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Optimizing Information Values In Smart Mobility, Fatima Afifah
Optimizing Information Values In Smart Mobility, Fatima Afifah
Electronic Theses and Dissertations, 2020-
Smart mobility, enabled by advanced sensing, communication, vehicle, and emerging mobility technologies, has transformed transportation systems. Real-time information shared by public and private entities plays a pivotal role in smart mobility, which facilitates informed decision-making, including effective mode choice, dynamic vehicle control, optimized travel routing, and strategic vehicle relocation. While more information is believed to benefit individual decision makers, it is crucial to acknowledge that the effects of information on transportation network performance are contingent; more information may not always benefit the safety and mobility of the whole system. The goal of this dissertation is to investigate the effects of …
Transportation Electrification In Interdependent Power And Transportation Systems - Analysis, Planning, And Operation, Sina Baghali
Transportation Electrification In Interdependent Power And Transportation Systems - Analysis, Planning, And Operation, Sina Baghali
Electronic Theses and Dissertations, 2020-
Electric vehicles (EVs) are one of the eminent alternatives to decarbonize the transportation sector. However, large-scale EV adoption brings new challenges and opportunities to both transportation and power systems (TPSs). The challenges include the lack of understanding of EV driving behaviors and the associated charging demand (CD) distribution, the complex interaction of the decentralized decision-makers from TPSs, and the insufficient infrastructure from TPSs to accommodate the growing CD of EVs. On the other hand, the opportunities include benefiting the power systems by leveraging vehicle-to-grid (V2G) technologies and improving transportation mobility by incorporating strategic infrastructure planning. The goal of this dissertation …
Understanding Evacuation Traffic Safety Issues During Hurricane Evacuation Using Machine Learning And Connected Vehicle Data, Zaheen E Muktadi Syed
Understanding Evacuation Traffic Safety Issues During Hurricane Evacuation Using Machine Learning And Connected Vehicle Data, Zaheen E Muktadi Syed
Electronic Theses and Dissertations, 2020-
Hurricane evacuation, ordered to save lives of people of coastal regions, generates high traffic demand with increased crash risk. To mitigate such risk, transportation agencies need to anticipate highway locations with high crash risks to deploy appropriate countermeasures. With ubiquitous sensors and communication technologies, it is now possible to retrieve micro-level vehicular data containing individual vehicle trajectory and speed information. Such high-resolution vehicle data, potentially available in real time, can be used to assess prevailing traffic safety conditions. Using vehicle speed and acceleration profiles, potential crash risks can be predicted in real time. Previous studies on real-time crash risk prediction …
Investigation Of Ground Deformations And Vibrations Due To Impact Pile Driving: Measurements And Prediction Model, Berk Turkel
Investigation Of Ground Deformations And Vibrations Due To Impact Pile Driving: Measurements And Prediction Model, Berk Turkel
Electronic Theses and Dissertations, 2020-
Pile driving, a commonly used method for installing deep foundations, has gained prominence as a foundation solution to transfer structural loads to deep competent strata. However, this method of installation can generate noise, ground vibrations, and deformations. These effects pose risks to adjacent structures and buried utilities, jeopardizing the safety and serviceability of urban infrastructure. Researchers and public and private agencies have proposed many vibration limit criteria to avoid damage to infrastructure. However, these criteria for construction vibrations are not linked to the ground densification associated with repetitive and cumulative loadings in sandy soils. This dissertation focuses on developing a …
Developing A Physics-Informed Deep Learning Paradigm For Traffic State Estimation, Jiheng Huang
Developing A Physics-Informed Deep Learning Paradigm For Traffic State Estimation, Jiheng Huang
Electronic Theses and Dissertations, 2020-
