T
Taehooie Kim
Researcher at Arizona State University
Publications - 7
Citations - 90
Taehooie Kim is an academic researcher from Arizona State University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 5 publications receiving 37 citations. Previous affiliations of Taehooie Kim include Washington State University.
Papers
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Journal ArticleDOI
Fusing Multiple Sources of Data to Understand Ride-Hailing Use:
Felipe F. Dias,Patrícia S. Lavieri,Patrícia S. Lavieri,Taehooie Kim,Chandra R. Bhat,Chandra R. Bhat,Ram M. Pendyala +6 more
TL;DR: A data fusion process is employed to gain deeper insights about the characteristics of ride-hailing trips and their users to better understand the use of these services.
Journal ArticleDOI
A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service
TL;DR: The experiment result indicates that the integrated model can capture the inter-relationships between existing taxis and ride-hailing services as well as identify the influence of additional factors, namely, the day of the week, weather, and holidays.
Journal ArticleDOI
Computational graph-based framework for integrating econometric models and machine learning algorithms in emerging data-driven analytical environments
TL;DR: In an era of big data and emergence of disrupting mobility technologies, statistical models have been utilized to uncover the influence of significant factors, and machine learning algorithms have been used to predict significant factors as discussed by the authors.
Journal ArticleDOI
Modeling the Evolution of Ride-Hailing Adoption and Usage: A Case Study of the Puget Sound Region
Felipe F. Dias,Taehooie Kim,Chandra R. Bhat,Chandra R. Bhat,Ram M. Pendyala,William H. K. Lam,Abdul Rawoof Pinjari,Karthik K. Srinivasan,Gitakrishnan Ramadurai +8 more
TL;DR: In this article, the authors provide a basis to understand and quantify changes in ride-hailing services in cities around the world, using publicly available data sources that can be used for understanding and quantifying changes.
Proceedings ArticleDOI
Mitigation of self-organized traffic jams using cooperative adaptive cruise control
Taehooie Kim,Kshitij Jerath +1 more
TL;DR: The included results indicate that traffic state-dependent CACC algorithms can improve traffic flow, and also hold significant potential to reduce susceptibility to the presence of human-driven vehicles.