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 from the new data sources contain more specific, detailed, and longitudinal information on individual travel modes 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.
Major: Civil Engineering
Educational Career:
Bachelor's of Urban Rail Transit, BE, 2015, Beijing Jiaotong University
Master's of Transportation Operation and Management, MS, 2018, Beijing Jiaotong University
Master's of smart cities, MS, 2020, University of Central Florida
Committee in Charge:
Samiul Hasan, Chair, Civil Engineering
Naveen Eluru, Professor
Yanjie Fu, Assistant Professor
Mohamed Zaki Hussein, Assistant Professor
Approved for distribution by Samiul Hasan, Committee Chair, on April 6, 2023.
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