The traffic delay due to congestion cost the U.S. economy $ 81 billion in 2022, and on average, each worker lost 97 hours each year during commute due to longer wait time. Traffic management and control strategies that serve as a potent solution to the congestion problem require accurate information on prevailing traffic conditions. However, due to the cost of sensor installation and maintenance, associated sensor noise, and outages, the key traffic metrics are often observed partially, making the task of estimating traffic states (TSE) critical. The challenge of TSE lies in the sparsity of observed traffic data and the …
Modeling Individual Activity And Mobility Behavior And Assessing Ridesharing Impacts Using Emerging Data Sources, Jiechao Zhang
Modeling Individual Activity And Mobility Behavior And Assessing Ridesharing Impacts Using Emerging Data Sources, Jiechao Zhang
Electronic Theses and Dissertations, 2020-
Predicting individual mobility behavior is one of the major steps of transportation planning models. Accurate prediction of individual mobility behavior will be beneficial for transportation planning. Although previous studies have used different data sources to model individual mobility behaviors, they have several limitations such as the lack of complete mobility sequences and travel mode information, limiting our ability to accurately predict individual movements. In recent years, the emergence of GPS-based floating car data (FCD) and on-demand ride-hailing service platforms can provide innovative data sources to understand and model individual mobility behavior. Compared to the previously used data sources such as …
Social Media-Based Crisis Communication: Analysis Of Twitter Data From Local Agencies During Hurricane Irma, Naiyara Noor
Social Media-Based Crisis Communication: Analysis Of Twitter Data From Local Agencies During Hurricane Irma, Naiyara Noor
Electronic Theses and Dissertations, 2020-
As social media platforms have become vital means of communications, it has become imperative for emergency managers and policy makers to understand how people are interacting with different agencies on these platforms for enhancing community response coordination during disasters. Although many public agencies have already adopted social media platforms for crisis communication purposes, empirical evidence on whether and how these agencies are effectively engaged on these platforms is lacking. This research aims to examine crisis communication activities of a variety of agencies on Twitter in response to Hurricane Irma in 2017. In this study, we analyzed 13,353 hurricane-related tweets posted …
A Generalized Accelerated Failure Time Model To Predict Restoration Time From Power Outages, Tasnuba Binte Jamal
A Generalized Accelerated Failure Time Model To Predict Restoration Time From Power Outages, Tasnuba Binte Jamal
Graduate Thesis and Dissertation 2023-2024
Major disasters such as wildfire, tornado, hurricane, tropical storm, flooding cause disruptions in infrastructure systems such as power outage, disruption to water supply system, wastewater management, telecommunication failures, and transportation facilities. Disruptions in electricity infrastructures have negative impacts on sectors throughout a region, including education, medical services, financial, and recreation sectors. In this study, we introduce a novel approach to investigate the factors which can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and utilizing a comprehensive set of county-level data, we have estimated a Generalized Accelerated Failure Time …
Development,Validation, And Integration Of Ai-Driven Computer Vision System And Digital-Twin System For Traffic Safety Dignostics, Ou Zheng
Electronic Theses and Dissertations, 2020-
The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive …
Assessing Public Perception And Proposing An Organized Questionnaire For The Deployment And Adoption Of Autonomous Vehicles, Md Rakibul Islam
Assessing Public Perception And Proposing An Organized Questionnaire For The Deployment And Adoption Of Autonomous Vehicles, Md Rakibul Islam
Electronic Theses and Dissertations, 2020-
Since the general public will play a central role in the evolution of AVs, research has been performed to assess their perception and acceptance of AVs. Nevertheless, the most potential users of AVs, i.e., young, students, and more educated people, have not received any particular focus in those studies. This research gap has motivated us to assess their perceptions. Extensive data analyses of the survey at the University of Central Florida with a sample of 315 reveal that on average 57% of the respondents were familiar with AVs, and about 44% of the respondents felt positive perceptions toward AVs. Around …
