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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 …
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 …
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 …
Understanding Changes In Land Use Patterns At A Parcel Level In Central Florida Counties (Orange, Seminole, Osceola), Youssef Mohamed Abdelaziz Elsebaie
Understanding Changes In Land Use Patterns At A Parcel Level In Central Florida Counties (Orange, Seminole, Osceola), Youssef Mohamed Abdelaziz Elsebaie
Electronic Theses and Dissertations, 2020-
In Florida, a state with significant population growth, it is essential to understand how land use change and transportation interact. Understanding these interactions between land use and transport is useful for transportation planners and land use modelers. Hence, in this research, we attempt to quantify the impact of the transportation system on land use and vice-versa. The research, using high-resolution land use data from 2011 to 2019, builds a binary logistic regression model of land use change. The model accounts for various independent variables, including socio-demographic attributes, built environment characteristics, and transportation network variables. The data set covers GIS data …