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 mobile phone and social media data, mobility data extracted of the new data sources contain more specific, detailed, and longitudinal information of individual travel mode and coordinates of the visited locations. This dissertation explores the potential of using GPS-based FCD and on-demand ride-hailing service data with different modeling techniques towards understanding and predicting individual mobility and activity behaviors and assessing the ridesharing impacts through three studies.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Civil, Environmental, and Construction Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Zhang, Jiechao, "Modeling Individual Activity and Mobility Behavior and Assessing Ridesharing Impacts Using Emerging Data Sources" (2023). Electronic Theses and Dissertations, 2020-. 1703.