Generative Modeling Of Human Behavior: Social Interaction And Networked Coordination In Shared Facilities, Saumya Gupta
Generative Modeling Of Human Behavior: Social Interaction And Networked Coordination In Shared Facilities, Saumya Gupta
Electronic Theses and Dissertations, 2020-
Urbanization is bringing together various modes of transport, and with that, there are challenges to maintaining the safety of all road users, especially vulnerable road users (VRUs). Therefore, there is a need for street designs that encourages cooperation and resource sharing among road users. Shared space is a street design approach that softens the demarcation of vehicles and pedestrian traffic by reducing traffic rules, traffic signals, road markings, and regulations. Understanding the interactions and trajectory formations of various VRUs will facilitate the design of safer shared spaces. It will also lead to many applications, such as implementing reliable ad hoc …
Testing The Influence Of Water Depth In Design Of Created Oyster Reef For Living Shoreline Applications, Peter Vien
Testing The Influence Of Water Depth In Design Of Created Oyster Reef For Living Shoreline Applications, Peter Vien
Electronic Theses and Dissertations, 2020-
Living shoreline stabilization has become a popular practice in shoreline restoration and bank protection; however, there are still many uncertainties regarding effective site design using living materials. For example, natural wave-breaks may be formed of created reefs, but the optimum water depth for hydrodynamic influence may differ from the preferred depth to ensure organism recruitment. The objective of this research is to understand how water depth relative to the crest of submerged artificial oyster reef structures influences nearshore hydrodynamic processes and sediment transport or retention in nearshore areas. A field study, sited in a microtidal estuary on the Atlantic coast …
Detecting And Tracking Vulnerable Road Users' Trajectories Using Different Types Of Sensors Fusion, Zhongchuan Wang
Detecting And Tracking Vulnerable Road Users' Trajectories Using Different Types Of Sensors Fusion, Zhongchuan Wang
Electronic Theses and Dissertations, 2020-
Vulnerable road user (VRU) detection and tracking has been a key challenge in transportation research. Different types of sensors such as the camera, LiDAR, and inertial measurement units (IMUs) have been used for this purpose. For detection and tracking with the camera, it is necessary to perform calibration to obtain correct GPS trajectories. This method is often tedious and necessitates accurate ground truth data. Moreover, if the camera performs any pan-tilt-zoom function, it is usually necessary to recalibrate the camera. In this thesis, we propose camera calibration using an auxiliary sensor: ultra-wideband (UWB). USBs are capable of tracking a road …
A Deep Learning Approach For Spatiotemporal-Data-Driven Traffic State Estimation, Amr Hatem Ragaa Abdelraouf
A Deep Learning Approach For Spatiotemporal-Data-Driven Traffic State Estimation, Amr Hatem Ragaa Abdelraouf
Electronic Theses and Dissertations, 2020-
The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed …
An Econometric Analysis Of Domestic Aviation In The Us, Sudipta Dey Tirtha
An Econometric Analysis Of Domestic Aviation In The Us, Sudipta Dey Tirtha
Electronic Theses and Dissertations, 2020-
In this dissertation, we examine two dimensions of domestic aviation - demand and delay - that directly influence economic impact of the sector. We conduct a comprehensive analysis of airline demand employing airline data compiled by Bureau of Transportation Statistics. The demand analysis is conducted in three steps. First, we propose a novel modeling approach for modeling airline demand evolution over time. Specifically, we develop a joint panel group generalized ordered probit (GGOP) model system for modeling air passenger arrivals and departures in a discretized framework that subsumes the traditional linear regression approach. Further, we consider the influence of observed …
Automated Vehicle To Vehicle Conflict Analysis At Signalized Intersections By Camera And Lidar Sensor Fusion, Alabi Mehzabin Anisha
Automated Vehicle To Vehicle Conflict Analysis At Signalized Intersections By Camera And Lidar Sensor Fusion, Alabi Mehzabin Anisha
Electronic Theses and Dissertations, 2020-
