interference matrices and AND/OR logical operations that could be implemented into a computer program. This makes it easier for future integration with CAD systems. Having determined the disassembly sequence, a scheme using alternating sum of volumes with partitioning (ASVP), form feature decomposition (FFD) and Gaussian hemisphere (GHS) has been proposed for determining disassembly directions in three-dimensional space. The algorithms presented would significantly enhance disassembly processes in the industry.

Committee: Dr. Sundararaman Anand (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2005, Civil Engineering

Annual Average Daily Traffic (AADT) is one of the most fundamental traffic statistics used for highway planning, design, and maintenance. State departments of transportation invest heavily in personnel and equipment to collect traffic counts supporting AADT estimation on all highway segments in their systems on a regular basis. Vehicles are detectable in air photos, high-resolution satellite images, and LiDAR data of highway segments, which are regularly collected for various purposes. A Bayesian approach is developed to incorporate the traffic data extracted from these images in the existing practice of AADT estimation. The uncertainty in the AADT on a segment is expressed by a probability distribution. In any year of interest, the approach begins with a prior AADT distribution that is updated to a posterior distribution when a traffic count is available. When incorporating the uncertainty in traffic growth, this approach can be applied year by year. Methods are developed to model the prior distribution of the AADT and the probability distribution of short-term traffic counts conditional on the AADT, which are two important components of this approach. Parameters are estimated to make the approach operational. A numerical study is conducted to simulate AADT estimation during a typical cycle of traffic count collection on the ground. The results show that a small amount of image-based data could be exploited through the Bayesian approach to improve accuracy in AADT estimates while reducing the number of costly and dangerous ground counts. Sensitivity analysis indicates that the Bayesian approach would provide positive benefits for a large range of conditions. Operational issues are discussed for the Bayesian approach, and it appears that the method could be implemented in state DOTs if the institutional means are developed to extract image-based data and place them in a format that could be easily integrated with data presently used to estimate AADT. Additional area (open full item for complete abstract)

Committee: Mark McCord (Advisor) Subjects: Transportation

Master of Science (MS), Ohio University, 2008, Computer Science (Engineering and Technology)

This thesis is intended to explore and investigate the possibility and usefulness of a completely hardware based silhouette edge detection mechanism for use in shadow voluming. Several implementations were developed, and rigorous testing was performed to determine if this goal was attainable. The implementations were integrated into an existing three-dimensional game engine to show that they were applicable in a practical setting. It was determined that GPU driven silhouette edge identification is indeed viable and, in fact, preferable for large scenes. This work culminated with an implementation of silhouette detection using CUDA, a fairly young GPGPU technology, performed entirely on the graphics card.

Committee: David M. Chelberg PhD (Committee Chair); David Juedes PhD (Committee Member); Cynthia Marling PhD (Committee Member); Teresa Franklin PhD (Committee Member) Subjects: Computer Science