This research presents an approach for safety diagnosis using sensor fusion techniques. This work fuses the outputs of a roadside low-resolution camera and a solid-state LiDAR. For vehicle classification and detection in videos, the YOLO v5 object detection model was utilized. The raw 3D point clouds generated by the LiDAR are processed by two manual steps - ground plane transformation and background segmentation, and two real-time steps - foreground clustering, and bounding box fitting. Taking the generated 2D bounding boxes of both camera and LiDAR, we associate the common bounding box pairs by thresholding on the Euclidean distance threshold of …
Distracted Driving And Pedestrians' Effects On Headway At Signalized Intersections, Bassel Elgamal
Distracted Driving And Pedestrians' Effects On Headway At Signalized Intersections, Bassel Elgamal
Electronic Theses and Dissertations, 2020-
Distracted driving and pedestrians pose one of the most difficult challenges to ensuring a safe and efficient transportation system. Modern communications have delivered greater convenience. However, this has come at the cost of attention spans. Safety has been thoroughly explored in terms of distracted driving and pedestrians. However, impacts on traffic operations have received minimal research attention. Few studies provided a theoretical mechanism on how intersection operations can be affected but failed to quantify the real-life impacts on traffic operations. Furthermore, new Florida laws prohibit cellphone usage while driving but is allowed when the vehicle is stationary, which may result …
Development Of Active Learning Data Fixing Tool With Visual Analytics To Enhance Traffic Near-Miss Diagnosis, Jinyu Pei
Electronic Theses and Dissertations, 2020-
This study proposes a software to upgrade the UCF SST's Automated Roadway Conflicts Identification System (ARCIS), a pixel-to-pixel manner automated safety diagnostics and conflict identification system. The system is developed to extract vehicles' trajectories and traffic parameters using unmanned aerial vehicles (UAV) video and utilizing deep learning techniques. A user-friendly tool to improve rapid system development with active-learning, data analysis, and visualization techniques is introduced, which is capable of traffic safety near-miss diagnostics based on the ARCIS output. Multiple approaches are used to enhance the system performance, including video stabilization, object filtering, stitching multiple videos, vehicle detection and tracing. In …
A Spatiotemporal Evaluation Of Freeway Traffic Demand In Florida During Covid-19 Pandemic, Md Istiak Jahan
A Spatiotemporal Evaluation Of Freeway Traffic Demand In Florida During Covid-19 Pandemic, Md Istiak Jahan
Electronic Theses and Dissertations, 2020-
This thesis contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study- a) Linear regression model, b) Spatial Autoregressive Model (SAR) and c) Spatial Error Model (SEM). For the model estimation, traffic volume data for the year 2019 and 2020 from 3,957 detectors were augmented with independent variables, such as- COVID-19 case information, socioeconomics, land-use and built environment characteristics, roadway characteristics, meteorological information, and spatial locations. Traffic volume data was analyzed separately for weekdays and holidays. SEM models …
Modeling, Reconstruction, And Trend Analysis Of Global Storm Surges Using A Data-Driven Approach, Michael Getachew Tadesse
Modeling, Reconstruction, And Trend Analysis Of Global Storm Surges Using A Data-Driven Approach, Michael Getachew Tadesse
Electronic Theses and Dissertations, 2020-
Storm surge is the deadliest component of extreme sea levels with one of the highest global death tolls per event. Tide gauges are the primary sources for historical sea-level measurements from which storm surge data is extracted. However, tide gauges are unevenly distributed across the globe, and most records are short in length and have gaps; this creates a challenge to assess long-term trends and perform robust extreme value analysis. This dissertation introduces a data-driven storm surge modeling framework that trains statistical and machine learning models with atmospheric and oceanographic variables. Data-driven models (DDMs) are trained and validated for more …
High Fidelity Injury Severity Analysis Using Econometric Modeling Approaches, Ahmed Kabli
High Fidelity Injury Severity Analysis Using Econometric Modeling Approaches, Ahmed Kabli
Electronic Theses and Dissertations, 2020-
Crash severity models are typically developed using police reported injury severity databases. However, several research studies have identified various challenges associated with police reported data. Therefore, the current dissertation is focusing on developing high resolution crash severity models based on medical professional driver injury severity reported using Abbreviated Injury Scale for eight body regions. The dissertation focused on developing a disaggregate injury severity modeling framework that can enhance the estimation accuracy of independent variable impacts on severity. Within this broad research vision, the dissertation has multiple objectives. First, a joint random parameters multivariate model structure with as many dimensions as …
Spatiotemporal Analysis Of Taxi And Transportation Network Companies (Tnc) Demand In The Wake Of Covid-19, Dewan Ashraful Parvez
Spatiotemporal Analysis Of Taxi And Transportation Network Companies (Tnc) Demand In The Wake Of Covid-19, Dewan Ashraful Parvez
Electronic Theses and Dissertations, 2020-
The objective of the thesis is to understand the factors affecting spatiotemporal ridehailing demand patterns as the COVID-19 pandemic has evolved. Specifically, the current study examines the key contributing factors of weekly ridehailing demand by employing Taxi and Transportation Network Companies (TNC) trip data from January 2019 through December 2020 for New York City. The ridehailing demand is partitioned across four time periods including Morning Peak, Morning Off Peak, Evening Peak and Evening Off Peak to accommodate for the time-of-day specific variations. Drawing on the high-resolution NYC data, the current study developed pooled spatial panel models to accommodate for the …
Using Machine Learning Technique To Develop A Deterioration Predicting Model For Pavement Marking In Florida, Ehab Abdelmaksoud
Using Machine Learning Technique To Develop A Deterioration Predicting Model For Pavement Marking In Florida, Ehab Abdelmaksoud
Electronic Theses and Dissertations, 2020-
Longitudinal pavement markings play a significant role on the roadways by delivering information to motorists to help them navigate and follow the road. These markings are also considered to be a crucial control device that can enhance ideal nighttime visibility, especially on rural roads where the surrounding luminance is insufficient. Hence, the main question for public agencies or officials is about when the replacement of the pavement markings needs to take place. The Federal Highway Administration (FHWA) is considering proposing a minimum level of retroreflectivity standard and based on that, the Manual on Uniform Traffic Control Devices (MUTCD) set aside …
Machine Learning Algorithms For Forecasting The Impacts Of Connected And Automated Vehicles On Highway Construction Costs, Amirsaman Mahdavian
Machine Learning Algorithms For Forecasting The Impacts Of Connected And Automated Vehicles On Highway Construction Costs, Amirsaman Mahdavian
Electronic Theses and Dissertations, 2020-
A multitude of externalities affects transport efficiency and numbers of trips. Population expansion, urban development, political issues, fiscal trends, and growth in the field of connected, automated, shared, and electric (CASE) vehicles have all played prominent roles. While the market is keenly aware of the upcoming shift to the CASE vehicles, the transformation itself is reliant upon the development of technologies, customer outlook, and guidelines. The purpose of this research is to establish an overview of the possible network design problems, as well as potential consequences to vehicle automation systems by employing machine learning and system dynamics analysis. Finally, the …
Enhancing The Decision-Making Process For Public-Private Partnerships' Concession Agreements: Socio-Economic Sustainability Approaches, Faisal Alghamdi
Enhancing The Decision-Making Process For Public-Private Partnerships' Concession Agreements: Socio-Economic Sustainability Approaches, Faisal Alghamdi
Electronic Theses and Dissertations, 2020-
Designing a concession for Public-Private Partnerships (PPP) agreements is a complicated process due to the number of variables that need to be considered. The PPP concession has many parameters and components, and a change in one component will considerably impact other components. Hence, the determination of the fair values of the concession components needs to be based on mutual benefits between the concession participants. The concession participants have different parts in the development of the concession; hence, they have different perspectives and goals. Therefore, the concession design needs to be constructed to balance the interests of the involved parties to …
Analytical Study Of Deep Learning Methods For Road Condition Assessment, Elham Eslami
Analytical Study Of Deep Learning Methods For Road Condition Assessment, Elham Eslami
Electronic Theses and Dissertations, 2020-
Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task, however, remains challenging due to the high variations in road objects and pavement types, variety of lighting condition, low contrast, and background noises in pavement images. In this dissertation, we propose novel deep learning algorithms for image-based road condition assessment to tackle current challenges in detection, classification and segmentation of pavement images. Motivated by the need for classifying a wide range of objects in road monitoring, this …
Analysis Of Shear Failure In Ultra-High Performance Concrete Beams, Kevin Conway
Analysis Of Shear Failure In Ultra-High Performance Concrete Beams, Kevin Conway
Electronic Theses and Dissertations, 2020-
Ultra-high performance concrete (UHPC) is an emerging cementitious material type currently undergoing research in different structural applications. This material is characterized by a compressive strength of 150 MPa (22 ksi), according to the Association Francaise de Genie Civil and the Federal Highway Administration, high tension strength, and high durability. Additionally, UHPC consists of secondary reinforcement in the form of steel or polymeric fibers, which enable UHPC to exhibit tension hardening behavior and tension ductility previously ignored in conventional concrete mixes. Previous research has focused on applications in the transportation and bridge design industries; however, applications in buildings and other heavy …
Numerical Study And Optimization Of Post-Tensioning Energy Dissipating Connections With Inerters, Hector Blanco Gavillan
Numerical Study And Optimization Of Post-Tensioning Energy Dissipating Connections With Inerters, Hector Blanco Gavillan
Electronic Theses and Dissertations, 2020-
Posttensioned connections for steel moment resisting frames (MRF) can reduce residual deformations through self-centering capabilities. Devices are added to improve the connections energy dissipating capability and includes energy dissipating (ED) bars, friction dampers, and steel angles located at the top and bottom of the beam-to-column connections. The Post-Tensioned Energy dissipating (PTED) connection reduces inelastic deformation to the beams and columns, is installed with minimum welding, displays self-centering thus reducing residual deformations, and allows for easy replacement of the ED devices. An inerter is a two-terminal mechanical device that generates a force proportional to the relative acceleration between its nodes. The …
Effect Of Various Speed Management Strategies On Bicycle Crashes For Urban Roads In Central Florida, Jorge Ugan
Effect Of Various Speed Management Strategies On Bicycle Crashes For Urban Roads In Central Florida, Jorge Ugan
Electronic Theses and Dissertations, 2020-
In recent years, cycling has become an increasingly popular transportation mode around the world. In contrast to other popular modes of transportation, cycling is more economic and energy efficient. While many studies have been conducted for the bicycle safety analysis, most of them were limited in terms of bicycle exposure data and on-street data. This study tries to improve the current safety performance functions for bicycle crashes at urban corridors by utilizing crowdsource data from STRAVA and on-street speed management strategies data. Speed management strategies are any roadway alterations that causes a change in motorists' driving behavior. In Florida, these …
Data Driven Methods For Large Scale Network Level Traffic Modeling, Rezaur Rahman
Data Driven Methods For Large Scale Network Level Traffic Modeling, Rezaur Rahman
Electronic Theses and Dissertations, 2020-
Rapid growth in population along with urban-centric activities impose a massive demand on existing transportation systems, thus increasing traffic congestion and other mobility related challenges. To overcome such challenges, we need network-scale models to accurately predict real-time traffic demand and associated congestion. However, traditional network modeling approaches have shortcomings due to the complexity in traffic flow modeling, limited scope to incorporate real-time data available from emerging data sources and requiring excessive computation time to generate accurate estimation of traffic flows. Advancement in traffic sensing technologies with big data has created a new opportunity to overcome these challenges and implement deployable …